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"\n",
"# Preliminary analysis of an asymmetric resonant ecliptic capture orbit sling transport system\n",
"Bryan Killett, 2018\n",
"***\n",
"\n",
" Puig-Suari et al. 1995 and Jokic and Longuski 2004 designed tether slings on Phobos and Deimos for transporting payloads to Earth. However, their slings' tether masses scale quickly with the required payload hyperbolic excess speed, partly because the small Oberth effect at Phobos and Deimos necessitates high sling tip speeds. If a hyperbolic excess speed higher than ~5 km/s is required, a lower total system mass can be obtained by using multiple slings. A 'moon sling' on Phobos or Deimos throws payloads and counterbalance masses into a capture orbit with a periapsis just above the atmosphere of Mars. The capture orbit is resonant with the chosen moon to allow for repeated throws, and it's ecliptic because trans-Earth injection velocities are close to lying in the ecliptic plane. A coupled pair of asymmetric 'capture slings' in the capture orbit rendezvous with those payloads and counterbalances, then use solar power to spin up both slings by torquing against each other. Both payloads and both counterbalances are then thrown at periapsis to maximize the Oberth effect. Asymmetric capture slings allow the counterbalance orbits to be specified approximately independently of the payload velocities, but asymmetric slings force a certain ratio between the payload masses and require ballast mass to keep the slings' centers of mass at the desired rotational axis. The capture slings can use tether reeling to avoid collisions with the moons and to rotate their periapsis velocity vector to throw payloads in different directions in the ecliptic plane.\n",
"\n",
"## Steps:\n",
"1. Specify the maximum payload tip speed and acceleration.\n",
"1. Choose the payloads' hyperbolic excess speeds, tether material, safety factor, etc.\n",
"1. Choose an orbital resonance for the capture slings.\n",
"1. Estimate the required tip speed to compare with the tip speed specified in step 1.\n",
"1. Initialize lists and define functions.\n",
"1. Solve for each capture sling's radius, rotation rate, counterbalance ratio, etc.\n",
"1. Print the capture slings' payload trajectories and counterbalance orbits.\n",
"1. Calculate the delta-v from the chosen moon's orbit to the inclined capture sling orbit.\n",
"1. Print the moon sling's payload and counterbalance orbits.\n",
"1. Solve for the ballast mass needed to keep an asymmetric sling's center of mass at the rotational axis.\n",
"1. Define the slings' moments of inertia and rotational kinetic energy.\n",
"1. Solve for the third capture sling which zeroes the total rotational angular momentum of the capture sling system whether it's full or empty.\n",
"1. Solve for the moon sling payload based on mt4ct and the total mass to be thrown.\n",
"1. Check the centers of mass of all slings while empty and full.\n",
"1. Calculate required power and solar panel mass for spinning the slings.\n",
"1. Animate the capture sling throw.\n",
"1. Make a 3D plot of the payloads and counterbalances to assess the risk of collisions.\n",
"1. Calculate mass of equivalent rocket.\n",
"1. Print summary.\n",
"1. Appendix A: Counterbalance mass ratios above 4.6033 cause collisions.\n",
"1. Appendix B: Indirect paths from the moon to the capture orbit.\n",
"\n",
" This notebook was initially written to reproduce values in Jokic and Longuski 2004 table 3. To do that, set grapple_fraction = 0, moon_name = 'Phobos', safety = 1.0, material = 'Zylon', tip_accel_max = 3\\*g, payload_mass = 11.2\\*1000 or 70\\*1000, cbrs[0] >= 1, cbrs[1] = 0, best_case_offset = 0.0, Isp = 379, s2p = 0.15. The moon sling payload tether mass, length, diameters, single-stage rocket propellant mass and mu_single reported in the summary at the bottom will then ~reproduce values in Jokic and Longuski 2004 table 3.\n",
"\n",
"### Look through phone for other things.\n",
"### Radiator sun shield doesn't show up!\n",
"### Add longitudinal supports to the hawsepipe. Add a light right outside the hub, for clarity.\n",
"### NOTE THAT THE CAPTURE TO MOON ROTATION MATRIX DOESN'T REALLY MAKE SENSE ANYMORE AFTER THE CELL CHANGED FROM MOON FRAME TO CAPTURE_FRAME!\n",
"### IN SEVERAL PLACES THE PERIAPSIS VS INFINITY HYPERBOLIC DEFLECTION ANGLE IS CALCULATED. MAKE SURE THAT IN ALL SUCH PLACES, THE CALCULATIONS ARE EITHER FOR THE MOON SLING *OR* THEY'RE FOR THE CAPTURE SLING BUT *BOTH* V_INFS ARE USED!!!\n",
"### DAMNIT! THE RETROGRADE ANCHOR NEEDS TO BE REALLY DIFFERENT MASSES BEFORE AND AFTER THE THROW!\n",
"### Changed all RR( to RDF( for speed https://ask.sagemath.org/question/9950/what-are-the-different-real-numbers-in-sage/ AT THE SAME TIME, CHANGE capture_frame_string to ecliptic_frame_string, inc_moon to inc_m2c (OR SOMETHING?!?!), H_cs_unit to H_cs_n to match others, etc??\n",
"### Because tether reeling becomes less effective with more circular orbits, might consider making the destination counterbalance orbit elliptical. However, that would complicate gravity gradient stabilization of habitats and getting the counterbalances to rendezvous with those habitats.\n",
"### Finish writing string for payload longitude (and rename it!), specifically start and finish the part dealing with the capture sling longitudes which don't require any tether reeling in between picking up payloads from one of the moon sling's nodes (either going out or going in).\n",
"### Eclipses aren't considered in calculations of power requirements. LINK here FOR MOON ECLIPSES, FOR CAPTURE SLING ECLIPSES, LINK TO THESIS AND/OR THAT RECENT TETHER ANALYSIS I DOWNLOADED (CAN'T FIND IT ON TABLET, MIGHT HAVE TO LOOK IN TETHER EMAIL AND/OR THAT KHAYMAN FOLDER WITH ALL THE PAPERS THAT NEED TO BE NAMED)?\n",
"### capture_frame_string should probably be called ecliptic_frame_string, and it shouldn't mention the capture slings if cbrs[1]==0.\n",
"### Consider adding equatorial capture sling option. This means inc_moon needs to become inc_m2c (inclination of moon wrt capture orbit). Also have inc_q2l (equator wrt ecliptic BUT THIS IS ALREADY IN CODE AS axial_tilt_planet!) and inc_c2q or inc_c2l???\n",
"### Finish J2 precession calcs.\n",
"### Still not dealing with nodal precession due to the Sun, which precesses the orbit around the normal to Mars's orbital plane. \". For each satellite the net precession is retrograde about the normal to its Laplace plane, a plane lying between the Mars equator and Mars orbit;\" http://www.planetary.brown.edu/planetary/geo287/PhobosDeimos/papers/Jacobson%20and%20Lainey_Martian%20satellite%20orbits%20and%20ephemerides-2014.pdf\n",
"### GREAT website overall, and this function should replace GAIA's distance function! http://web.archive.org/web/20110825045635/https://www.projectpluto.com/dist.cpp\n",
"\n",
"\n",
"## Limitations:\n",
" - Simple rigid tether model- no elasticity, oscillations, tumbling, chaotic motion, etc.\n",
" - There's no coupling between orbital and rotational angular momenta. The capture slings rotate as though they're in free space and their center of mass isn't accelerating. Completely separately, the capture sling center of mass moves in a Keplerian orbit as if it were a point mass.\n",
" - Only centrifugal forces are included, so forces due to gravitational gradients aren't considered.\n",
" - Gravitational forces due to Deimos, Phobos, and the oblateness of Mars (and all other gravitational perturbations) are ignored.\n",
" - Deimos and Phobos are treated as being in circular orbits that are inclined relative to the ecliptic plane.\n",
" - Hyperbolic payload trajectories lie in the ecliptic plane, so plane-change corrections are needed because planetary orbits aren't all in the ecliptic.\n",
" - The specified maximum acceleration and tip speed only apply to the payload, so if a sling uses a counterbalance ratio < 1.0, its counterbalance acceleration and tip speed will be above the specified maximums. In that case, the counterbalance arm could also reach below the target altitude of the counterbalances, which is a safety hazard.\n",
" - This notebook was written mostly from scratch. While this was fun and educational, the code has only been tested by one person. That's a serious limitation compared to code from Project Pluto which has been tested by many people. In particular, functions like xyz2kepler() and kepler2xyz() should probably be replaced with code from Project Pluto. On a related note, this notebook is sort of designed to allow the origin planet to be changed (e.g. so the capture slings throw payloads from Jupiter rather than Mars), but that hasn't really been tested. If you want to throw payloads from a planet that isn't Mars then beware of software bugs. You can manually run each cell by hitting Shift-Enter, but it's safer to run the entire notebook at once by clicking on Kernel -> Restart & Run All . (Unintended output can sometimes occur when running cells repeatedly or out of order.)\n",
" - Haven't estimated the masses of the radiators, electric motors and the central hub that transmits torque from one capture sling to the other. The summary prints values for these masses as reminders to produce rigorous estimates, but right now those values are either all zero (if estimate_misc is set to 0) or they're just copies of the solar panel mass estimate (based on Juno's solar panels).\n",
" - Launch windows are ignored, as is the longitude of the payload's trajectory in the ecliptic plane. If the capture slings' periapsis velocity vector isn't pointing in the right direction, they can compensate in two ways. First, they can release the payloads and counterbalances before or after periapsis, although this will require higher tip speeds and make it harder to get the counterbalances into the same desired low orbit. This hasn't been addressed. Secondly, they can use tether reeling to rotate the capture slings' argument of periapsis until it's pointing in the right direction. The time necessary to rotate the capture orbit like this isn't addressed, nor are any of the technical requirements like electrical power or the required speed that the masses need to be reeled so the capture orbit rotates at the required rate, etc.\n",
" - Even if the two payloads have the same hyperbolic excess speeds, they're released at different heights so they're deflected by different hyperbolic deflection angles. As a result, they converge on two different asymptotes. This can probably be dealt with by throwing one of the payloads at a slightly different angle and a slightly different time than the other payload. Sorensen 2003 might help. Since both payloads are currently thrown at the optimal time and angle (i.e. at periapsis with their rotational velocity vectors parallel with the capture slings' orbital velocity vector), this will increase the tether masses. Also, it might not be enough to converge on the same asymptote; rendezvous with a cycler spacecraft will require that both payloads rendezvous with each other and the cycler in finite time, ideally with low relative velocities. None of this has been addressed, partly because it seems like these next problems need to be addressed simultaneously:\n",
" - This notebook allows each payload to be thrown at its own hyperbolic excess speed, but doesn't address the fact that this probably means each payload needs to be thrown at a different hyperbolic asymptote. Consider quantifying the added tether mass of changing the angle between the two payloads' asymptotes (the optimal angle with the lowest total tether mass was explained above). One could vary one or both of the slings' throw times away from the time of periapsis. But instead of trying to match the asymptote of the first sling, just choose a throw angle so the rotational velocity vector is parallel with the capture slings' orbital velocity vector. Then calculate the sling radius and rotation rate necessary to acheive the same hyperbolic excess speed- this will increase because the sling's orbital velocity isn't as fast as it is at periapsis. This increase in the tip speed increases the tether mass. The new payload's trajectory will have a different the argument of periapsis and hyperbolic deflection angle, both of which are needed to calculate the hyperbolic asymptote. Calculate the angle between this new asymptote and that of the first sling, then vary the sling's throw time again and repeat the procedure. After varying the throw time over a \"large enough\" timespan, one could make a plot of the required total tether mass versus throw time, and a plot of the corresponding angle between the payload asymptotes versus throw time. This could help quantify the added tether mass of changing the angle between the two payloads' asymptotes. It would help to load an ephemeris and perform a rigorous search of possible ~simultaneous throws (to different planets?) in order to figure out how long a \"large enough\" timespan is, in order to separate the two asymptotes by an angle that corresponds to an actual ~simultaneous launch window, given the actual positions of the origin planet and destination planet(s). How \"simultaneous\" do the throws have to be? For instance, could one payload be thrown multiple capture orbits before the other payload is thrown? That would allow for more mission flexibility but would expose the tethers to space for a longer time and increase the risk of chaotic tether motion.\n",
" - The capture slings are treated as though they're both rotating in the ecliptic plane, which isn't possible. In fact, safe operation will depend on the separation between the centers of mass for capture slings A and B being larger than the maximum size (in the z direction, along the slings' axis of rotation) of oscillations and/or chaotic motion. So it's necessary to account for this separation distance. A rigorous treatment during the spinup procedure is beyond the scope of this preliminary analysis. Perhaps a reasonable compromise at this stage would be to solve for the throw times (plus throw angles, radii, omegas and counterbalance ratios) while assuming they're coplanar but then estimate the targeting error by moving those payload/balance positions and velocities a 'safe' operating distance in the z-direction while keeping velocity unchanged, then recomputing the Keplerian elements of those positions/velocities using xyz2kepler(). Then estimate the delta-v needed for the two payloads/balances to rendezvous. This might be a good use for maneuvering thrusters, and a reason why the capture orbit sling system could benefit from using at least some propellant (e.g. CO2 from the Martian atmosphere) and/or fuel (e.g. LOX/CH4 made from hydrogen and the Martian atmosphere)."
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"\n",
"## 1. Specify the maximum payload tip speed and acceleration. (Go back to the top)."
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"Mission 1: v_inf = 0.00000 m/s - Parabola\n",
"Mission 2: v_inf = 2640.0 m/s - M-E Hohmann\n",
"Mission 3: v_inf = 3000.0 m/s - M-E in < 6 months\n",
"Mission 4: v_inf = 4000.0 m/s - M-E-M\n",
"Mission 5: v_inf = 5470.0 m/s - MEEEM cycler\n",
"Mission 6: v_inf = 8000.0 m/s - 8 km/s cycler\n",
"Mission 7: v_inf = 11900. m/s - Aldrin cycler\n"
]
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"source": [
"import copy #Copy lists using deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
"from IPython.display import Image\n",
"var('mu r a v ecc cos_ta v_tip') # mu = G*M, a = semi major axis, cos_ta = cosine of true anomaly\n",
"set_verbose(2) #Set to 0 to disable print statements in xyz2kepler() and kepler2xyz().\n",
"textonly = 0 #Set to anything but \"0\" to limit output to text only.\n",
"g = 9.81 #m/s^2 Earth gravity\n",
"\n",
"#Try 1105.0 for the M-E Hohmann mission from Deimos using the 11:5 resonance.\n",
"#Try 3465.0 for the MEEM cycler mission from Deimos using the 11:6 resonance.\n",
"v_tip_max = 1105.0 #Highest sling payload tip speed in m/s under consideration. Determines longest sling radius at a given tip accel. For best results, run this notebook with a guess at v_tip_max, then change v_tip_max so it matches the highest calculated sling tip speed. Then iterate this process a few times, because the calculated sling tip speed will change after v_tip_max is changed.\n",
"tip_accel_max = 1*g #Along with v_tip_max, this controls r_max:\n",
"r_max = v_tip_max^2/tip_accel_max\n",
"\n",
"#To simulate Hohmann transfers to/from other planets, try this Hohmann calculator by Hop David: http://web.archive.org/web/20170202021347/http://clowder.net/hop/railroad/Hohmann.xls\n",
"v_infs = [] ; mnames = []\n",
"v_infs += [ 0.0]; mnames += ['Parabola']\n",
"v_infs += [2640.0]; mnames += ['M-E Hohmann']\n",
"v_infs += [3000.0]; mnames += ['M-E in < 6 months']\n",
"v_infs += [4000.0]; mnames += ['M-E-M']\n",
"v_infs += [5470.0]; mnames += ['MEEEM cycler']\n",
"v_infs += [8000.0]; mnames += ['8 km/s cycler']\n",
"v_infs += [11900.0]; mnames += ['Aldrin cycler'] #Unless sending counterbalance to surface or retrograde, this mission needs a counterbalance mass ratio > 4.6, which would cause a collision.\n",
"for j in range(len(v_infs)): print 'Mission',str(j+1).rjust(2)+': v_inf =',str(v_infs[j].n(digits=5)).rjust(7),'m/s -',mnames[j]"
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"\n",
"## 2. Choose the payloads' hyperbolic excess speeds, tether material, safety factor, etc. (Go back to the top)."
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"Resonance s:p is defined as: s*period_capture = p*period_moon.\n",
"Choice 1 = 1: 1 resn w Deimos, period= 1.2629, revisit time= 1.2629 days, periapsis= 3920.5 , apoapsis= 43006. km, ecc= 0.83291\n",
"Choice 2 = 2: 1 resn w Deimos, period= 0.63147, revisit time= 1.2629 days, periapsis= 3920.5 , apoapsis= 25641. km, ecc= 0.73476\n",
"Choice 3 = 3: 2 resn w Deimos, period= 0.84196, revisit time= 2.5259 days, periapsis= 3920.5 , apoapsis= 31891. km, ecc= 0.78105\n",
"Choice 4 = 4: 3 resn w Deimos, period= 0.94720, revisit time= 3.7888 days, periapsis= 3920.5 , apoapsis= 34816. km, ecc= 0.79758\n",
"Choice 5 = 5: 3 resn w Deimos, period= 0.75776, revisit time= 3.7888 days, periapsis= 3920.5 , apoapsis= 29462. km, ecc= 0.76512\n",
"Choice 6 = 5: 4 resn w Deimos, period= 1.0103, revisit time= 5.0517 days, periapsis= 3920.5 , apoapsis= 36520. km, ecc= 0.80611\n",
"Choice 7 = 6: 5 resn w Deimos, period= 1.0524, revisit time= 6.3147 days, periapsis= 3920.5 , apoapsis= 37635. km, ecc= 0.81131\n",
"Choice 8 = 7: 4 resn w Deimos, period= 0.72168, revisit time= 5.0517 days, periapsis= 3920.5 , apoapsis= 28394. km, ecc= 0.75735\n",
"Choice 9 = 7: 5 resn w Deimos, period= 0.90210, revisit time= 6.3147 days, periapsis= 3920.5 , apoapsis= 33577. km, ecc= 0.79089\n",
"Choice 10 = 7: 6 resn w Deimos, period= 1.0825, revisit time= 7.5776 days, periapsis= 3920.5 , apoapsis= 38423. km, ecc= 0.81483\n",
"Choice 11 = 8: 5 resn w Deimos, period= 0.78933, revisit time= 6.3147 days, periapsis= 3920.5 , apoapsis= 30383. km, ecc= 0.77142\n",
"Choice 12 = 8: 7 resn w Deimos, period= 1.1051, revisit time= 8.8405 days, periapsis= 3920.5 , apoapsis= 39009. km, ecc= 0.81735\n",
"Choice 13 = 9: 5 resn w Deimos, period= 0.70163, revisit time= 6.3147 days, periapsis= 3920.5 , apoapsis= 27792. km, ecc= 0.75275\n",
"Choice 14 = 9: 7 resn w Deimos, period= 0.98228, revisit time= 8.8405 days, periapsis= 3920.5 , apoapsis= 35767. km, ecc= 0.80243\n",
"Choice 15 = 9: 8 resn w Deimos, period= 1.1226, revisit time= 10.103 days, periapsis= 3920.5 , apoapsis= 39462. km, ecc= 0.81926\n",
"Choice 16 = 10: 7 resn w Deimos, period= 0.88406, revisit time= 8.8405 days, periapsis= 3920.5 , apoapsis= 33075. km, ecc= 0.78806\n",
"Choice 17 = 10: 9 resn w Deimos, period= 1.1366, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 39823. km, ecc= 0.82075\n",
"Choice 18 = 11: 5 resn w Deimos, period= 0.57406, revisit time= 6.3147 days, periapsis= 3920.5 , apoapsis= 23821. km, ecc= 0.71736\n",
"Choice 19 = 11: 6 resn w Deimos, period= 0.68887, revisit time= 7.5776 days, periapsis= 3920.5 , apoapsis= 27407. km, ecc= 0.74971\n",
"Choice 20 = 11: 7 resn w Deimos, period= 0.80369, revisit time= 8.8405 days, periapsis= 3920.5 , apoapsis= 30798. km, ecc= 0.77415\n",
"Choice 21 = 11: 8 resn w Deimos, period= 0.91850, revisit time= 10.103 days, periapsis= 3920.5 , apoapsis= 34030. km, ecc= 0.79339\n",
"Choice 22 = 11: 9 resn w Deimos, period= 1.0333, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 37130. km, ecc= 0.80899\n",
"Choice 23 = 11: 10 resn w Deimos, period= 1.1481, revisit time= 12.629 days, periapsis= 3920.5 , apoapsis= 40117. km, ecc= 0.82195\n",
"Choice 24 = 11: 12 resn w Deimos, period= 1.3777, revisit time= 15.155 days, periapsis= 3920.5 , apoapsis= 45808. km, ecc= 0.84233\n",
"Choice 25 = 12: 7 resn w Deimos, period= 0.73671, revisit time= 8.8405 days, periapsis= 3920.5 , apoapsis= 28841. km, ecc= 0.76066\n",
"Choice 26 = 12: 11 resn w Deimos, period= 1.1577, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 40361. km, ecc= 0.82293\n",
"Choice 27 = 12: 13 resn w Deimos, period= 1.3682, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 45578. km, ecc= 0.84159\n",
"Choice 28 = 13: 6 resn w Deimos, period= 0.58289, revisit time= 7.5776 days, periapsis= 3920.5 , apoapsis= 24105. km, ecc= 0.72022\n",
"Choice 29 = 13: 7 resn w Deimos, period= 0.68004, revisit time= 8.8405 days, periapsis= 3920.5 , apoapsis= 27138. km, ecc= 0.74755\n",
"Choice 30 = 13: 8 resn w Deimos, period= 0.77719, revisit time= 10.103 days, periapsis= 3920.5 , apoapsis= 30030. km, ecc= 0.76905\n",
"Choice 31 = 13: 9 resn w Deimos, period= 0.87434, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 32804. km, ecc= 0.78649\n",
"Choice 32 = 13: 10 resn w Deimos, period= 0.97149, revisit time= 12.629 days, periapsis= 3920.5 , apoapsis= 35476. km, ecc= 0.80097\n",
"Choice 33 = 13: 11 resn w Deimos, period= 1.0686, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 38060. km, ecc= 0.81323\n",
"Choice 34 = 13: 12 resn w Deimos, period= 1.1658, revisit time= 15.155 days, periapsis= 3920.5 , apoapsis= 40568. km, ecc= 0.82375\n",
"Choice 35 = 13: 14 resn w Deimos, period= 1.3601, revisit time= 17.681 days, periapsis= 3920.5 , apoapsis= 45383. km, ecc= 0.84096\n",
"Choice 36 = 14: 9 resn w Deimos, period= 0.81189, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 31033. km, ecc= 0.77568\n",
"Choice 37 = 14: 11 resn w Deimos, period= 0.99231, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 36037. km, ecc= 0.80377\n",
"Choice 38 = 14: 13 resn w Deimos, period= 1.1727, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 40744. km, ecc= 0.82445\n",
"Choice 39 = 14: 15 resn w Deimos, period= 1.3531, revisit time= 18.944 days, periapsis= 3920.5 , apoapsis= 45215. km, ecc= 0.84042\n",
"Choice 40 = 15: 7 resn w Deimos, period= 0.58937, revisit time= 8.8405 days, periapsis= 3920.5 , apoapsis= 24312. km, ecc= 0.72228\n",
"Choice 41 = 15: 8 resn w Deimos, period= 0.67357, revisit time= 10.103 days, periapsis= 3920.5 , apoapsis= 26941. km, ecc= 0.74593\n",
"Choice 42 = 15: 11 resn w Deimos, period= 0.92615, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 34240. km, ecc= 0.79453\n",
"Choice 43 = 15: 13 resn w Deimos, period= 1.0945, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 38736. km, ecc= 0.81618\n",
"Choice 44 = 15: 14 resn w Deimos, period= 1.1787, revisit time= 17.681 days, periapsis= 3920.5 , apoapsis= 40896. km, ecc= 0.82505\n",
"Choice 45 = 15: 16 resn w Deimos, period= 1.3471, revisit time= 20.207 days, periapsis= 3920.5 , apoapsis= 45069. km, ecc= 0.83995\n",
"Choice 46 = 16: 9 resn w Deimos, period= 0.71040, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 28056. km, ecc= 0.75479\n",
"Choice 47 = 16: 11 resn w Deimos, period= 0.86827, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 32633. km, ecc= 0.78550\n",
"Choice 48 = 16: 13 resn w Deimos, period= 1.0261, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 36940. km, ecc= 0.80810\n",
"Choice 49 = 16: 15 resn w Deimos, period= 1.1840, revisit time= 18.944 days, periapsis= 3920.5 , apoapsis= 41030. km, ecc= 0.82556\n",
"Choice 50 = 16: 17 resn w Deimos, period= 1.3419, revisit time= 21.470 days, periapsis= 3920.5 , apoapsis= 44941. km, ecc= 0.83953\n",
"Choice 51 = 17: 8 resn w Deimos, period= 0.59432, revisit time= 10.103 days, periapsis= 3920.5 , apoapsis= 24470. km, ecc= 0.72382\n",
"Choice 52 = 17: 9 resn w Deimos, period= 0.66861, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 26790. km, ecc= 0.74468\n",
"Choice 53 = 17: 10 resn w Deimos, period= 0.74290, revisit time= 12.629 days, periapsis= 3920.5 , apoapsis= 29024. km, ecc= 0.76200\n",
"Choice 54 = 17: 11 resn w Deimos, period= 0.81719, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 31185. km, ecc= 0.77665\n",
"Choice 55 = 17: 12 resn w Deimos, period= 0.89148, revisit time= 15.155 days, periapsis= 3920.5 , apoapsis= 33282. km, ecc= 0.78924\n",
"Choice 56 = 17: 13 resn w Deimos, period= 0.96577, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 35321. km, ecc= 0.80019\n",
"Choice 57 = 17: 14 resn w Deimos, period= 1.0401, revisit time= 17.681 days, periapsis= 3920.5 , apoapsis= 37309. km, ecc= 0.80982\n",
"Choice 58 = 17: 15 resn w Deimos, period= 1.1144, revisit time= 18.944 days, periapsis= 3920.5 , apoapsis= 39249. km, ecc= 0.81837\n",
"Choice 59 = 17: 16 resn w Deimos, period= 1.1886, revisit time= 20.207 days, periapsis= 3920.5 , apoapsis= 41147. km, ecc= 0.82602\n",
"Choice 60 = 17: 18 resn w Deimos, period= 1.3372, revisit time= 22.733 days, periapsis= 3920.5 , apoapsis= 44829. km, ecc= 0.83916\n",
"Choice 61 = 18: 11 resn w Deimos, period= 0.77179, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 29873. km, ecc= 0.76797\n",
"Choice 62 = 18: 13 resn w Deimos, period= 0.91212, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 33854. km, ecc= 0.79243\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Choice 63 = 18: 17 resn w Deimos, period= 1.1928, revisit time= 21.470 days, periapsis= 3920.5 , apoapsis= 41251. km, ecc= 0.82642\n",
"Choice 64 = 18: 19 resn w Deimos, period= 1.3331, revisit time= 23.996 days, periapsis= 3920.5 , apoapsis= 44728. km, ecc= 0.83883\n",
"Choice 65 = 19: 9 resn w Deimos, period= 0.59823, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 24595. km, ecc= 0.72503\n",
"Choice 66 = 19: 10 resn w Deimos, period= 0.66470, revisit time= 12.629 days, periapsis= 3920.5 , apoapsis= 26670. km, ecc= 0.74368\n",
"Choice 67 = 19: 11 resn w Deimos, period= 0.73117, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 28676. km, ecc= 0.75946\n",
"Choice 68 = 19: 12 resn w Deimos, period= 0.79764, revisit time= 15.155 days, periapsis= 3920.5 , apoapsis= 30623. km, ecc= 0.77301\n",
"Choice 69 = 19: 13 resn w Deimos, period= 0.86411, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 32517. km, ecc= 0.78481\n",
"Choice 70 = 19: 14 resn w Deimos, period= 0.93058, revisit time= 17.681 days, periapsis= 3920.5 , apoapsis= 34362. km, ecc= 0.79518\n",
"Choice 71 = 19: 15 resn w Deimos, period= 0.99705, revisit time= 18.944 days, periapsis= 3920.5 , apoapsis= 36164. km, ecc= 0.80439\n",
"Choice 72 = 19: 16 resn w Deimos, period= 1.0635, revisit time= 20.207 days, periapsis= 3920.5 , apoapsis= 37926. km, ecc= 0.81263\n",
"Choice 73 = 19: 17 resn w Deimos, period= 1.1300, revisit time= 21.470 days, periapsis= 3920.5 , apoapsis= 39652. km, ecc= 0.82005\n",
"Choice 74 = 19: 18 resn w Deimos, period= 1.1965, revisit time= 22.733 days, periapsis= 3920.5 , apoapsis= 41345. km, ecc= 0.82678\n",
"Choice 75 = 19: 20 resn w Deimos, period= 1.3294, revisit time= 25.259 days, periapsis= 3920.5 , apoapsis= 44638. km, ecc= 0.83853\n",
"Choice 76 = 20: 9 resn w Deimos, period= 0.56832, revisit time= 11.366 days, periapsis= 3920.5 , apoapsis= 23636. km, ecc= 0.71546\n",
"Choice 77 = 20: 11 resn w Deimos, period= 0.69461, revisit time= 13.892 days, periapsis= 3920.5 , apoapsis= 27581. km, ecc= 0.75109\n",
"Choice 78 = 20: 13 resn w Deimos, period= 0.82091, revisit time= 16.418 days, periapsis= 3920.5 , apoapsis= 31292. km, ecc= 0.77732\n",
"Choice 79 = 20: 17 resn w Deimos, period= 1.0735, revisit time= 21.470 days, periapsis= 3920.5 , apoapsis= 38187. km, ecc= 0.81379\n",
"Choice 80 = 20: 19 resn w Deimos, period= 1.1998, revisit time= 23.996 days, periapsis= 3920.5 , apoapsis= 41428. km, ecc= 0.82710\n",
"Choice 81 = 20: 21 resn w Deimos, period= 1.3261, revisit time= 26.522 days, periapsis= 3920.5 , apoapsis= 44557. km, ecc= 0.83826\n",
"Finished displaying resonances.\n"
]
}
],
"source": [
"mission_choice1 = 2 #Mission for sling A, or the moon sling if the capture sling is disabled.\n",
"mission_choice2 = mission_choice1 #Mission for sling B.\n",
"\n",
"mis = [mission_choice1-1,mission_choice1-1,mission_choice2-1,-1] #Populate list of mission indices based on the above choices, using the same indices as elsewhere. E.g. the first entry is the mission for the moon sling, then sling A, sling B, sling C. The moon sling mission only applies if the capture sling is disabled. The mission index for capture sling C will be copied from whichever sling it ends up being attached to.\n",
"\n",
"#Control the payload trajectories by choosing a \"trajopt\" option.\n",
"##################################################################### Perpendicular capture sling options:\n",
"#Values of \"trajopt\" < 100 setup the capture sling with its plane of rotation perpendicular to its orbital plane. The capture sling's axis of rotation points radially away from the planet when it releases the payload at periapsis. This orientation isn't tidally stabilized and tether reeling for maneuvering won't have steep gravitational gradients. But it's easy to calculate because both payloads are released at the same radial distance from the planet.\n",
"#trajopt = 1; traj_string = 'PERPENDICULAR SETUP. Give both payloads identical v_inf and identical release radii. Counterbalance goes directly into a circular orbit at periapsis_capture (though it should really either use aerobraking or go directly into an orbit with a periapsis above atmosphere and the same semi-latus rectum as the specified counterbalance orbit).'\n",
"##################################################################### Coplanar capture sling options:\n",
"#Values of \"trajopt\" > 100 setup the capture sling with its plane of rotation coplanar with its orbital plane, so there are two different tip speeds. This orientation is tidally stabilized and tether reeling for maneuvering will have steep gravitational gradients.\n",
"trajopt = 101; traj_string = 'COPLANAR SETUP. Capture slings release payloads A and B at r = periapsis_capture +/- sling A/B radius.'#This used to continue: 'Counterbalance A either uses aerobraking or goes directly into an orbit with a periapsis above atmosphere and the same semi-latus rectum as the specified counterbalance orbit. Counterbalance B either uses a tether to help lift a payload from a suborbital trajectory, aerobrakes until it can enter the specified counterbalance orbit, or aerobrakes until it hits Mars.'\n",
"\n",
"#failure = '!!!!! FAILURE !!!!!' #This line is normally commented, so that serious errors crash the program to make the error more obvious. The crashes happen by trying to 'print failure' so uncomment this line if those crashes aren't desired.\n",
"warnstring = '!!!WARNING!!! '\n",
"if trajopt < 100 and mission_choice1 != mission_choice2:\n",
" print warnstring,'Perpendicular slings require identical missions for both slings.'\n",
" print failure #Stop the program right here, because the variable 'failure' isn't defined.\n",
"\n",
"grapple_fraction = 0.25 #Grapple mass is this fraction of payload mass if it's accelerating at 1g. Grapple mass increases linearly with acceleration of the sling tip it's on, so it scales with the force placed on the grapple.\n",
"material='Zylon'; sigma = 5.8E9; rho = 1560 #sigma = tensile strength (Pa), rho = density (kg/m^3).\n",
"#material='Spectra 2000'; sigma = 3.5E9; rho = 970 #sigma = tensile strength (Pa), rho = density (kg/m^3).\n",
"safety = 1.0 #Engineering safety factor on sling tension. Jokic and Longuski 2004 table 3 gives a Phobos MEEM sling mass of 281 tons for an 11.2 ton payload, effectively using safety = 1.0. Jokic and Longuski 2004 table 4 simulates manufacturing errors to arrive at a Phobos MEEEM sling mass of 342 tons. For comparison, using a safety factor of 1.07 here yields a mass of 343 tons.\n",
"throw_in = 1 #Up to two places on the capture orbit are at the moon's (\"circular\") radial distance. Set to \"0\" to throw the payload OUT away from the planet or \"1\" to throw IN.\n",
"throw_at_LAN = 1 #If 1/0, moon sling throws to the capture sling at the longitude of the ascending/descending node.\n",
"ballast_choice = 1 #If a sling's cbr != 1, the forces on the hub are unequal by default, which means the center of mass isn't at the hub. If ballast_choice == 0, ignore this problem for comparison with studies that also ignore it. If ballast_choice == 1, the heavier mass on each sling has an extra ballast_mass1/2 added to it. This mass is zero for the lighter mass (whether that's the payload or the counterbalance). If ballast_choice == 2, the shorter sling is overbuilt as though its v_c (characteristic velocity) were reduced by a factor of cbr. This is the same as multiplying the safety factor on that sling tension by cbr^2. Using this option, all ballast_mass values are set to zero because the heavier counterbalance tether does all the necessary balancing.\n",
"payload_mass = 1.0*1000.0 #Calculate sling mass for a payload with this mass in kg. E.g. taxi (11.2*1000.0 kg), full vehicle (70*1000.0 kg), etc.\n",
"best_case_offset = 0.0 #Only applies if cbrs[1]==0. Set best_case_offset=0.0 to match Jokic and Longuski 2004, where the moon's orbit is in just the right orientation. Set best_case_offset=90 for a 90 degree offset, which is the worst case.\n",
"mt4ct = 1 #Number of moon throws 4 each capture throw. Moon sling throws \"mt4ct\" payloads for each capture sling throw. Set mt4ct = 1 for the shortest possible time between capture sling throws. Set mt4ct = -1 to automatically size the moon payload to match the larger of the capture payload or capture counterbalance. This way neither the capture payload or counterbalance has to be subdivided. Set mt4ct = -2 to automatically size the moon payload to match the capture payload, so if the capture counterbalance is larger it will have to be subdivided. If cbrs[1]==0, this is automatically disabled below by setting mt4ct = 1.\n",
"ms_spinup_time = 10*24*3600 #Spin up the moon sling over this amount of time. Set to -1 for a spinup time that's a perfect match for the shortest possible cycle time (based on the capture orbit revisit time and ms_attach_time below). #Set to -2 for a spinup time that's a perfect match for the next shortest possible cycle time. Etc. If ms_spinup_time is < 0, cs_spinup_time also have to be < 0, and vice versa.\n",
"cs_spinup_time = 10*24*3600 #Spin up the capture slings over this amount of time. Set to -1 for a spinup time that's a perfect match for the shortest possible cycle time (based on the capture orbit period, the time from the moon to periapsis, and cs_attach_time below). #Set to -2 for a spinup time that's a perfect match for the next shortest possible cycle time. Etc. If ms_spinup_time is < 0, cs_spinup_time also have to be < 0, and vice versa.\n",
"ms_attach_time = 1.0*24*3600 #Time needed for the moon sling to attach a new payload.\n",
"cs_attach_time = 3.0*24*3600 #Time needed for the capture slings to rendezvous with and attach new payloads.\n",
"estimate_misc = 1 #Set to 0 to zero all estimates of miscellaneous equipment: radiators, electric motors, the hub connecting the capture slings. Set to 1 to copy the solar panel mass estimate as an extremely crude estimate.\n",
"num_slings = 4 #Current system allows for a maximum of four slings- one on the moon and three in a system in an inclined resonant capture orbit (two with payload_mass and one extra payload that serves as moment of inertia ballast).\n",
"cbrs = [0.0 for i in range(num_slings)] #Initialize list of counterbalance ratios.\n",
"cbrs[0] = 4.0 #[dmr] #The first entry of cbrs is the ratio of moon sling counterbalance mass divided by moon sling payload mass.\n",
"cbrs[1] = -1.0 #Set cbrs[1]=0 to disable the capture orbit sling, which means the moon sling throws its payload directly into the specified hyperbolic escape trajectory. Any other value means the moon sling throws its payload into the inclined capture orbit so it can rendezvous with the capture orbit sling. If cbrs[1] > 0, the first capture sling's counterbalance ratio will be exactly cbrs[1], regardless of where the counterbalance goes. If cbrs[1] < 0, the first capture sling's counterbalance ratio will be calculated based on the desired counterbalance orbits, and the value of cbrs[1] here is ignored.\n",
"cbrs[2] = -1.0 #If cbrs[2] > 0, the second capture sling's counterbalance ratio will be exactly cbrs[2], regardless of where the counterbalance goes. If cbrs[2] < 0, the second capture sling's counterbalance ratio will be calculated based on the desired counterbalance orbits, and the value of cbrs[2] here is ignored.\n",
"tolerance = 1e-6 #Used to compare floating point values (e.g. any value less than tolerance is treated as \"zero\" in some calculations). Tolerance can't currently go below 1e-6, otherwise find_root fails to find the eccentric anomaly in kepler2xyz. Probably need to replace find_root with code from Project Pluto: https://www.projectpluto.com/\n",
"long_time = 1/tolerance^2 #Used to calculate the payload velocity after a \"long time\" has passed.\n",
"if abs(cbrs[1]) < tolerance: mt4ct = 1 #If capture orbit sling is disabled, override number of moon throws for each capture throw.\n",
"dmr = 4.60333884875169 #The \"dangerous mass ratio\" from near the bottom of this notebook. Any higher counterbalance mass ratio causes the longer sling to hit the heavier mass as it leaves.\n",
"\n",
"Isp = 379 #Rocket comparison - specific impulse in seconds. This value is for a LOX/CH4 chemical rocket, as in Jokic and Longuski 2004.\n",
"s2p = 0.15 #Rocket comparison - structural to propellant mass ratio. Use 0.15 to match Jokic and Longuski 2004.\n",
"\n",
"descs = ['moon sling'] #Verbose descriptions of the first index in many lists. 1st index is the sling (0=M,1=A,2=B,3=C).\n",
"descs += ['capture sling A'] ; descs += ['capture sling B'] ; descs += ['capture sling C']\n",
"sdescs = ['M'] #Short descriptions.\n",
"sdescs += ['A'] ; sdescs += ['B'] ; sdescs += ['C']\n",
"\n",
"def cap1st(string1):\n",
" \"\"\"\n",
" Input : String of characters.\\n\n",
" Output: Same string, with the first letter capitalized but no other changes (as would happen if just capitalize() were used).\n",
" \"\"\"\n",
" return string1[0].capitalize()+string1[1:]\n",
"\n",
"for j in range(num_slings-1):\n",
" if cbrs[j] > tolerance:\n",
" if cbrs[j] > dmr: print '!!! DANGER !!!',cap1st(descs[j]),'mass ratio',cbrs[j],'is higher than the safe limit of',dmr,'!!!'\n",
" if cbrs[j] < 1/dmr: print '!!! DANGER !!!',cap1st(descs[j]),'mass ratio',cbrs[j],'is lower than the safe limit of',1/dmr,'!!!'\n",
"\n",
"#Choose the units used to display time, then uncomment that line:\n",
"#units=3600; uname='hours'\n",
"units=3600*24; uname='days'\n",
"#units=88775.244147; uname='sols' #https://en.wikipedia.org/wiki/Timekeeping_on_Mars\n",
"#units=3600*24*7; uname='weeks'\n",
"#units=3600*24*365.25; uname='years'\n",
"year = 3600*24*365.25\n",
"\n",
"summary_digits = 5 #Many print statements in this notebook will print \"summary_digits\" significant figures.\n",
"\n",
"mu_mars = 4.282837E13 #G*M for Mars. https://en.m.wikipedia.org/wiki/Standard_gravitational_parameter\n",
"mu_sun = 1.32712440018E20 #G*M for the Sun. https://en.m.wikipedia.org/wiki/Standard_gravitational_parameter\n",
"mu_earth = 3.986004418E14 #G*M for the Earth. https://en.m.wikipedia.org/wiki/Standard_gravitational_parameter\n",
"a_mars = 227.9392E9 # https://en.m.wikipedia.org/wiki/Mars\n",
"a_earth = 149.598023E9 # https://en.m.wikipedia.org/wiki/Earth\n",
"a_jupiter = 778570000000 #In meters. https://en.wikipedia.org/wiki/Jupiter\n",
"ecc_mars = 0.0934 # https://en.m.wikipedia.org/wiki/Mars\n",
"ecc_earth = 0.0167086 # https://en.m.wikipedia.org/wiki/Earth\n",
"ecc_jupiter = 0.0489 #In meters. https://en.wikipedia.org/wiki/Jupiter\n",
"inc_mars = 1.850*pi/180 # Relative to ecliptic plane. https://en.m.wikipedia.org/wiki/Mars\n",
"inc_earth = 0 #By definition.\n",
"inc_jupiter = 1.303*pi/180 #Relative to ecliptic plane https://en.wikipedia.org/wiki/Jupiter\n",
"solar_constant = 1361 #Watts/square meter at 1 AU. https://en.wikipedia.org/wiki/Solar_constant\n",
"radius_sun = 695700000.0 #The Sun's equatorial radius in meters. https://en.wikipedia.org/wiki/Sun\n",
"radius_mars = 3396000.0 #Mars's equatorial radius. https://en.wikipedia.org/wiki/Mars\n",
"polar_radius_mars = 3376200.0 #Mars's polar radius. https://en.wikipedia.org/wiki/Mars\n",
"axial_tilt_mars = 25.19*pi/180 #Axial tilt in radians, relative to its orbital plane.\n",
"J2_mars = 1960.45E-6 #Oblateness of Mars, given as \"J2\" here: https://nssdc.gsfc.nasa.gov/planetary/factsheet/marsfact.html http://archive.li/4Bv1P\n",
"soi_mars = a_mars*(mu_mars/mu_sun)^0.4 #5.76E8m from https://en.m.wikipedia.org/wiki/Sphere_of_influence_(astrodynamics)\n",
"radius_earth = 6378100.0 #Earth's equatorial radius. https://en.m.wikipedia.org/wiki/Earth\n",
"polar_radius_earth = 6356800.0 #Earth's polar radius. https://en.m.wikipedia.org/wiki/Earth\n",
"axial_tilt_earth = 23.439*pi/180 #Axial tilt in radians, relative to its orbital plane.\n",
"J2_earth = 1082.63E-6 #Oblateness of Earth, given as \"J2\" here: https://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html http://archive.li/Jj9PO\n",
"soi_earth = a_earth*(mu_earth/mu_sun)^0.4 #9.24E8m from https://en.m.wikipedia.org/wiki/Sphere_of_influence_(astrodynamics)\n",
"periapsis_phobos = 9234420.0 # https://en.wikipedia.org/wiki/Phobos_(moon)\n",
"apoapsis_phobos = 9517580.0\n",
"periapsis_deimos = 23455500.0 # https://en.wikipedia.org/wiki/Deimos_(moon)\n",
"apoapsis_deimos = 23470900.0\n",
"a_phobos = (apoapsis_phobos + periapsis_phobos)/2 ; b_phobos = sqrt(apoapsis_phobos*periapsis_phobos)\n",
"a_deimos = (apoapsis_deimos + periapsis_deimos)/2 ; b_deimos = sqrt(apoapsis_deimos*periapsis_deimos)\n",
"period_phobos = (2*pi*sqrt(a_phobos^3/mu_mars)).n() #mu_mars instead of mu because Phobos and Deimos orbit Mars.\n",
"period_deimos = (2*pi*sqrt(a_deimos^3/mu_mars)).n()\n",
"inc_phobos = 26.04*pi/180 #Inclination in radians, relative to ecliptic plane.\n",
"inc_deimos = 27.58*pi/180\n",
"\n",
"#These values for the Martian atmosphere are currently copied over to other planets like Earth, but if you plan to throw from Earth or aerobrake in its atmosphere you should change those values.\n",
"mars_aerobrake_alt = 110000.0 #The lowest altitude used in the Mars Global Surveyor aerobraking process: http://web.archive.org/web/20170416044559/https://mars.jpl.nasa.gov/mgs/sci/aerobrake/SFMech.html\n",
"mars_atmo_alt = 253000.0 #MERITT's Marswhip tip never goes below 253 km altitude, so treat that as the effective height of Mars's atmosphere.\n",
"lmo_alt = 400000.0 #The counterbalance masses can be sent into the 400km low Mars orbit (LMO) described in table 1 of http://www.csc.caltech.edu/references/Hopkins-Phobos-Deimos-Paper.pdf\n",
"\n",
"#Choose a planet of origin, then uncomment that line.\n",
"pname = 'Mars'; mu = mu_mars; a_pl = a_mars; r_pl = radius_mars; polar_r_pl = polar_radius_mars; axial_tilt_pl = axial_tilt_mars; soi_pl = soi_mars; ecc_pl = ecc_mars; inc_pl = inc_mars; aerobrake_alt = mars_aerobrake_alt; atmo_alt = mars_atmo_alt; target_alt = lmo_alt; J2 = J2_mars\n",
"#pname = 'Earth'; mu = mu_earth; a_pl = a_earth; r_pl = radius_earth; polar_r_pl = polar_radius_earth; axial_tilt_pl = axial_tilt_earth; soi_pl = soi_earth; ecc_pl = ecc_earth; inc_pl = inc_earth; aerobrake_alt = mars_aerobrake_alt; atmo_alt = mars_atmo_alt; target_alt = lmo_alt; J2 = J2_earth\n",
"#pname = 'GM=1'; mu = 1.0; a_pl = a_earth; r_pl = radius_earth; polar_r_pl = polar_radius_earth; soi_pl = 1; ecc_pl = 0; inc_pl = 0; aerobrake_alt = mars_aerobrake_alt; atmo_alt = mars_atmo_alt; target_alt = lmo_alt #Sets G*M = 1.0 for comparison with this: https://janus.astro.umd.edu/orbits/elements/convertframe.html\n",
"\n",
"#Choose a moon of origin (Phobos or Deimos), then uncomment that line:\n",
"#moon_name = 'Phobos'; periapsis_moon = periapsis_phobos; apoapsis_moon = apoapsis_phobos; inc_moon = inc_phobos\n",
"moon_name = 'Deimos'; periapsis_moon = periapsis_deimos; apoapsis_moon = apoapsis_deimos; inc_moon = inc_deimos\n",
"\n",
"#The planet's oblateness (\"J2\") affects the capture sling orbit in three ways, described here: http://farside.ph.utexas.edu/teaching/celestial/Celestialhtml/node93.html http://archive.li/amqki\n",
"#AP_rate = 3*J2/4*mean_motion_cs*r_pl^2/a_cs^2*(5*cos(inc_c2q)-1)/(1-ecc_cs^2)^2 #Argument of periapsis rotation rate due to J2. Equation 10.127 http://archive.li/amqki\n",
"#LAN_rate = -3*J2/2*mean_motion_cs*r_pl^2/a_cs^2*cos(inc_c2q) #Node precession rate due to J2. Equation 10.128 http://archive.li/amqki\n",
"#mean_motion_cs_factor = 1.0 + 3*J2/2*(r_pl/a_cs)^2*(1-ecc_cs^2)^(-3/2)*(1-3/2*sin(inc_c2q)^2) #Mean motion factor due to J2. Equation 10.129 (just the part in brackets because this is just the factor multiplying the standard Keplerian mean motion) http://archive.li/amqki\n",
"\n",
"a_moon = (apoapsis_moon + periapsis_moon)/2 ; b_moon = sqrt(apoapsis_moon*periapsis_moon)\n",
"speed_moon = sqrt(mu/a_moon)\n",
"period_moon = (2*pi*sqrt(a_moon^3/mu)).n() # https://en.wikipedia.org/wiki/Elliptic_orbit#Orbital_period\n",
"\n",
"#Specify one of the apsides of the capture orbit. Doesn't matter if it's the apoapsis or periapsis. The resonance search will look for another apsis which satisfies the desired orbital period, then the capture orbit periapsis/apoapsis will be set equal to the smaller/larger apsis.\n",
"lowest_apsis = r_pl + target_alt + r_max #Any lower and the tips of the capture slings might hit objects in the target orbit.\n",
"apsis1 = lowest_apsis\n",
"#apsis1 = a_moon #This could either be a periapsis or an apoapsis.\n",
"\n",
"min_s= 1; min_p= 1; #Sling completes \"s\" orbits in the same time it takes the moon to complete \"p\" orbits.\n",
"max_s=20; max_p=30; #Given periapsis_capture (specified above), display resonances in these s,p ranges satisfying these conditions:\n",
"#lower_bound = 12*units #Choose upper and lower bounds of period_resonance to display in find_resonance.\n",
"#upper_bound = 15*units #These bounds are useful for high v_inf missions.\n",
"lower_bound = 0.0#*period_moon #Choose upper and lower bounds of period_resonance to display in find_resonance.\n",
"upper_bound = 1.1*period_moon #These bounds are useful for low v_inf missions.\n",
"assume(r,'real')\n",
"list_resn_s = [] ; list_resn_p = [] ; list_periapsis = [] ; list_apoapsis = []\n",
"print 'Resonance s:p is defined as: s*period_capture = p*period_moon.'\n",
"for s in range(min_s,max_s+1): #range(n1,n2) goes up to n2-1.\n",
" sys.stdout.flush()\n",
" for p in range(min_p,max_p+1):\n",
" if (s != p or s==1) and (s%2 + p%2 != 0) and (s%3 + p%3 != 0) and (s%4 + p%4 != 0) and (s%5 + p%5 != 0) and (s%6 + p%6 != 0) and (s%7 + p%7 != 0) and (s%8 + p%8 != 0) and (s%9 + p%9 != 0) and (s%10 + p%10 != 0): #eliminate some redundant resonances like 2:2 or 9:3 by brute force\n",
" period_resonance = (RDF(p)/RDF(s))*period_moon\n",
" revisit_time = s*period_resonance #equals p*target_period\n",
" #if revisit_time < upper_bound and revisit_time > lower_bound:\n",
" if period_resonance < upper_bound and period_resonance > lower_bound:\n",
" #Orbital period = 2*pi*sqrt(a^3/mu) = 2*pi*sqrt(((r_p + r_a)/2)^3/mu)\n",
" apsis2 = (2*(mu*(period_resonance/(2*pi))^2)^(1/3) - apsis1).n()\n",
" if apsis2 > lowest_apsis and max([apsis1,apsis2]) >= a_moon: #Don't display any resonances so low that the tips of the capture slings might hit objects in low Mars orbit. Also don't bother displaying resonances with an apoapsis below the chosen moon's \"circular\" orbit.\n",
" list_resn_s += [s] ; list_resn_p += [p]\n",
" list_periapsis += [min([apsis1,apsis2])] ; list_apoapsis += [max([apsis1,apsis2])]\n",
" ecc = (list_apoapsis[-1] - list_periapsis[-1]) / (list_apoapsis[-1] + list_periapsis[-1])\n",
" print 'Choice',str(len(list_resn_s)).rjust(3)+' =',str(list_resn_s[-1]).rjust(2)+':',str(list_resn_p[-1]).ljust(2),'resn w',moon_name+', period=',str((period_resonance/units).n(digits=5)).rjust(7)+', revisit time=',(revisit_time/units).n(digits=5),uname+', periapsis=',(list_periapsis[-1]/1000).n(digits=5),', apoapsis=',(list_apoapsis[-1]/1000).n(digits=5),'km, ecc=',ecc.n(digits=5)\n",
" sys.stdout.flush()\n",
"print 'Finished displaying resonances.'"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 3. Choose an orbital resonance for the capture slings. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Capture sling A throws payloads with v_inf = 2640.0, which is a M-E Hohmann mission.\n",
"Capture sling B throws payloads with v_inf = 2640.0, which is a M-E Hohmann mission.\n",
"\n",
"Resonance number 18 was chosen.\n",
"11:5 resonance with Deimos, period= 0.57406, revisit time= 6.3147 days.\n",
"\n",
"Capture orbit apoapsis (km) = 23821.3846522879\n",
"Capture orbit periapsis (km) = 3920.46738022426\n",
"Capture orbit periapsis altitude (km) = 524.467380224261\n",
"Capture orbit eccentricity = 0.717360803768281\n"
]
}
],
"source": [
"resonance_choice = 18\n",
"resn_s = list_resn_s[resonance_choice-1]\n",
"resn_p = list_resn_p[resonance_choice-1]\n",
"periapsis_capture = list_periapsis[resonance_choice-1]\n",
"apoapsis_capture = list_apoapsis[resonance_choice-1]\n",
"a_capture = (apoapsis_capture + periapsis_capture)/2 ; b_capture = sqrt(apoapsis_capture*periapsis_capture)\n",
"period_capture = (2*pi*sqrt(a_capture^3/mu_mars)).n()\n",
"\n",
"for j in range(1,3): print cap1st(descs[j]),'throws payloads with v_inf =',str(v_infs[mis[j]].n(digits=5))+', which is a',mnames[mis[j]],'mission.'\n",
"\n",
"print '\\nResonance number',resonance_choice,'was chosen.\\n',str(resn_s)+':'+str(resn_p),'resonance with',moon_name+', period=',str((period_capture/units).n(digits=5)).rjust(7)+', revisit time=',((resn_p*period_moon)/units).n(digits=5),uname+'.'\n",
"\n",
"lj=37\n",
"print '\\nCapture orbit apoapsis (km)'.ljust(lj+1),'=',apoapsis_capture/1000.0\n",
"print 'Capture orbit periapsis (km)'.ljust(lj),'=',periapsis_capture/1000.0\n",
"print 'Capture orbit periapsis altitude (km)'.ljust(lj),'=',(periapsis_capture-r_pl)/1000.0\n",
"ecc = (apoapsis_capture - periapsis_capture) / (apoapsis_capture + periapsis_capture)\n",
"print 'Capture orbit eccentricity'.ljust(lj),'=',ecc"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 4. Estimate the required tip speed to compare with the tip speed specified in step 1. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"COPLANAR SETUP. Capture slings release payloads A and B at r = periapsis_capture +/- sling A/B radius. \n",
"\n",
"Mars sphere of influence (km) = 577227.461555802\n",
"Capture orbit apoapsis (km) = 23821.3846522879\n",
"Capture orbit periapsis (km) = 3920.46738022426\n",
"Capture orbit periapsis altitude (km) = 524.467380224261\n",
"Capture orbit period (days) = 0.574061651272195\n",
"Phobos period (days) = 0.319026493831701\n",
"Deimos period (days) = 1.26293563279884\n",
"Maximum sling radius (km) = 124.467380224261\n",
"Capture orbit speed at periapsis (m/s) = 4331.39333469434\n",
"Required tip speed of best-case direct moon sling (m/s) = 1907.82552332217\n",
"\n",
"Consider a capture sling with its plane of rotation perpendicular to its orbital plane:\n",
"Required tip speed of capture sling (m/s) = 1036.86561791447\n",
"\n",
"Consider a capture sling with its plane of rotation coplanar with its orbital plane, so there are two different tip speeds:\n",
"Required tip speed of capture sling (coplanar setup- far side) = 973.877350324047\n",
"Required tip speed of capture sling (coplanar setup- near side) = 1103.18116566905\n"
]
}
],
"source": [
"#It's easiest to orient the initial capture sling orbit so its periapsis position lies along the positive x axis because that matches the definition of Euler angles:\n",
"#https://en.wikipedia.org/wiki/Orbital_elements#Euler_angle_transformations\n",
"#\"x, y are in the orbital plane and with x in the direction to the pericenter (periapsis). z is perpendicular to the plane of the orbit. y is mutually perpendicular to x and z.\"\n",
"com_cs_pos0 = vector([periapsis_capture,0,0]) #Capture sling's center of mass position vector when it reaches periapsis.\n",
"capture_speed_periapsis = sqrt(mu_mars*(2/periapsis_capture-1/a_capture))\n",
"capture_speed_apoapsis = sqrt(mu_mars*(2/apoapsis_capture-1/a_capture))\n",
"com_cs_vel0 = vector([0,capture_speed_periapsis,0]) #Capture sling's center of mass velocity vector when it reaches periapsis.\n",
"H_cs = com_cs_pos0.cross_product(com_cs_vel0)\n",
"H_cs_unit = H_cs/H_cs.norm()\n",
"if H_cs[2].n() > 0: orbit_sign = 1 #Capture orbit sling c.o.m. orbits counter-clockwise, so its anomaly increases over time.\n",
"else: orbit_sign = -1 #Capture orbit sling c.o.m. orbits clockwise, so its anomaly DEcreases over time.\n",
"\n",
"#Position and velocity of moon when it throws its payload to the capture sling at the moment it passes through its ascending node in the ecliptic plane.\n",
"#The \"0\" suffix indicades that these are the initial vectors in the moon frame (described below) before they're rotated into the capture frame.\n",
"p_moon0 = com_cs_pos0*a_moon/com_cs_pos0.norm()\n",
"v_moon0 = com_cs_vel0*speed_moon/com_cs_vel0.norm()\n",
"\n",
"#Rotate the moon's position and velocity vectors so the x-y plane is the ecliptic. I.e. rotate into the capture frame.\n",
"#This only works if the moon's position vector lies along the x axis.\n",
"if abs(p_moon0[0] - p_moon0.norm()) > tolerance:\n",
" print warnstring,'p_moon0 needs to lie along the x-axis but instead it equals:',p_moon0\n",
" print failure\n",
"\n",
"def rotateX(rad):\n",
" \"\"\"\n",
" Input : Angle in radians.\\n\n",
" Output: 3D rotation matrix about Z axis by 'rad' radians.\\n\n",
" Reference: https://en.wikipedia.org/wiki/Rotation_matrix#Basic_rotations\n",
" \"\"\"\n",
" return matrix([[1,0,0],[0,cos(rad),-sin(rad)],[0,sin(rad),cos(rad)]])\n",
"\n",
"p_moon1 = rotateX(inc_moon)*p_moon0 #Since p_moon0 currently lies along the x-axis, this means p_moon1 = p_moon0.\n",
"v_moon1 = rotateX(inc_moon)*v_moon0\n",
"\n",
"def restrict_rad(r):\n",
" \"\"\"\n",
" Input : Angle in radians.\\n\n",
" Output: Restricts angle to lie in [0,2*pi).\n",
" \"\"\"\n",
" while r >= 2*pi - tolerance: r = r - 2*pi #Offset [0,2*pi) by tolerance, otherwise deg = 359.999999 doesn't get changed to -0.000001.\n",
" while r < -tolerance: r = r + 2*pi #Offset [0,2*pi) by tolerance, otherwise r = -1e-15 gets changed to r = 2*pi.\n",
" return r\n",
"\n",
"LAN_moon = restrict_rad(atan2(p_moon1[1],p_moon1[0])) #Moon's longitude of the ascending node through the ecliptic plane. Sagemath expects arctan2(y,x).\n",
"moon_frame_string = 'Final vectors in this cell are calculated in a frame where '+moon_name+' stays in the x-y plane and the capture slings are inclined by '+str((inc_moon*180/pi).n(digits=4))+' degrees with a longitude of ascending node arbitrarily set to '+str((LAN_moon*180/pi).n(digits=4))+' degrees.\\n' #Newer code doesn't use this frame.\n",
"if abs(cbrs[1]) > tolerance: capture_frame_string = 'Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane'\n",
"else: capture_frame_string = 'Final vectors in this cell are calculated in a frame where the x-y plane is the ecliptic'\n",
"capture_frame_string += ' and '+moon_name+' is inclined by '+str((inc_moon*180/pi).n(digits=4))+' degrees with a longitude of ascending node arbitrarily set to '+str((LAN_moon*180/pi).n(digits=4))+' degrees.\\n'\n",
"nullvec = vector([0,0,0])\n",
"\n",
"#specific orbital energy = v^2/2 - mu/r = -mu/(2*a)\n",
"#v = sqrt(mu*(2/r-1/a))\n",
"\n",
"#Calculate the required tip speed of the capture sling to achieve the desired v_inf:\n",
"assume(v_tip>0,r>0)\n",
"eq_v_tip = v_infs[mis[1]]^2 == 2*((v+v_tip)^2/2 - mu/r) #Definition: C3 characteristic energy = v_inf^2 = 2*specific orbital energy https://en.wikipedia.org/wiki/Characteristic_energy\n",
"\n",
"#For simplicity, first consider a capture sling with its plane of rotation perpendicular to its orbital plane. If the capture sling's axis of rotation points radially away from Mars when it releases the payload at periapsis while rotating perpendicular to Mars, r ~= periapsis_capture. (If the sling were perfectly straight and perpendicular, r would be sqrt(periapsis_capture^2+r_max^2). However, the sling will bend towards Mars somewhat so just use r = periapsis_capture to simplify the calculations.)\n",
"soln_v_tip = solve(eq_v_tip.subs(v=capture_speed_periapsis,r=periapsis_capture),v_tip)\n",
"v_tip_capture_payload1 = soln_v_tip[0].rhs().n() #This is called capture_payload1 because the capture sling actually throws two payloads.\n",
"\n",
"#Alternatively, consider a capture sling with its plane of rotation coplanar with its orbital plane, so there are two different tip speeds. If the capture sling releases the payload at the farthest point from Mars, r = periapsis_capture+r_max .\n",
"soln_v_tip = solve(eq_v_tip.subs(v=capture_speed_periapsis,r=periapsis_capture+r_max),v_tip)\n",
"v_tip_capture_farside = soln_v_tip[0].rhs().n()\n",
"#If the capture sling releases the payload at the nearest point from Mars, r = periapsis_capture-r_max .\n",
"soln_v_tip = solve(eq_v_tip.subs(v=capture_speed_periapsis,r=periapsis_capture-r_max),v_tip)\n",
"v_tip_capture_nearside = soln_v_tip[0].rhs().n()\n",
"\n",
"#Calculate tip speed of the best-case direct moon sling needed to achieve the desired v_inf (for Phobos, this ~reproduces values in Jokic and Longuski 2004 table 3)\n",
"soln3 = solve(eq_v_tip.subs(v=speed_moon,r=a_moon),v_tip)\n",
"v_tip_direct_moon_best_case = soln3[0].rhs().n()\n",
"\n",
"print '\\n',traj_string,'\\n'\n",
"lj=45\n",
"desc = pname+' sphere of influence (km)' ; print desc.ljust(lj),'=',soi_pl/1000.0\n",
"if abs(cbrs[1]) > tolerance:\n",
" print 'Capture orbit apoapsis (km)'.ljust(lj),'=',apoapsis_capture/1000.0\n",
" print 'Capture orbit periapsis (km)'.ljust(lj),'=',periapsis_capture/1000.0\n",
" print 'Capture orbit periapsis altitude (km)'.ljust(lj),'=',(periapsis_capture-r_pl)/1000.0\n",
" desc = 'Capture orbit period ('+uname+')' ; print desc.ljust(lj),'=',period_capture/units\n",
"desc = 'Phobos period ('+uname+')' ; print desc.ljust(lj),'=',period_phobos/units\n",
"desc = 'Deimos period ('+uname+')' ; print desc.ljust(lj),'=',period_deimos/units\n",
"print 'Maximum sling radius (km)'.ljust(lj),'=',r_max/1000.0\n",
"if abs(cbrs[1]) > tolerance: print 'Capture orbit speed at periapsis (m/s)'.ljust(lj),'=',capture_speed_periapsis\n",
"print 'Required tip speed of best-case direct moon sling (m/s)'.ljust(lj),'=',v_tip_direct_moon_best_case\n",
"\n",
"if abs(cbrs[1]) > tolerance:\n",
" print '\\nConsider a capture sling with its plane of rotation perpendicular to its orbital plane:'\n",
" print 'Required tip speed of capture sling (m/s) =',v_tip_capture_payload1\n",
" if trajopt < 100 and v_tip_capture_payload1 > v_tip_max:\n",
" print warnstring,'This tip speed is greater than the specified maximum tip speed of',v_tip_max,'m/s!'\n",
" print failure\n",
" print '\\nConsider a capture sling with its plane of rotation coplanar with its orbital plane, so there are two different tip speeds:'\n",
" print 'Required tip speed of capture sling (coplanar setup- far side) =',v_tip_capture_farside\n",
" print 'Required tip speed of capture sling (coplanar setup- near side) =',v_tip_capture_nearside\n",
" if trajopt >= 100 and v_tip_capture_nearside > v_tip_max:\n",
" print warnstring,'The near side tip speed is greater than the specified maximum tip speed of',v_tip_max,'m/s!'\n",
" print failure"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 5. Initialize lists and define functions. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lists initialized.\n"
]
}
],
"source": [
"#Initialize lists that describe slings.\n",
"\n",
"#cbrs were initialized above, but make a copy because negative values will be changed.\n",
"orig_cbrs = copy.deepcopy(cbrs) #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
"orig_traj_string = traj_string\n",
"\n",
"#These lists have a single entry per sling. 1st index is the sling (0=M,1=A,2=B,3=C).\n",
"radii = [-1.0 for i in range(num_slings)]\n",
"omegas = [-1.0 for i in range(num_slings)]\n",
"throw_times = [ 0.0 for i in range(num_slings)] #Time in seconds at which each sling throws its masses, relative to capture sling periapsis. Set to 0 so trajopt < 100 don't need to explicitly zero these.\n",
"#theta0s[j] is sling j's rotational angle \"theta\" at time 0 when the capture sling reaches periapsis. In general NOT equal to \"theta\" at throw_times[j] unless throw_times[j]==0.\n",
"#If theta0s[j]==0, when the capture sling reaches periapsis, that sling's payload arm has a tip velocity vector parallel to the c.o.m. velocity.\n",
"theta0s = [ 0.0 for i in range(num_slings)] #Set to 0 so trajopt < 100 don't need to explicitly zero these.\n",
"coms_empty = [-1.0 for i in range(num_slings)]\n",
"coms_full = [-1.0 for i in range(num_slings)]\n",
"junopanel_masses = [-1.0 for i in range(num_slings)]\n",
"spinup_times = [-1.0 for i in range(num_slings)]\n",
"spindown_times = [-1.0 for i in range(num_slings)]\n",
"wait_times = [-1.0 for i in range(num_slings)]\n",
"avg_powers = [-1.0 for i in range(num_slings)]\n",
"avg_powers_alt = [-1.0 for i in range(num_slings)]\n",
"avg_torques = [-1.0 for i in range(num_slings)]\n",
"radiator_masses = [-1.0 for i in range(num_slings)]\n",
"motor_masses = [-1.0 for i in range(num_slings)]\n",
"hub_masses = [-1.0 for i in range(num_slings)]\n",
"\n",
"#These lists have two entries per sling. 1st index is the sling (0=M,1=A,2=B,3=C). 2nd index is the payload arm [0] or the counterbalance arm [1].\n",
"keplers = [[-1.0 for i in range(2)] for j in range(num_slings)] #Keplerian elements for the payloads and balances as they're thrown.\n",
"throw_tpers = [[ 0.0 for i in range(2)] for j in range(num_slings)] #\"Time since periapsis\" for the payloads and balances as they're thrown. Set to 0 so trajopt < 100 don't need to explicitly zero these.\n",
"throw_pos = [[nullvec for i in range(2)] for j in range(num_slings)] #Position vectors for the payloads and balances as they're thrown.\n",
"throw_vel = [[nullvec for i in range(2)] for j in range(num_slings)] #Velocity vectors for the payloads and balances as they're thrown.\n",
"payload_masses = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"v_cs = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"tether_masses = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"grapple_masses = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"ballast_masses = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"tip_masses_empty = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"tip_masses_full = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"tip_diams = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"hub_diams = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"moments_empty = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"moments_full = [[-1.0 for i in range(2)] for j in range(num_slings)]\n",
"\n",
"print 'Lists initialized.'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"Capture sling orbit:\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (3920.46738022426, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (0.000000000000000, 4.33139333469434, 0.000000000000000)\n",
"Radial distance (km) = 3920.46738022426\n",
"Speed (km/s) = 4.33139333469434\n",
"Radial velocity (km/s) = 0.000000000000000\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 13.7774796305327\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 1.69810862795899e10)\n",
"Specific relative angular momentum (m^2/s) = 1.69810862795899e10\n",
"Specific orbital energy (kJ/kg) = -1543.81798121506\n",
"Eccentricity vector = (0.717360803768281, 0.000000000000000, 0.000000000000000)\n",
"Eccentricity = 0.717360803768281 , Alt. calc. = 0.717360803768281\n",
"Semi-latus rectum (km) = 6732.85701124926 , Alt. calc. = 6732.85701124927\n",
"Semi-minor axis (km) = 9663.89991054697\n",
"Semi-major axis (km) = 13870.9260162561\n",
"Periapsis altitude (km) = 524.467380224261\n",
"Periapsis (km) = 3920.46738022426\n",
"Apoapsis altitude (km) = 20425.3846522879\n",
"Apoapsis (km) = 23821.3846522879\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Mean anomaly (degrees) = 0.000000000000000\n",
"Eccentric anomaly (degrees) = 0.000000000000000\n",
"True anomaly (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Time since periapsis (hours) = 0.000000000000000\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n"
]
}
],
"source": [
"#Define some custom functions.\n",
"print capture_frame_string\n",
"\n",
"def is_numeric(expr):\n",
" \"\"\"\n",
" Input : An expression that's either numeric or symbolic.\\n\n",
" Output: A \"1\" if the expression is numeric (meaning \".n()\" works), or a \"0\" if it's symbolic.\\n\n",
" Reference:\\n\n",
" https://ask.sagemath.org/question/10495/how-to-check-if-a-symbolic-expression-is-numerically-evaluable/\n",
" \"\"\"\n",
" is_it = 1\n",
" if not hasattr(expr, \"__n__\"): is_it = 0\n",
" else:\n",
" try: n = expr.n()\n",
" except TypeError: is_it = 0\n",
" return is_it\n",
"\n",
"def debug_sling(sind):\n",
" \"\"\"\n",
" Input : Sling index \"sind\". Prints many global variables.\\n\n",
" Output: Displays sling variables using sling index \"sind\" for debugging.\n",
" \"\"\"\n",
" desc = '\\n'\n",
" ndigits = 3\n",
" desc += 'cbrs['+str(sind)+']= '+str(cbrs[sind].n(digits=ndigits))+', ' #This list was initialized separately at the beginning.\n",
" desc += 'radii['+str(sind)+']= '+str(radii[sind].n(digits=ndigits))+', '\n",
" desc += 'omegas['+str(sind)+']= '+str(omegas[sind].n(digits=ndigits))+', '\n",
" desc += 'throw_times['+str(sind)+']= '+str(throw_times[sind].n(digits=ndigits))+', '\n",
" desc += 'theta0s['+str(sind)+']= '+str(theta0s[sind].n(digits=ndigits))+', '\n",
" desc += 'coms_empty['+str(sind)+']= '+str(coms_empty[sind].n(digits=ndigits))+', '\n",
" desc += 'coms_full['+str(sind)+']= '+str(coms_full[sind].n(digits=ndigits))+', '\n",
" desc += 'junopanel_masses['+str(sind)+']= '+str(junopanel_masses[sind].n(digits=ndigits))+', '\n",
" desc += 'spinup_times['+str(sind)+']= '+str(spinup_times[sind].n(digits=ndigits))+', '\n",
" desc += 'spindown_times['+str(sind)+']= '+str(spindown_times[sind].n(digits=ndigits))+', '\n",
" desc += 'wait_times['+str(sind)+']= '+str(wait_times[sind].n(digits=ndigits))+', '\n",
" desc += 'avg_powers['+str(sind)+']= '+str(avg_powers[sind].n(digits=ndigits))+', '\n",
" desc += 'avg_powers_alt['+str(sind)+']= '+str(avg_powers_alt[sind].n(digits=ndigits))+', '\n",
" desc += 'avg_torques['+str(sind)+']= '+str(avg_torques[sind].n(digits=ndigits))+', '\n",
" #1st index is the sling (0=M,1=A,2=B,3=C). 2nd index is the payload arm [0] or the counterbalance arm [1].\n",
" desc += 'keplers['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in keplers[sind]])+', '\n",
" desc += 'throw_tpers['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in throw_tpers[sind]])+', '\n",
" desc += 'throw_pos['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in throw_pos[sind]])+', '\n",
" desc += 'throw_vel['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in throw_vel[sind]])+', '\n",
" desc += 'payload_masses['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in payload_masses[sind]])+', '\n",
" desc += 'v_cs['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in v_cs[sind]])+', '\n",
" desc += 'tether_masses['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in tether_masses[sind]])+', '\n",
" desc += 'grapple_masses['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in grapple_masses[sind]])+', '\n",
" desc += 'ballast_masses['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in ballast_masses[sind]])+', '\n",
" desc += 'tip_masses_empty['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in tip_masses_empty[sind]])+', '\n",
" desc += 'tip_masses_full['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in tip_masses_full[sind]])+', '\n",
" desc += 'tip_diams['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in tip_diams[sind]])+', '\n",
" desc += 'hub_diams['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in hub_diams[sind]])+', '\n",
" desc += 'moments_empty['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in moments_empty[sind]])+', '\n",
" desc += 'moments_full['+str(sind)+']= '+str([temp.n(digits=ndigits) for temp in moments_full[sind]])+'.\\n'\n",
" print desc\n",
" return\n",
"#debug_sling(2)\n",
"\n",
"def my_arccos(x,s):\n",
" \"\"\"\n",
" Input : Argument to arccos \"x\" and descriptive string \"s\".\\n\n",
" Output: arccos(x) if abs(x) < 1. If abs(x) is slightly >1, sets x= +1 or -1 silently. If not just \"slightly\" > 1, it's not silent.\n",
" \"\"\"\n",
" x = x.n() #Sometimes gives errors if not converted to numerical form.\n",
" if x-1 > 0:\n",
" theta = arccos(1.0)\n",
" if x - 1.0 > tolerance: print warnstring,'Input to',s,'arccos is > 1 by',x-1,', so',s,'arccos is being set to',theta\n",
" elif -x-1 > 0:\n",
" theta = arccos(-1.0)\n",
" if -x - 1.0 > tolerance: print warnstring,'Input to',s,'arccos is < -1 by',-x-1,', so',s,'arccos is being set to',theta\n",
" else: theta = arccos(x)\n",
" return theta\n",
"\n",
"################################################################\n",
"################################################################\n",
"################################################################\n",
"def xyz2kepler(position,velocity):\n",
" \"\"\"\n",
" Input : Position and velocity 3D vectors.\\n\n",
" Output: Vector of Keplerian orbital elements.\\n\n",
" Reference: (also see references in kepler2xyz)\\n\n",
" http://web.archive.org/web/20060914123625/http://ccar.colorado.edu/asen5070/handouts/cart2kep2002.pdf\n",
" \"\"\"\n",
" if get_verbose() > 0: print '-------------------------------------------------------'\n",
" position = position.n() #Sometimes gives errors if not converted to numerical form.\n",
" velocity = velocity.n() #These two numerical assignments were added after many \".n()\" assignment statements below failed to eliminate problems with too many terms in symbolic calculations. So those \".n()\" assignment statements below in this function (the ones commented as \"sometimes gives errors if it isn't converted to numerical form\", not the print statements using \".n()\") might not be necessary any more.\n",
" r = position.norm() #radial distance\n",
" v = velocity.norm() #speed\n",
" energy = v^2/2 - mu/r #Specific orbital energy\n",
" H = position.cross_product(velocity) #specific relative orbital angular momentum vector\n",
" h = H.norm() #magnitude of the specific relative orbital angular momentum vector.\n",
"\n",
" if abs(energy) > tolerance:\n",
" a = -mu/(2*energy) #Semi-major axis\n",
" if h^2/(a*mu) > 1.0:\n",
" ecc = 0.0 #Sometimes h^2/(a*mu) is larger than 1.0 by ~1e-16, which yields an imaginary \"ecc\". In that case, \"ecc\" should just be zero.\n",
" if h^2/(a*mu) - 1.0 > tolerance: print '!!!WARNING!!! 1-h^2/(a*mu) =',(1.0-h^2/(a*mu)).n(),', so ecc is being set to 0.'\n",
" else: ecc = sqrt(1.0 - h^2/(a*mu)) #eccentricity\n",
" else:\n",
" a = 0 #Semi-major axis isn't defined for parabolic radial trajectories, so set it to zero.\n",
" ecc = 1.0 #Parabolic trajectories have specific energy = 0 and eccentricity = 1.0.\n",
" end\n",
" ecc = ecc.n() #Comparisons involving \"ecc\" sometimes give errors if it isn't converted to numerical form.\n",
"\n",
" p = h^2/mu #Semi-latus rectum. https://en.wikipedia.org/wiki/Specific_relative_angular_momentum\n",
" p2 = a*(1-ecc^2) #Alternate derivation of semi-latus rectum, doesn't apply to parabolic trajectories.\n",
"\n",
" if h > tolerance: inc = my_arccos(H[2]/h,'inclination') #[2] is z-component.\n",
" else: inc = 0 #For radial trajectories, inclination is undefined, so set it to zero.\n",
"\n",
" if inc > tolerance and inc < pi - tolerance:\n",
" LAN = atan2(H[0],-H[1]) #Longitude of the ascending node. Sagemath expects arctan2(y,x).\n",
" else: LAN = 0 #Defined to be zero for non-inclined orbits.\n",
" LAN = restrict_rad(LAN) #Restricts angle to lie in [0,2*pi).\n",
" K = vector([0,0,1])\n",
" N = K.cross_product(H) #Node line vector.\n",
" n = N.norm()\n",
" #Longitude of the ascending node: https://en.wikipedia.org/wiki/Longitude_of_the_ascending_node\n",
" if inc > tolerance and inc < pi - tolerance:\n",
" LAN2 = my_arccos(N[0]/n,'LAN2') #[0] is x-component.\n",
" if N[1] < 0: LAN2 = 2*pi - LAN2 #[1] is y-component.\n",
" else: LAN2 = 0 #Defined to be zero for non-inclined orbits.\n",
" LAN2 = restrict_rad(LAN2) #Restricts angle to lie in [0,2*pi).\n",
"\n",
" if inc > tolerance and inc < pi - tolerance:\n",
" arg_lat = atan2(position[2]/sin(inc),position[0]*cos(LAN)+position[1]*sin(LAN)) #Argument of latitude.\n",
" else: arg_lat = 0 #Argument of latitude is undefined for non-inclined orbits, so just set it to zero.\n",
" arg_lat = arg_lat.n() #arg_lat sometimes gives errors if it isn't converted to numerical form.\n",
" arg_lat = restrict_rad(arg_lat) #Restricts angle to lie in [0,2*pi).\n",
" if ecc < tolerance: nu = 0 #True anomaly is undefined for circular orbits, so set it to zero.\n",
" elif abs(1-ecc) < tolerance: nu = my_arccos(p/r-1,'nu') #https://en.wikipedia.org/wiki/Parabolic_trajectory#Equation_of_motion\n",
" else: nu = my_arccos((a*(1-ecc^2)-r)/(ecc*r),'nu') #True anomaly- works for elliptical or hyperbolic.\n",
" vr = position.dot_product(velocity)/r #radial velocity\n",
" if vr<0: nu = 2*pi - nu\n",
" nu = restrict_rad(nu) #Restricts angle to lie in [0,2*pi).\n",
" if ecc > tolerance: nu2 = atan2(sqrt(p/mu)*position.dot_product(velocity),p-r) #Alternate derivation of true anomaly.\n",
" else: nu2 = 0 #True anomaly is undefined for circular orbits, so set it to zero.\n",
" nu2 = restrict_rad(nu2) #Restricts angle to lie in [0,2*pi).\n",
"\n",
" #Eccentricity vector: https://en.wikipedia.org/wiki/Eccentricity_vector\n",
" ev = velocity.cross_product(H)/mu - position/r\n",
" ecc2 = norm(ev)\n",
"\n",
" #Argument of periapsis: https://en.wikipedia.org/wiki/Argument_of_periapsis\n",
" if ecc < tolerance:\n",
" AP = 0 #Argument of periapsis is zero by definition for circular orbits.\n",
" AP2 = 0\n",
" elif inc.n() > tolerance and inc.n() < pi - tolerance: #Inclined elliptical orbits.\n",
" AP = arg_lat - nu\n",
" AP2 = my_arccos(N.dot_product(ev)/(n*ecc2),'AP2')\n",
" if ev[2].n() < 0: AP2 = 2*pi - AP2 #[2] is z-component.\n",
" else: #Non-inclined elliptical orbits have no ascending node, so use this:\n",
" AP = arctan2(ev[1],ev[0])\n",
" if H[2].n() < 0: AP = 2*pi - AP #Modify if the orbit is clockwise.\n",
" AP2 = AP #In this case, AP2 isn't an 'alternate' derivation.\n",
" end\n",
" AP = restrict_rad(AP) #Restricts angle to lie in [0,2*pi).\n",
" AP2 = restrict_rad(AP2) #Restricts angle to lie in [0,2*pi).\n",
" if AP.n() == NaN and AP2.n() != NaN:\n",
" if get_verbose() > 0: print 'Using AP2 because AP = NaN, but AP2 =',(AP2*180/pi).n()\n",
" AP = AP2\n",
" AP2 = NaN #Swap them so it's clear on output that the alternative is NaN\n",
"\n",
" #True longitude for non-inclined circular orbits: https://en.wikipedia.org/wiki/True_anomaly#Circular_orbit_with_zero_inclination\n",
" true_longitude = my_arccos(position[0]/r,'true_longitude')\n",
" if velocity[0] > 0: true_longitude = 2*pi - true_longitude\n",
" true_longitude = restrict_rad(true_longitude) #Restricts angle to lie in [0,2*pi).\n",
"\n",
" if h.n() > tolerance:\n",
" if energy < -tolerance or ecc < 1 - tolerance: #Elliptical and circular orbits definitely have energy<0, but some elliptical orbits have ecc=0.99999999..\n",
" semi_minor_axis = a*sqrt(1-ecc^2)\n",
" periapsis = a*(1-ecc) #http://www.bogan.ca/orbits/kepler/orbteqtn.html http://archive.is/zQPrQ\n",
" if ecc > tolerance:\n",
" if get_verbose() > 0: print 'Elliptical orbit: using true anomaly.'#' (degrees) =',(nu2*180/pi).n(),', Alt. calc. =',(nu*180/pi).n()\n",
" correct_arg = nu2\n",
" elif inc.n() > tolerance and inc.n() < pi - tolerance:\n",
" if get_verbose() > 0: print 'Circular, inclined orbit: using argument of latitude.'#' (degrees) =',(arg_lat*180/pi).n()\n",
" correct_arg = arg_lat\n",
" else:\n",
" if get_verbose() > 0: print 'Circular, non-inclined orbit: using true longitude.'#' (degrees) =',(true_longitude*180/pi).n()\n",
" correct_arg = true_longitude\n",
" end\n",
" EA = 2*arctan2(sqrt(1-ecc)*sin(correct_arg/2),sqrt(1+ecc)*cos(correct_arg/2))\n",
" EA = EA.n() #EA sometimes gives errors if it isn't converted to numerical form.\n",
" EA = restrict_rad(EA) #Restricts angle to lie in [0,2*pi).\n",
" MA = EA - ecc*sin(EA)\n",
" MA = restrict_rad(MA) #Restricts angle to lie in [0,2*pi).\n",
" tper = sqrt(a^3/mu)*MA\n",
" elif ecc > 1 + tolerance:\n",
" semi_minor_axis = a*sqrt(ecc^2-1)\n",
" periapsis = a*(1-ecc) #http://www.bogan.ca/orbits/kepler/orbteqtn.html http://archive.is/zQPrQ\n",
" if get_verbose() > 0: print 'Hyperbolic trajectory'\n",
" EA = 2*arctanh(sqrt((ecc-1)/(ecc+1))*tan(nu2/2))\n",
" MA = ecc*sinh(EA) - EA\n",
" tper = sqrt((-a)^3/mu)*MA\n",
" else:\n",
" periapsis = p/2 #http://www.bogan.ca/orbits/kepler/orbteqtn.html http://archive.is/zQPrQ\n",
" if get_verbose() > 0: print 'Parabolic trajectory'\n",
" EA = tan(nu2/2)\n",
" MA = EA + EA^3/3\n",
" tper = sqrt(2*(p/2)^3/mu)*MA\n",
" end\n",
" else: #if h < tolerance: https://en.wikipedia.org/wiki/Radial_trajectory\n",
" semi_minor_axis = 0\n",
" periapsis = 0\n",
" w = 1/r - v^2/(2*mu)\n",
" if energy < -tolerance: #w is often too small, so use specific energy to identify the type of radial trajectory.\n",
" if get_verbose() > 0: print 'Elliptical radial trajectory'\n",
" tper = ((arcsin(sqrt(w*r)) - sqrt(w*r*(1-w*r)))/(sqrt(2*mu)*w^(3/2)))\n",
" elif energy > tolerance:\n",
" if get_verbose() > 0: print 'Hyperbolic radial trajectory'\n",
" w = abs(w)\n",
" tper = (sqrt((w*r)^2+w*r)-ln(sqrt(w*r)+sqrt(1+w*r)))/(sqrt(2*mu)*w^(3/2))\n",
" else:\n",
" if get_verbose() > 0: print 'Parabolic radial trajectory'\n",
" tper = sqrt(2*(r)^3/(9*mu))\n",
" end\n",
" end\n",
"\n",
" orbital_period = (2*pi*sqrt(a^3/mu)).n() #Only applies to closed (non-radial) orbits.\n",
" apoapsis = a*(1+ecc) #Only useful for elliptical orbits.\n",
"\n",
" if r.n() > tolerance and v.n() > tolerance:\n",
" if h.n() > tolerance: fpa = my_arccos(h/(r*v),'fpa')\n",
" else: #Radial trajectories have simple flight path angles.\n",
" if vr.n() > 0: fpa = pi/2\n",
" else: fpa = -pi/2\n",
" end\n",
" elif r.n() > tolerance:\n",
" # https://en.wikipedia.org/wiki/Equations_for_a_falling_body#Examples\n",
" fallingtime = (arccos(sqrt(x/r)) + sqrt(x/r*(1-x/r)))*r^(3/2)/sqrt(2*mu)\n",
" time2surface = fallingtime.subs(x=r_pl) #Time to fall to the planet's (equatorial) surface.\n",
" time2center = fallingtime.subs(x=0) #Time to fall to the planet's center.\n",
" if get_verbose() > 0: print 'Dropped. Time to fall to surface (hours)'.ljust(lj),'=',(time2surface/3600).n()\n",
" if get_verbose() > 0: print 'Dropped. Time to fall to center (hours)'.ljust(lj),'=',(time2center/3600).n()\n",
" fpa = -pi/2 #If v=0, it will quickly start falling : fpa is -90 degrees.\n",
" elif v.n() > tolerance:\n",
" fpa = pi/2 #If r=0, any velocity will take it right out: fpa is +90 degrees.\n",
" else:\n",
" fpa = pi/2 #If r=0 and v=0, any velocity will take it right out: fpa is +90 degrees.\n",
" end\n",
"\n",
" #deflection = the angle between the periapsis velocity vector and the velocity vector at infinity. https://en.wikipedia.org/wiki/Hyperbolic_trajectory#Eccentricity_and_angle_between_approach_and_departure\n",
" if ecc > 1.0: deflection = my_arccos(-1/ecc,'deflection') - pi/2\n",
"\n",
" lj = 45 #Left justify descriptions by this number of characters.\n",
" if get_verbose() > 0:\n",
" print 'Position vector (km)'.ljust(lj),'=',(position/1000).n() #Echo input.\n",
" print 'Velocity vector (km/s)'.ljust(lj),'=',(velocity/1000).n() #Echo input.\n",
" print 'Radial distance (km)'.ljust(lj),'=',(r/1000).n()\n",
" print 'Speed (km/s)'.ljust(lj),'=',(v/1000).n()\n",
" print 'Radial velocity (km/s)'.ljust(lj),'=',(vr/1000).n()\n",
" print 'Flight path angle (degrees)'.ljust(lj),'=',(fpa*180/pi).n()\n",
" if energy < -tolerance and h > tolerance:\n",
" print 'Orbital period (hours)'.ljust(lj),'=',(orbital_period/3600).n()\n",
" print 'Specific relative angular momentum vector'.ljust(lj),'=',(H).n()\n",
" print 'Specific relative angular momentum (m^2/s)'.ljust(lj),'=',(h).n()\n",
" print 'Specific orbital energy (kJ/kg)'.ljust(lj),'=',(energy/1000).n()\n",
" if energy > tolerance:\n",
" print 'Hyperbolic excess velocity at infinity (km/s)'.ljust(lj),'=',(sqrt(2*energy)/1000).n(),', C3 (km/s)^2:',(2*energy/1e6).n()\n",
" if h > tolerance: print 'Periapsis vs infinity deflection (degrees)'.ljust(lj),'=',(deflection*180/pi).n()\n",
" print 'Eccentricity vector'.ljust(lj),'=',(ev).n()\n",
" print 'Eccentricity'.ljust(lj),'=',(ecc).n(),', Alt. calc. =',(ecc2).n()\n",
" print 'Semi-latus rectum (km)'.ljust(lj),'=',(p/1000).n(),', Alt. calc. =',(p2/1000).n()\n",
" if h > tolerance and abs(energy) > tolerance and abs(ecc-1) > tolerance: #Skip these for radial or parabolic trajectories, because in those cases they're not defined.\n",
" print 'Semi-minor axis (km)'.ljust(lj),'=',(semi_minor_axis/1000).n()\n",
" print 'Semi-major axis (km)'.ljust(lj),'=',(a/1000).n()\n",
" if periapsis > r_pl:\n",
" print 'Periapsis altitude (km)'.ljust(lj),'=',((periapsis - r_pl)/1000).n()\n",
" else:\n",
" print '!!DANGER!! Periapsis is below surface by (km)'.ljust(lj),'=',(-(periapsis - r_pl)/1000).n()\n",
" print 'Periapsis (km)'.ljust(lj),'=',(periapsis/1000).n()\n",
" if ecc < 1 - tolerance: #Skip apoapsis for parabolic or hyperbolic trajectories, because in those cases it's not defined.\n",
" print 'Apoapsis altitude (km)'.ljust(lj),'=',((apoapsis - r_pl)/1000).n()\n",
" print 'Apoapsis (km)'.ljust(lj),'=',(apoapsis/1000).n()\n",
" if h > tolerance: #Skip these for radial trajectories, because in those cases they're not defined.\n",
" print 'Inclination (degrees)'.ljust(lj),'=',(inc*180/pi).n()\n",
" print 'Longitude of the ascending node (degrees)'.ljust(lj),'=',(LAN2*180/pi).n(),', Alt. calc. =',(LAN*180/pi).n()\n",
" print 'Argument of periapsis (degrees)'.ljust(lj),'=',(AP*180/pi).n(),', Alt. calc. =',(AP2*180/pi).n()\n",
" print 'Mean anomaly (degrees)'.ljust(lj),'=',(MA*180/pi).n()\n",
" print 'Eccentric anomaly (degrees)'.ljust(lj),'=',(EA*180/pi).n()\n",
" if ecc > tolerance:\n",
" print 'True anomaly (degrees)'.ljust(lj),'=',(nu2*180/pi).n(),', Alt. calc. =',(nu*180/pi).n()\n",
" elif inc > tolerance and inc < pi - tolerance:\n",
" print 'Argument of latitude (degrees)'.ljust(lj),'=',(arg_lat*180/pi).n()\n",
" else:\n",
" print 'True longitude (degrees)'.ljust(lj),'=',(true_longitude*180/pi).n()\n",
" end\n",
" print 'Time since periapsis (hours)'.ljust(lj),'=',(tper/3600).n()\n",
" print 'returns [a, ecc, inc, AP, LAN2, tper, p]'\n",
" kepler = vector([a, ecc, inc, AP, LAN2, tper, p])\n",
" return kepler\n",
"\n",
"################################################################\n",
"################################################################\n",
"################################################################\n",
"def rotateY(rad):\n",
" \"\"\"\n",
" Input : Angle in radians.\\n\n",
" Output: 3D rotation matrix about Z axis by 'rad' radians.\\n\n",
" Reference:\\n\n",
" https://en.wikipedia.org/wiki/Rotation_matrix#Basic_rotations\n",
" \"\"\"\n",
" return matrix([[cos(rad),0,sin(rad)],[0,1,0],[-sin(rad),0,cos(rad)]])\n",
"\n",
"def rotateZ(rad):\n",
" \"\"\"\n",
" Input : Angle in radians.\\n\n",
" Output: 3D rotation matrix about Z axis by 'rad' radians.\\n\n",
" Reference:\\n\n",
" https://en.wikipedia.org/wiki/Rotation_matrix#Basic_rotations\n",
" \"\"\"\n",
" return matrix([[cos(rad),-sin(rad),0],[sin(rad),cos(rad),0],[0,0,1]])\n",
"\n",
"def rotateV(unit,rad):\n",
" \"\"\"\n",
" Input : 3D unit vector, angle in radians.\\n\n",
" Output: 3D rotation matrix about unit vector by \"rad\" radians, using Rodrigues' rotation formula.\\n\n",
" References:\\n\n",
" https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula#Matrix_notation \\n\n",
" http://mathworld.wolfram.com/RodriguesRotationFormula.html \\n\n",
" https://math.stackexchange.com/questions/142821/matrix-for-rotation-around-a-vector\n",
" \"\"\"\n",
" unit /= unit.norm() #Unit vector is divided by its norm just in case it's not already a unit vector.\n",
" #unit = vector([1,2,3]) #To check that curlish is written correctly. Can't use the actual 0,1,2 indices because then \"0\" would be ambiguous.\n",
" curlish = matrix([[0,-unit[2],unit[1]],[unit[2],0,-unit[0]],[-unit[1],unit[0],0]]) #Wolfram notes that \"the entries in this matrix are defined analogously to the differential matrix representation of the curl operator.\"\n",
" #print 'curlish =\\n',curlish\n",
" return matrix.identity(3) + sin(rad)*curlish + 2*sin(rad/2)^2*curlish^2 #This version with the \"2*sin(rad/2)^2\" coefficient is mathematically equivalent (via a half-angle trig identity) to the conventional version but as \"J. M. is not a mathematician\" notes at math.stackexchange.com, this version is more numerically sound because it's less prone to subtractive cancellations than the conventional \"1-cos(rad)\" coefficient when the angle \"rad\" is small.\n",
"\n",
"################################################################\n",
"################################################################\n",
"################################################################\n",
"def kepler2xyz(kepler):\n",
" \"\"\"\n",
" Input : Keplerian orbital elements.\\n\n",
" Output: Position and velocity 3D vectors.\\n\n",
" References:\\n\n",
" http://web.archive.org/web/20050328065332/http://ccar.colorado.edu/asen5070/handouts/kep2cart_2002.doc \\n\n",
" https://downloads.rene-schwarz.com/download/M001-Keplerian_Orbit_Elements_to_Cartesian_State_Vectors.pdf \\n\n",
" Generalized to hyperbolas: http://web.archive.org/web/20180613173542/http://astro.pas.rochester.edu/~aquillen/aqnbody/kepcart.cpp \\n\n",
" http://web.archive.org/web/20180613174357/https://space.stackexchange.com/questions/24646/finding-x-y-z-vx-vy-vz-from-hyperbolic-orbital-elements \\n\n",
" http://web.archive.org/web/20171209014117/http://web.mit.edu/8.01t/www/materials/modules/chapter25.pdf \\n\n",
" http://web.archive.org/web/20161020194753/http://astrowww.phys.uvic.ca/~tatum/celmechs/celm9.pdf \\n\n",
" http://web.archive.org/web/20050527081541/http://www.bruce-shapiro.com:80/pair/ElementConversionRecipes.pdf \\n\n",
" http://web.archive.org/web/20180811085042/http://shodhganga.inflibnet.ac.in/bitstream/10603/41384/10/10_chapter%202.pdf \\n\n",
" http://web.archive.org/web/20171215024400/http://dma.ing.uniroma1.it/users/lss_mo/MATERIALE/AvanziniColasurdoAstrodynamics.pdf \\n\n",
" Sort of helpful: http://web.archive.org/web/20180613174224/https://space.stackexchange.com/questions/23128/design-of-an-elliptical-transfer-orbit/23130 \\n\n",
" http://web.archive.org/web/20180613174829/https://space.stackexchange.com/questions/19322/converting-orbital-elements-to-cartesian-state-vectors \\n\n",
" http://web.archive.org/web/20150911070910/https://physics.stackexchange.com/questions/86188/calculating-orbital-vectors-in-the-future \\n\n",
" Related: Tokis 2014, A Solution of Kepler’s Equation: http://web.archive.org/web/20170923055710/https://file.scirp.org/pdf/IJAA_2014123013365071.pdf\n",
" \"\"\"\n",
" if get_verbose() > 0: print '-------------------------------------------------------'\n",
" kepler = kepler.n() #Sometimes gives errors if not converted to numerical form.\n",
" a = kepler[0] #semi-major axis\n",
" ecc = kepler[1] #eccentricity\n",
" inc = kepler[2] #inclination\n",
" AP = kepler[3] #argument of periapsis\n",
" LAN = kepler[4] #longitude of ascending node\n",
" tper = kepler[5] #time from periapsis\n",
" p = kepler[6] #Adding semi-latus rectum because \"a\" is undefined for parabolic trajectories.\n",
" p2 = a*(1-ecc^2) #Use the other elements to calculate the semi-latus rectum.\n",
" h = sqrt(mu*p)\n",
"\n",
" if ecc < 1 - tolerance: #Circular or elliptical orbit.\n",
" if get_verbose() > 0:\n",
" if ecc < tolerance: print 'Circular orbit'\n",
" else: print 'Elliptical orbit'\n",
" MA = sqrt(mu/a^3)*tper\n",
" MA = restrict_rad(MA) #Restricts angle to lie in [0,2*pi).\n",
" var('EAvar')\n",
" anomalies = EAvar - ecc*sin(EAvar) - MA #http://www.bogan.ca/orbits/kepler/orbteqtn.html http://archive.is/zQPrQ\n",
" EA = find_root(anomalies,-tolerance,2*pi)\n",
" EA = restrict_rad(EA) #Restricts angle to lie in [0,2*pi).\n",
" cosEA = cos(EA) ; sinEA = sin(EA)\n",
" nu = 2*arctan2(sqrt(1+ecc)*sin(EA/2),sqrt(1-ecc)*cos(EA/2))\n",
" elif ecc > 1 + tolerance:\n",
" if get_verbose() > 0: print 'Hyperbolic trajectory'\n",
" MA = sqrt(mu/(-a)^3)*tper\n",
" var('EAvar')\n",
" anomalies = ecc*sinh(EAvar) - EAvar - MA #http://www.bogan.ca/orbits/kepler/orbteqtn.html http://archive.is/zQPrQ\n",
" elimit = max([1000.0,1000.0*MA]) #This control the limits for find_root. Those limits weren't rigorously derived, of course, but perhaps they'll work for most cases?\n",
" #display(plot(anomalies,(EAvar,-elimit,elimit),axes_labels=['EAvar','anomalies']))\n",
" EA = find_root(anomalies,-elimit,elimit)\n",
" cosEA = cosh(EA) ; sinEA = sinh(EA) #Use hyperbolic trig for hyperbolas.\n",
" nu = 2*arctan2(sqrt(ecc+1)*sinh(EA/2),sqrt(ecc-1)*cosh(EA/2)) #https://en.wikipedia.org/wiki/Hyperbolic_trajectory\n",
" else:\n",
" if get_verbose() > 0: print 'Parabolic trajectory'\n",
" MA = sqrt(mu/(2*(p/2)^3))*tper\n",
" var('EAvar')\n",
" eqp = MA == EAvar + EAvar^3/3\n",
" solnp = solve(eqp,EAvar)\n",
" EA = solnp[2].rhs().n() #The first two solutions are imaginary, so use the third solution.\n",
" cosEA = cos(EA) ; sinEA = sin(EA)\n",
" nu = 2*arctan(EA) #http://www.bogan.ca/orbits/kepler/orbteqtn.html http://archive.is/zQPrQ\n",
" end\n",
"\n",
" r1 = a*(1-ecc*cosEA)\n",
" r2 = p/(1+ecc*cos(nu)) #compare various ways of calculating radius.\n",
" r3 = a*(1-ecc^2)/(1+ecc*cos(nu))\n",
" r = r2 #r2 should be more general than the other equations.\n",
" \n",
" #These equations for cartesian vectors were copied from http://web.archive.org/web/20050328065332/http://ccar.colorado.edu/asen5070/handouts/kep2cart_2002.doc\n",
" #They seem to work for elliptical and hyperbolic cases. Even parabolic trajectories seem to give vectors that are very similar to those returned by a \"just barely hyperbolic\" trajectory.\n",
" x = r*(cos(LAN)*cos(AP+nu) - sin(LAN)*sin(AP+nu)*cos(inc))\n",
" y = r*(sin(LAN)*cos(AP+nu) + cos(LAN)*sin(AP+nu)*cos(inc))\n",
" z = r*(sin(inc)*sin(AP+nu))\n",
"\n",
" vx = (x*h*ecc/(r*p))*sin(nu) - (h/r)*(cos(LAN)*sin(AP+nu) + sin(LAN)*cos(AP+nu)*cos(inc))\n",
" vy = (y*h*ecc/(r*p))*sin(nu) - (h/r)*(sin(LAN)*sin(AP+nu) - cos(LAN)*cos(AP+nu)*cos(inc))\n",
" vz = (z*h*ecc/(r*p))*sin(nu) + (h/r)*(cos(AP+nu)*sin(inc))\n",
"\n",
" position = vector([x,y,z])\n",
" velocity = vector([vx,vy,vz])\n",
"\n",
" #Alternate rotation matrix approach from https://downloads.rene-schwarz.com/download/M001-Keplerian_Orbit_Elements_to_Cartesian_State_Vectors.pdf\n",
" #position2 = vector([r*cos(nu),r*sin(nu),0])\n",
" #This doesn't apply to parabolic trajectories because it uses \"a\":\n",
" #velocity2 = vector([sqrt(mu*abs(a))/r*(-sinEA),sqrt(mu*abs(a))/r*(cosEA*sqrt(abs(1-ecc^2))),0]) #Use abs() because of hyperbolas.\n",
" #rotation_matrix = rotateZ(LAN)*rotateX(inc)*rotateZ(AP) #The negative signs in Rene's formalism give incorrect positions here.\n",
" #position2 = rotation_matrix*position2\n",
" #velocity2 = rotation_matrix*velocity2\n",
" #position = position2\n",
" #velocity = velocity2\n",
"\n",
" if get_verbose() > 0:\n",
" r4 = position.norm() #radial distance - check that it's identical to the above calculations.\n",
" v = velocity.norm() #speed\n",
" vr = position.dot_product(velocity)/r #radial velocity\n",
" print 'Position vector (km) =',(position/1000).n()\n",
" print 'Velocity vector (km/s) =',(velocity/1000).n()\n",
" #print 'Position2vector (km) =',(position2/1000).n()\n",
" #print 'Velocity2vector (km/s) =',(velocity2/1000).n()\n",
" print 'Radial distance (km) =',(r2/1000).n(),', Alt. calcs. =',(r1/1000).n(),', ',(r3/1000).n(),', ',(r4/1000).n()\n",
" print 'Speed (km/s) =',(v/1000).n()\n",
" print 'Radial velocity (km/s) =',(vr/1000).n()\n",
" #print 'returns position, velocity'\n",
" return position, velocity\n",
"\n",
"################################################################\n",
"################################################################\n",
"################################################################\n",
"def xyz2spherical(v):\n",
" \"\"\"\n",
" Input : A 3D vector: x, y, z.\\n\n",
" Output: A 3D vector: \"r\" magnitude, \"theta\" inclination (rad), \"phi\" azimuth (rad).\\n\n",
" Reference:\\n\n",
" https://en.wikipedia.org/wiki/Spherical_coordinate_system\n",
" \"\"\"\n",
" r = v.norm()\n",
" if r > tolerance:\n",
" theta = my_arccos(v[2]/r,'xyz2spherical')\n",
" phi = arctan2(v[1],v[0])\n",
" else:\n",
" theta = 0 #Arbitrarily set inclination and azimuth to 0 if r < tolerance.\n",
" phi = 0\n",
" end\n",
" return vector([r,theta,phi])\n",
"\n",
"def spherical2xyz(v):\n",
" \"\"\"\n",
" Input : A 3D vector: magnitude, inclination (rad), azimuth (rad).\\n\n",
" Output: A 3D vector: x, y, z.\\n\n",
" Reference:\\n\n",
" https://en.wikipedia.org/wiki/Spherical_coordinate_system\n",
" \"\"\"\n",
" x = v[0]*sin(v[1])*cos(v[2])\n",
" y = v[0]*sin(v[1])*sin(v[2])\n",
" z = v[0]*cos(v[1])\n",
" return vector([x,y,z])\n",
"\n",
"def spherical2deg(v):\n",
" \"\"\"\n",
" Input : A 3D vector: magnitude, inclination (rad), azimuth (rad).\\n\n",
" Output: A 3D vector: magnitude, inclination (deg), azimuth (deg).\\n\n",
" \"\"\"\n",
" v[1] = v[1]*180/pi ; v[2] = v[2]*180/pi\n",
" return v\n",
"\n",
"def compare_vectors(v1,v2,*args):\n",
" \"\"\"\n",
" Input : Two 3D vectors: x, y, z. Then an optional numeric flag.\\n\n",
" Output: Difference of those vectors, and print statements.\\n\n",
" If numeric flag is either 1 or absent, numeric values are printed.\\n\n",
" If numeric flag is 0, symbolic values are printed.\n",
" \"\"\"\n",
" print '-------------------------------------------------------'\n",
" if args: numeric_flag = args[0]\n",
" else: numeric_flag = 1\n",
" if numeric_flag != 0:\n",
" v1 = v1.n() #Sometimes gives errors if not converted to numerical form.\n",
" v2 = v2.n()\n",
" n1 = v1.norm()\n",
" n2 = v2.norm()\n",
" dv = v2 - v1\n",
" dvnorm = dv.norm()\n",
" s1 = xyz2spherical(v1)\n",
" s2 = xyz2spherical(v2)\n",
" sdv = xyz2spherical(dv)\n",
" if dvnorm > n1*tolerance or abs(s1[1] - s2[1]) > tolerance or abs(s1[2] - s2[2]) > tolerance or numeric_flag == 0:\n",
" if numeric_flag != 0:\n",
" print '#1 =',v1.n()\n",
" print '#2 =',v2.n()\n",
" print 'delta =',dv.n()\n",
" print '#1 magnitude, inclination, azimuth (deg) =',spherical2deg(s1).n()\n",
" print '#2 magnitude, inclination, azimuth (deg) =',spherical2deg(s2).n()\n",
" print 'delta magnitude, inclination, azimuth (deg) =',spherical2deg(sdv).n()\n",
" else:\n",
" print '#1 =',v1\n",
" print '#2 =',v2\n",
" print 'delta =',dv\n",
" print '#1 magnitude, inclination, azimuth (deg) =',spherical2deg(s1)\n",
" print '#2 magnitude, inclination, azimuth (deg) =',spherical2deg(s2)\n",
" print 'delta magnitude, inclination, azimuth (deg) =',spherical2deg(sdv)\n",
" if n1 > tolerance and n2 > tolerance:\n",
" angle = my_arccos((v1.dot_product(v2))/(n1*n2),'compare_vectors angle') #From the definition of the dot product.\n",
" if numeric_flag != 0: print 'angle between vectors #1 and #2 (degrees) =',(angle*180/pi).n()\n",
" else: print 'angle between vectors #1 and #2 (degrees) =',(angle*180/pi)\n",
" cross_prod = v1.cross_product(v2)\n",
" cross_prod /= cross_prod.norm()\n",
" if numeric_flag != 0: print 'Normalized cross product =',(cross_prod).n()\n",
" else: print 'Normalized cross product =',(cross_prod).n()\n",
" else: print 'Vectors are the same, within tolerance.'\n",
" return dv\n",
"\n",
"print 'Capture sling orbit:'\n",
"set_verbose(2)\n",
"kepler_com_cs = xyz2kepler(com_cs_pos0,com_cs_vel0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 6. Solve for each capture sling's radius, rotation rate, counterbalance ratio, etc. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"-----------------------------------------------------------------------------------\n",
"Solving for capture sling A's radius, rotation rate, counterbalance ratio, etc.\n"
]
},
{
"data": {
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\n",
"text/plain": [
"Graphics object consisting of 3 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"capture sling A radius1 (km) : 99.1859671034564\n",
"capture sling A omega (rad/s) : 0.00994510533955852\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 3 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"periapsis altitude at target cbr (km) : 375.7945714745382\n",
"apoapsis altitude at target cbr (km) : 424.518111025446\n",
"orbital period of counterbalance (hours) : 1.97242682475854\n",
"payload arm length (km) : 99.1859671034564\n",
"balance arm length (km) : 99.9492691988\n",
"velocity at payload tip (m/s) : 986.414891049860\n",
"velocity at balance tip (m/s) : 994.006010794\n",
"acceleration at payload tip (g's) : 1.00000000000000\n",
"acceleration at balance tip (g's) : 1.00769566621\n",
"payload arm could reach down to 425.281413120804 km above the surface of Mars\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 3 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"cbrs[1]= 0.992, radii[1]= 99200., omegas[1]= 0.00994, throw_times[1]= 0.000, theta0s[1]= 0.000, coms_empty[1]= -1.00, coms_full[1]= -1.00, junopanel_masses[1]= -1.00, spinup_times[1]= -1.00, spindown_times[1]= -1.00, wait_times[1]= -1.00, avg_powers[1]= -1.00, avg_powers_alt[1]= -1.00, avg_torques[1]= -1.00, keplers[1]= [-1.00, -1.00], throw_tpers[1]= [0.000, 0.000], throw_pos[1]= [(0.000, 0.000, 0.000), (0.000, 0.000, 0.000)], throw_vel[1]= [(0.000, 0.000, 0.000), (0.000, 0.000, 0.000)], payload_masses[1]= [-1.00, -1.00], v_cs[1]= [-1.00, -1.00], tether_masses[1]= [-1.00, -1.00], grapple_masses[1]= [-1.00, -1.00], ballast_masses[1]= [-1.00, -1.00], tip_masses_empty[1]= [-1.00, -1.00], tip_masses_full[1]= [-1.00, -1.00], tip_diams[1]= [-1.00, -1.00], hub_diams[1]= [-1.00, -1.00], moments_empty[1]= [-1.00, -1.00], moments_full[1]= [-1.00, -1.00].\n",
"\n",
"\n",
"-----------------------------------------------------------------------------------\n",
"Solving for capture sling B's radius, rotation rate, counterbalance ratio, etc.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 3 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"capture sling B radius1 (km) : 124.000561331188\n",
"capture sling B omega (rad/s) : 0.00889452332022582\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 3 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Can't send counterbalance B directly into an orbit with the same semi-latus rectum as the target orbit because then its periapsis altitude would be 173.01636602082382 km.\n",
"\n",
"capture sling B counterbalance ratio was derived : 0.917526189989\n",
"periapsis altitude at target cbr (km) : 109.99999999999953\n",
"apoapsis altitude at target cbr (km) : 659.613998036490\n",
"orbital period of counterbalance (hours) : 1.97242682475854\n",
"payload arm length (km) : 124.000561331188\n",
"balance arm length (km) : 135.146617812\n",
"velocity at payload tip (m/s) : 1102.92588448134\n",
"velocity at balance tip (m/s) : 1202.06474378\n",
"acceleration at payload tip (g's) : 1.00000000000000\n",
"acceleration at balance tip (g's) : 1.08988714536\n",
"payload arm could reach down to 400.466818893073 km above the surface of Mars\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 3 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"cbrs[2]= 0.918, radii[2]= 124000., omegas[2]= 0.00889, throw_times[2]= 0.000, theta0s[2]= 0.000, coms_empty[2]= -1.00, coms_full[2]= -1.00, junopanel_masses[2]= -1.00, spinup_times[2]= -1.00, spindown_times[2]= -1.00, wait_times[2]= -1.00, avg_powers[2]= -1.00, avg_powers_alt[2]= -1.00, avg_torques[2]= -1.00, keplers[2]= [-1.00, -1.00], throw_tpers[2]= [0.000, 0.000], throw_pos[2]= [(0.000, 0.000, 0.000), (0.000, 0.000, 0.000)], throw_vel[2]= [(0.000, 0.000, 0.000), (0.000, 0.000, 0.000)], payload_masses[2]= [-1.00, -1.00], v_cs[2]= [-1.00, -1.00], tether_masses[2]= [-1.00, -1.00], grapple_masses[2]= [-1.00, -1.00], ballast_masses[2]= [-1.00, -1.00], tip_masses_empty[2]= [-1.00, -1.00], tip_masses_full[2]= [-1.00, -1.00], tip_diams[2]= [-1.00, -1.00], hub_diams[2]= [-1.00, -1.00], moments_empty[2]= [-1.00, -1.00], moments_full[2]= [-1.00, -1.00].\n",
"\n",
"COPLANAR SETUP. Capture slings release payloads A and B at r = periapsis_capture +/- sling A/B radius. Counterbalance A has a periapsis altitude of 375.79 km and the same semi-latus rectum as the specified counterbalance orbit. Counterbalance B aerobrakes at a periapsis altitude of 110.00 km.\n"
]
}
],
"source": [
"if abs(cbrs[1]) < tolerance: print 'Skipping this section because the capture sling has been disabled.'\n",
"elif trajopt < 100: print 'Skipping this section because it only applies to coplanar trajectory options.'\n",
"else:\n",
" lj = 50 ; summary_digits = 5 #Many print statements in this notebook will print \"summary_digits\" significant figures.\n",
" traj_string = orig_traj_string #Otherwise running this cell repeatedly keeps adding the same counterbalance notes to traj_string.\n",
" for j in range(1,3): #Loop through capture slings A and B (j = 1 and 2).\n",
" print '\\n-----------------------------------------------------------------------------------'\n",
" print 'Solving for '+descs[j]+'\\'s radius, rotation rate, counterbalance ratio, etc.'\n",
" #Capture sling A/B releases the payload at the farthest/nearest point to Mars, so r = periapsis_capture +/- capture sling radius \"c_rad\".\n",
" someneg = (-1)^(j-1) #This term is sometimes negative (when j=2), which allows the same equation to be used for slings A and B.\n",
" throw_times[j] = 0.0 #Sling \"j\" throws its masses exactly when the capture sling center of mass reaches periapsis.\n",
" theta0s [j] = 0.0 #Sling \"j\" throws its masses exactly along the capture sling center of mass velocity vector.\n",
" throw_tpers[j][0] = 0 #\"Time since periapsis\" is currently always zero for both payloads and both counterbalances.\n",
" throw_tpers[j][1] = 0\n",
"\n",
" var('c_rad w cbr r_p')\n",
" assume(c_rad>0,w>0,cbr>0,r_p>0)\n",
"\n",
" #energy = v^2/2 - mu/r #Specific orbital energy\n",
" #energy = (capture_speed_periapsis+c_rad*w)^2/2 - mu/(periapsis_capture+someneg*c_rad) #Specific orbital energy\n",
" #tip_accel_max = c_rad*w^2 #max accel >= v^2/r = r*w^2\n",
" #w = sqrt(tip_accel_max/c_rad)\n",
" energy_max_accel = (capture_speed_periapsis+c_rad*sqrt(tip_accel_max/c_rad))^2/2 - mu/(periapsis_capture+someneg*c_rad) #Specific orbital energy\n",
"\n",
" pt1 = plot( (energy_max_accel)/1000.0, c_rad, 0, r_max, rgbcolor='red', linestyle = '-', fill=False, thickness=1, legend_label = 'specific orbital energy (kJ/kg)' )\n",
" pt2 = plot( 0.5*v_infs[mis[j]]^2/1000.0, c_rad, 0, r_max, rgbcolor='green', linestyle = '-.', fill=False, thickness=1, legend_label = 'target specific energy (kJ/kg)' )\n",
" #display(plot(0.5*v_infs[mis[j]]^2 - energy_max_accel,(c_rad,0,r_max),axes_labels=['c_rad','energy differences']))\n",
" target_c_rad = find_root(0.5*v_infs[mis[j]]^2 - energy_max_accel,0,r_max)\n",
" #desc = descs[j]+' radius1 target_c_rad (km)' ; print desc.ljust(lj),':',target_c_rad/1000.0\n",
" pt3 = line([(target_c_rad,0),(target_c_rad,0.5*v_infs[mis[j]]^2/1000.0)])\n",
" if textonly == 0: show( pt1 + pt2 + pt3, axes=true, frame=True, gridlines=false, figsize=6, xmin=0, xmax=r_max, ymin=0, ymax=0.52*v_infs[mis[j]]^2/1000.0 ,axes_labels=[descs[j]+' sling radius at max accel','specific orbital energy (kJ/kg)'])\n",
" else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'\n",
" c_rad = target_c_rad\n",
" w = sqrt(tip_accel_max/c_rad)\n",
"\n",
" if c_rad > r_max: print warnstring,descs[j],'radius1',c_rad/1000.0,'km is larger than maximum sling radius',r_max/1000.0,'km.'\n",
" else: desc = descs[j]+' radius1 (km)' ; print desc.ljust(lj),':',(c_rad/1000.0).n()\n",
" desc = descs[j]+' omega (rad/s)' ; print desc.ljust(lj),':',w.n()\n",
"\n",
" #counterbalance orbit has apoapsis at r-someneg*c_rad/cbr, periapsis needs to be above r_pl + atmo_alt OR it needs to aerobrake.\n",
" #v^2 = mu*(2/r-1/a) #v at apoapsis\n",
" #v^2 = mu*(2/(a*(1+ecc))-1/a) = mu/a*(2/(1+ecc) - 1) = mu/a*((1-ecc)/(1+ecc)) = mu/a*(r_p/r_a) = 2*mu*(r_p/r_a)/(r_p+r_a) #v at apoapsis\n",
" eq_cbr3 = (capture_speed_periapsis-c_rad/cbr*w)^2 == 2*mu*(r_p/(periapsis_capture-someneg*c_rad/cbr))/(r_p+(periapsis_capture-someneg*c_rad/cbr)) #v at apoapsis\n",
" soln_cbr3 = solve(eq_cbr3,r_p)\n",
" perCB1(cbr) = soln_cbr3[0].rhs()\n",
" #print 'perCB1 =',perCB1\n",
" apoCB1(cbr) = periapsis_capture-someneg*c_rad/cbr\n",
" if textonly == 0: plot((perCB1-r_pl)/1000.0,(cbr,1/dmr,dmr),axes_labels=[descs[j]+' counterbalance ratio','balance periapsis altitude (km)'])\n",
" else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'\n",
"\n",
" pt1 = plot( (perCB1-r_pl)/1000.0, cbr, 1/dmr, dmr, rgbcolor='red', linestyle = '-', fill=False, thickness=1, legend_label = 'periapsis altitude' )\n",
" pt2 = plot( (apoCB1-r_pl)/1000.0, cbr, 1/dmr, dmr, rgbcolor='blue', linestyle = '--', fill=False, thickness=1, legend_label = 'apoapsis altitude' )\n",
" pt3 = plot( (target_alt)/1000.0, cbr, 1/dmr, dmr, rgbcolor='green', linestyle = '-.', fill=False, thickness=1, legend_label = 'target altitude' )\n",
" if textonly == 0: show( pt1 + pt2 + pt3, axes=true, frame=True, gridlines=false, figsize=6, xmin=1/dmr, xmax=dmr, ymin=0, ymax=(periapsis_capture - r_pl)/1000.0,axes_labels=[descs[j]+' cbr','balance apsis altitudes (km)'] )\n",
" else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'\n",
" slr = (0.5*(apoCB1+perCB1)*(1-((apoCB1-perCB1)/(apoCB1+perCB1))^2))\n",
" pt1 = plot( slr/1000.0, cbr, 1/dmr, dmr, rgbcolor='red', linestyle = '-', fill=False, thickness=1, legend_label ='semi-latus rectum' )\n",
" pt2 = plot( (target_alt+r_pl)/1000.0, cbr, 1/dmr, dmr, rgbcolor='green', linestyle = '-.', fill=False, thickness=1, legend_label ='target semi-latus rectum' )\n",
" #display(plot(slr - (target_alt+r_pl),(cbr,1/dmr,dmr),axes_labels=[descs[j]+' cbr','semi-latus rectum differences']))\n",
" target_cbr_cs = RDF(find_root(slr - (target_alt+r_pl),1/dmr,dmr))\n",
" if orig_cbrs[j] > tolerance:\n",
" target_cbr_cs = orig_cbrs[j]\n",
" desc = descs[j]+' counterbalance ratio was specified' ; print desc.ljust(lj),':',target_cbr_cs\n",
" traj_string += ' Counterbalance '+sdescs[j]+' was randomly sent to a periapsis altitude of '+str(((perCB1(target_cbr_cs) - r_pl)/1000.0).n(digits=summary_digits))+' km.'\n",
" elif (perCB1(target_cbr_cs) - r_pl) < atmo_alt:\n",
" print 'Can\\'t send counterbalance',sdescs[j],'directly into an orbit with the same semi-latus rectum as the target orbit because then its periapsis altitude would be',(perCB1(target_cbr_cs) - r_pl)/1000.0,'km.\\n'\n",
" target_cbr_cs = RDF(find_root(perCB1(cbr) - (r_pl+aerobrake_alt),1/dmr,dmr))\n",
" traj_string += ' Counterbalance '+sdescs[j]+' aerobrakes at a periapsis altitude of '+str(((perCB1(target_cbr_cs) - r_pl)/1000.0).n(digits=summary_digits))+' km.' #'Counterbalance A either uses aerobraking or goes directly into an orbit with a periapsis above atmosphere and the same semi-latus rectum as the specified counterbalance orbit. Counterbalance B either uses a tether to help lift a payload from a suborbital trajectory, aerobrakes until it can enter the specified counterbalance orbit, or aerobrakes until it hits Mars.'\n",
" desc = descs[j]+' counterbalance ratio was derived' ; print desc.ljust(lj),':',target_cbr_cs\n",
" else:\n",
" traj_string += ' Counterbalance '+sdescs[j]+' has a periapsis altitude of '+str(((perCB1(target_cbr_cs) - r_pl)/1000.0).n(digits=summary_digits))+' km and the same semi-latus rectum as the specified counterbalance orbit.'\n",
" pt3 = line([(target_cbr_cs,0),(target_cbr_cs,(target_alt+r_pl)*1.1/1000.0)])\n",
" print 'periapsis altitude at target cbr (km)'.ljust(lj),':',(perCB1(target_cbr_cs) - r_pl)/1000.0\n",
" print 'apoapsis altitude at target cbr (km)'.ljust(lj),':',(apoCB1(target_cbr_cs) - r_pl)/1000.0\n",
" if apoCB1(target_cbr_cs) < perCB1(target_cbr_cs): print warnstring+'Periapsis is above apoapsis, so counterbalance',sdescs[j],'goes UP!'\n",
" print 'orbital period of counterbalance (hours)'.ljust(lj),':',(2*pi*sqrt((target_alt+r_pl)^3/mu)/3600).n()\n",
" print 'payload arm length (km)'.ljust(lj),':',c_rad/1000.0\n",
" print 'balance arm length (km)'.ljust(lj),':',c_rad/target_cbr_cs/1000.0\n",
" print 'velocity at payload tip (m/s)'.ljust(lj),':',c_rad*w\n",
" print 'velocity at balance tip (m/s)'.ljust(lj),':',c_rad/target_cbr_cs*w\n",
" print 'acceleration at payload tip (g\\'s)'.ljust(lj),':',c_rad*w^2/g\n",
" print 'acceleration at balance tip (g\\'s)'.ljust(lj),':',c_rad/target_cbr_cs*w^2/g\n",
" print 'payload arm could reach down to',(periapsis_capture-c_rad-r_pl)/1000.0,'km above the surface of',pname\n",
" if textonly == 0: show( pt1 + pt2 + pt3, axes=true, frame=True, gridlines=false, figsize=6, xmin=1/dmr, xmax=dmr, ymin=0, ymax=(target_alt+r_pl)*1.1/1000.0 ,axes_labels=[descs[j]+' cbr','semi-latus rectum'])\n",
" else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'\n",
"\n",
" cbrs [j] = RDF(target_cbr_cs)\n",
" radii [j] = RDF(target_c_rad)\n",
" omegas[j] = RDF(sqrt(tip_accel_max/radii[j])) #Angular rotation rate of the capture orbit sling with index j.\n",
" debug_sling(j)\n",
" print traj_string"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"Orbit sign = 1 (+1 for CCW, -1 for CW)\n",
"capture sling rotation axis = (0.0, 0.0, 1.0)\n",
"-------------------------------------------------------\n",
"Elliptical orbit\n",
"Position vector (km) = (3920.46738022426, -2.23712340321356e-10, -0.000000000000000)\n",
"Velocity vector (km/s) = (1.43919013598211e-13, 4.33139333469434, 0.000000000000000)\n",
"Radial distance (km) = 3920.46738022426 , Alt. calcs. = 3920.46738022426 , 3920.46738022426 , 3920.46738022426\n",
"Speed (km/s) = 4.33139333469434\n",
"Radial velocity (km/s) = -1.03241859272351e-13\n",
"-------------------------------------------------------\n",
"Elliptical orbit\n",
"Position vector (km) = (3920.46738022426, -2.23712340321356e-10, -0.000000000000000)\n",
"Velocity vector (km/s) = (1.43919013598211e-13, 4.33139333469434, 0.000000000000000)\n",
"Radial distance (km) = 3920.46738022426 , Alt. calcs. = 3920.46738022426 , 3920.46738022426 , 3920.46738022426\n",
"Speed (km/s) = 4.33139333469434\n",
"Radial velocity (km/s) = -1.03241859272351e-13\n"
]
}
],
"source": [
"#If cbrs[1] was initially negative, it's not any more. So there's no need to write abs(cbrs[1]) from now on:\n",
"if cbrs[1] < tolerance: print 'Skipping this section because the capture sling has been disabled.'\n",
"else:\n",
"\n",
" print capture_frame_string\n",
"\n",
" #debug_sling(1)\n",
" #debug_sling(2)\n",
" print 'Orbit sign =',orbit_sign,'(+1 for CCW, -1 for CW)'\n",
" #print 'com_cs_pos0 =',com_cs_pos0\n",
" #print 'com_cs_vel0 =',com_cs_vel0\n",
"\n",
" def sling_tips(t):\n",
" \"\"\"\n",
" Input : Time in seconds since capture sling reaches periapsis.\\n\n",
" Output: Lists of lists of position and velocity vectors of the capture sling tips, relative to its center of mass. Dummy entries for moon sling tips included.\n",
" \"\"\"\n",
" tips_pos = [[nullvec for i in range(2)] for j in range(num_slings)]\n",
" tips_vel = [[nullvec for i in range(2)] for j in range(num_slings)]\n",
" if trajopt < 100: #Perpendicular setup - capture sling axis points radially away from the planet at periapsis.\n",
" somerotate = rotateY(pi/2) #This only works when com_cs_pos0 points along the x-axis. A more general solution would rotate the slings' axis of rotation around a vector that's perpendicular to both the original axis of rotation and com_cs_pos0.\n",
" else: #Coplanar setup - capture sling axis points along specific orbital angular momentum vector.\n",
" somerotate = 1\n",
" for j in range(1,3): #Loop through capture slings A and B.\n",
" if j == 1: rotation_sign = orbit_sign #Capture sling A rotates in the same way as the orbit.\n",
" else: rotation_sign = -orbit_sign #Capture sling B rotates in the opposite way as the orbit.\n",
" theta_t = rotation_sign*omegas[j]*t + theta0s[j]\n",
" if j == 2: theta_t += pi.n() #Capture sling B is rotated by \"pi\" so if its theta0s == 0, its payload is closest to the planet.\n",
" tips_pos[j][0] = somerotate*rotateZ(theta_t)*radii[j]*com_cs_pos0/com_cs_pos0.norm() #Orient using unit vector in the direction of the capture sling center of mass when it reaches periapsis.\n",
" tips_pos[j][1] = -tips_pos[j][0]/cbrs[j]\n",
" someneg = (-1)^(j-1) #This term is sometimes negative (when j=2), which allows the same equation to be used for slings A and B.\n",
" tips_vel[j][0] = somerotate*rotateZ(theta_t)*radii[j]*omegas[j]*someneg*com_cs_vel0/com_cs_vel0.norm() #Velocity vector needs to be reversed for capture sling B.\n",
" tips_vel[j][1] = -tips_vel[j][0]/cbrs[j]\n",
" return tips_pos, tips_vel\n",
"\n",
" tips_pos, tips_vel = sling_tips(0)\n",
" if 2 == 1:\n",
" print 'A tips0 pos:',tips_pos[1]\n",
" print 'A tips0 vel:',tips_vel[1]\n",
" print 'B tips0 pos:',tips_pos[2]\n",
" print 'B tips0 vel:',tips_vel[2]\n",
"\n",
" cs_axis = tips_pos[1][0].cross_product(tips_vel[1][0]) #This vector points from the capture sling system's total center of mass to the center of mass of sling A.\n",
" cs_axis /= cs_axis.norm() #Unit vector along capture sling axis.\n",
" print 'capture sling rotation axis =',cs_axis\n",
"\n",
" #The capture sling also throws counterbalance mass backwards to keep the sling's center of mass stationary and to prevent the capture\n",
" #sling's orbit from lowering due to throwing the payload forward.\n",
"\n",
" #First, consider throwing the counterbalance mass into a circular orbit.\n",
" v_circ_at_periapsis = sqrt(mu/periapsis_capture)\n",
" delta_v_capture_to_circ = capture_speed_periapsis - v_circ_at_periapsis\n",
"\n",
" if trajopt == 1:\n",
" #Obtain the radius, cbr and omega from the tip velocities calculated above.\n",
" cbrs [1] = v_tip_capture_payload1/delta_v_capture_to_circ\n",
" radii [1] = v_tip_capture_payload1^2/tip_accel_max\n",
" #if cbrs[1] < 1: radii[1] /= cbrs[1] #If payload moves slower, accel limit applies to the counterbalance arm, not the payload arm. Disabled because this is harder to fix for the coplanar configuration, so the accel limit now applies ONLY to the payload arm.\n",
" omegas[1] = v_tip_capture_payload1/radii[1] #Angular rotation rate of the capture orbit sling A.\n",
" cbrs [2] = cbrs [1] #Here, slings A and B are identical.\n",
" radii [2] = radii [1]\n",
" omegas[2] = omegas[1]\n",
"\n",
" for j in range(1,3): #Loop through capture orbit slings.\n",
" kepler_com_cs[5] = throw_times[j]\n",
" com_cs_pos,com_cs_vel = kepler2xyz(kepler_com_cs)\n",
" tips_pos, tips_vel = sling_tips(throw_times[j])\n",
" for k in range(2): #Loop through capture orbit sling arms.\n",
" throw_pos[j][k] = com_cs_pos + tips_pos[j][k]\n",
" throw_vel[j][k] = com_cs_vel + tips_vel[j][k]"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 7. Print the capture slings' payload trajectories and counterbalance orbits. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"capture orbit speed at periapsis (m/s) = 4331.39333469434\n",
"circular orbit speed at periapsis (m/s) = 3305.19320027322\n",
"delta-v from capture orbit to circular orbit (m/s) = 1026.20013442112\n",
"required tip speed of capture payload sling A (m/s) = 986.41489105\n",
"mass ratio (counterbalance mass divided by payload mass) = 0.992363104788\n",
"\n",
"Payload A trajectory:\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n",
"Position vector (km) = (4019.65334732772, -2.23712340321356e-10, 0.000000000000000)\n",
"Velocity vector (km/s) = (1.43919013598211e-13, 5.31780822574420, 0.000000000000000)\n",
"Radial distance (km) = 4019.65334732772\n",
"Speed (km/s) = 5.31780822574420\n",
"Radial velocity (km/s) = -1.52041662799987e-13\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Specific relative angular momentum vector = (-0.000000000000000, 0.000000000000000, 2.13757456350595e10)\n",
"Specific relative angular momentum (m^2/s) = 2.13757456350595e10\n",
"Specific orbital energy (kJ/kg) = 3484.80000000000\n",
"Hyperbolic excess velocity at infinity (km/s) = 2.64000000000000 , C3 (km/s)^2: 6.96960000000000\n",
"Periapsis vs infinity deflection (degrees) = 37.1962472232506\n",
"Eccentricity vector = (1.65413126788470, -1.61757013386656e-14, 0.000000000000000)\n",
"Eccentricity = 1.65413126788470 , Alt. calc. = 1.65413126788470\n",
"Semi-latus rectum (km) = 10668.6876351999 , Alt. calc. = 10668.6876351999\n",
"Semi-minor axis (km) = -8096.87334661346\n",
"Semi-major axis (km) = -6145.02553948577\n",
"Periapsis altitude (km) = 623.653347327717\n",
"Periapsis (km) = 4019.65334732772\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = -5.60293753805189e-13 , Alt. calc. = -5.60293753805189e-13\n",
"Mean anomaly (degrees) = -8.53573971326177e-13\n",
"Eccentric anomaly (degrees) = -1.30489706460053e-12\n",
"True anomaly (degrees) = -2.62848194844022e-12 , Alt. calc. = -1.70754729250319e-6\n",
"Time since periapsis (hours) = -9.63242853357347e-15\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"Counterbalance A orbit:\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (3820.51811102545, -2.23712340321356e-10, -0.000000000000000)\n",
"Velocity vector (km/s) = (1.43919013598211e-13, 3.33738732390023, 0.000000000000000)\n",
"Radial distance (km) = 3820.51811102545\n",
"Speed (km/s) = 3.33738732390023\n",
"Radial velocity (km/s) = -5.15033629202851e-14\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 1.97254867994179\n",
"Specific relative angular momentum vector = (0.000000000000000, -0.000000000000000, 1.27505487144676e10)\n",
"Specific relative angular momentum (m^2/s) = 1.27505487144676e10\n",
"Specific orbital energy (kJ/kg) = -5641.01767024399\n",
"Eccentricity vector = (-0.00641748326082714, 1.57089830136041e-14, 0.000000000000000)\n",
"Eccentricity = 0.00641748326083358 , Alt. calc. = 0.00641748326082714\n",
"Semi-latus rectum (km) = 3796.00000000025 , Alt. calc. = 3796.00000000025\n",
"Semi-minor axis (km) = 3796.07816982026\n",
"Semi-major axis (km) = 3796.15634124999\n",
"Periapsis altitude (km) = 375.794571474513\n",
"Periapsis (km) = 3771.79457147451\n",
"Apoapsis altitude (km) = 424.518111025472\n",
"Apoapsis (km) = 3820.51811102547\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 179.999999999860 , Alt. calc. = 179.999999999860\n",
"Mean anomaly (degrees) = 180.000000000139\n",
"Eccentric anomaly (degrees) = 180.000000000138\n",
"True anomaly (degrees) = 180.000000000137 , Alt. calc. = 180.000081864383\n",
"Time since periapsis (hours) = 0.986274339971654\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"Payload B trajectory:\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n",
"Position vector (km) = (3796.46681889307, -2.23697154632303e-10, 0.000000000000000)\n",
"Velocity vector (km/s) = (1.44054083063624e-13, 5.43431921917568, 0.000000000000000)\n",
"Radial distance (km) = 3796.46681889307\n",
"Speed (km/s) = 5.43431921917568\n",
"Radial velocity (km/s) = -1.76149359949131e-13\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Specific relative angular momentum vector = (-0.000000000000000, 0.000000000000000, 2.06312125988734e10)\n",
"Specific relative angular momentum (m^2/s) = 2.06312125988734e10\n",
"Specific orbital energy (kJ/kg) = 3484.80000000000\n",
"Hyperbolic excess velocity at infinity (km/s) = 2.64000000000000 , C3 (km/s)^2: 6.96960000000000\n",
"Periapsis vs infinity deflection (degrees) = 38.1789052710473\n",
"Eccentricity vector = (1.61781139793453, -1.04710406686117e-14, 0.000000000000000)\n",
"Eccentricity = 1.61781139793453 , Alt. calc. = 1.61781139793453\n",
"Semi-latus rectum (km) = 9938.43411037852 , Alt. calc. = 9938.43411037852\n",
"Semi-minor axis (km) = -7814.85325714902\n",
"Semi-major axis (km) = -6145.02553948577\n",
"Periapsis altitude (km) = 400.466818893073\n",
"Periapsis (km) = 3796.46681889307\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = -3.70838305495469e-13 , Alt. calc. = -3.70838305495469e-13\n",
"Mean anomaly (degrees) = -9.01952604088587e-13\n",
"Eccentric anomaly (degrees) = -1.45991577219845e-12\n",
"True anomaly (degrees) = -3.00516982546808e-12 , Alt. calc. = 0.000000000000000\n",
"Time since periapsis (hours) = -1.01783726910692e-14\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"Counterbalance B orbit:\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (4055.61399803649, -2.23728891008648e-10, -0.000000000000000)\n",
"Velocity vector (km/s) = (1.43771803124127e-13, 3.12932859091382, 0.000000000000000)\n",
"Radial distance (km) = 4055.61399803649\n",
"Speed (km/s) = 3.12932859091382\n",
"Radial velocity (km/s) = -2.88583376106089e-14\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 1.96059710486674\n",
"Specific relative angular momentum vector = (0.000000000000000, -0.000000000000000, 1.26913488377659e10)\n",
"Specific relative angular momentum (m^2/s) = 1.26913488377659e10\n",
"Specific orbital energy (kJ/kg) = -5663.91910657185\n",
"Eccentricity vector = (-0.0726847466928630, 1.25612742670732e-14, 0.000000000000000)\n",
"Eccentricity = 0.0726847466928633 , Alt. calc. = 0.0726847466928630\n",
"Semi-latus rectum (km) = 3760.83272190518 , Alt. calc. = 3760.83272190518\n",
"Semi-minor axis (km) = 3770.80663480852\n",
"Semi-major axis (km) = 3780.80699901825\n",
"Periapsis altitude (km) = 110.000000000000\n",
"Periapsis (km) = 3506.00000000000\n",
"Apoapsis altitude (km) = 659.613998036492\n",
"Apoapsis (km) = 4055.61399803649\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 179.999999999990 , Alt. calc. = 179.999999999990\n",
"Mean anomaly (degrees) = 180.000000000008\n",
"Eccentric anomaly (degrees) = 180.000000000007\n",
"True anomaly (degrees) = 180.000000000007 , Alt. calc. = 180.000006331755\n",
"Time since periapsis (hours) = 0.980298552433412\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Position vector (km) = (-1.59600415940996e12, 2.10294384273536e12, 0.000000000000000)\n",
"Velocity vector (km/s) = (-1.59600393112755, 2.10294352855019, 0.000000000000000)\n",
"Radial distance (km) = 2.64000039442278e12 , Alt. calcs. = 2.64000011717475e12 , 2.64000039442278e12 , 2.64000039442278e12\n",
"Speed (km/s) = 2.64000000614503\n",
"Radial velocity (km/s) = 2.64000000614503\n",
"Payload A's velocity vector after 31688. years: (-1596.00393112755, 2102.94352855019, 0.000000000000000)\n",
"Payload A's velocity vector is aimed at 127.196247223250 degrees longitude, relative to Deimos's ascending node.\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n",
"Position vector (km) = (-1.63183423226953e12, 2.07526316382690e12, 0.000000000000000)\n",
"Velocity vector (km/s) = (-1.63183422339311, 2.07526313989546, 0.000000000000000)\n",
"Radial distance (km) = 2.64000003044385e12 , Alt. calcs. = 2.64000011731145e12 , 2.64000003044385e12 , 2.64000003044385e12\n",
"Speed (km/s) = 2.64000000614503\n",
"Radial velocity (km/s) = 2.64000000614503\n",
"Payload B's velocity vector after 31688. years: (-1631.83422339311, 2075.26313989546, 0.000000000000000)\n",
"Payload B's velocity vector is aimed at 128.178905271047 degrees longitude, relative to Deimos's ascending node.\n",
"\n",
"Payload A's longitude after 31688. years is 127.20 degrees, and its residual velocity out of the ecliptic plane is 0.00000 m/s. No periapsis rotation is required if payload A's longitude =FINISH THIS BY LOOPING. ALSO, CHECK PREVIOUS LINES CAUSE THEY WERE HASTILY COPIED!Payload B's longitude after 31688. years is 128.18 degrees, and its residual velocity out of the ecliptic plane is 0.00000 m/s. No periapsis rotation is required if payload B's longitude =FINISH THIS BY LOOPING. ALSO, CHECK PREVIOUS LINES CAUSE THEY WERE HASTILY COPIED!\n",
"\n",
"\n",
"Low altitude circular orbit:\n",
"-------------------------------------------------------\n",
"Circular, non-inclined orbit: using true longitude.\n",
"Position vector (km) = (3796.00000000000, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (0.000000000000000, 3.35894328621369, 0.000000000000000)\n",
"Radial distance (km) = 3796.00000000000\n",
"Speed (km/s) = 3.35894328621369\n",
"Radial velocity (km/s) = 0.000000000000000\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 1.97242682475854\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 1.27505487144672e10)\n",
"Specific relative angular momentum (m^2/s) = 1.27505487144672e10\n",
"Specific orbital energy (kJ/kg) = -5641.25000000000\n",
"Eccentricity vector = (0.000000000000000, 0.000000000000000, 0.000000000000000)\n",
"Eccentricity = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Semi-latus rectum (km) = 3796.00000000000 , Alt. calc. = 3796.00000000000\n",
"Semi-minor axis (km) = 3796.00000000000\n",
"Semi-major axis (km) = 3796.00000000000\n",
"Periapsis altitude (km) = 400.000000000000\n",
"Periapsis (km) = 3796.00000000000\n",
"Apoapsis altitude (km) = 400.000000000000\n",
"Apoapsis (km) = 3796.00000000000\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Mean anomaly (degrees) = 0.000000000000000\n",
"Eccentric anomaly (degrees) = 0.000000000000000\n",
"True longitude (degrees) = 0.000000000000000\n",
"Time since periapsis (hours) = 0.000000000000000\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"Simple payload deflection subtraction (degrees): -0.982658047796883\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n",
"Position vector (km) = (-1.59600415940996e12, 2.10294384273536e12, 0.000000000000000)\n",
"Velocity vector (km/s) = (-1.59600393112755, 2.10294352855019, 0.000000000000000)\n",
"Radial distance (km) = 2.64000039442278e12 , Alt. calcs. = 2.64000011717475e12 , 2.64000039442278e12 , 2.64000039442278e12\n",
"Speed (km/s) = 2.64000000614503\n",
"Radial velocity (km/s) = 2.64000000614503\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n",
"Position vector (km) = (-1.63183423226953e12, 2.07526316382690e12, 0.000000000000000)\n",
"Velocity vector (km/s) = (-1.63183422339311, 2.07526313989546, 0.000000000000000)\n",
"Radial distance (km) = 2.64000003044385e12 , Alt. calcs. = 2.64000011731145e12 , 2.64000003044385e12 , 2.64000003044385e12\n",
"Speed (km/s) = 2.64000000614503\n",
"Radial velocity (km/s) = 2.64000000614503\n",
"\n",
"Numeric payload deflection angle (degrees), absolute value:\n",
"-------------------------------------------------------\n",
"#1 = (-1596.00393112755, 2102.94352855019, 0.000000000000000)\n",
"#2 = (-1631.83422339311, 2075.26313989546, 0.000000000000000)\n",
"delta = (-35.8302922655535, -27.6803886547350, 0.000000000000000)\n",
"#1 magnitude, inclination, azimuth (deg) = (2640.00000614503, 90.0000000000000, 127.196247223250)\n",
"#2 magnitude, inclination, azimuth (deg) = (2640.00000614503, 90.0000000000000, 128.178905271047)\n",
"delta magnitude, inclination, azimuth (deg) = (45.2770776432420, 90.0000000000000, -142.312423752851)\n",
"angle between vectors #1 and #2 (degrees) = 0.982658047797712\n",
"Normalized cross product = (0.000000000000000, 0.000000000000000, 1.00000000000000)\n"
]
}
],
"source": [
"if cbrs[1] < tolerance: print '!!!!!Need to display counterbalance trajectory for cbrs[1]=0!!!'\n",
"else:\n",
" print capture_frame_string\n",
" print 'capture orbit speed at periapsis (m/s) =',capture_speed_periapsis\n",
" print 'circular orbit speed at periapsis (m/s) =',v_circ_at_periapsis\n",
" print 'delta-v from capture orbit to circular orbit (m/s) =',delta_v_capture_to_circ\n",
" print 'required tip speed of capture payload sling A (m/s) =',radii[1]*omegas[1]\n",
" print 'mass ratio (counterbalance mass divided by payload mass) =',cbrs[1]\n",
"\n",
" for j in range(1,num_slings-1): #Don't loop over capture sling C because its counterbalance ratio hasn't yet been derived.\n",
" if cbrs[j] > dmr: print '\\n!!! DANGER !!!',cap1st(descs[j]),'mass ratio',cbrs[j],'is higher than the safe limit of',dmr,'!!!'\n",
" if cbrs[j] < 1/dmr: print '\\n!!! DANGER !!!',cap1st(descs[j]),'mass ratio',cbrs[j], 'is lower than the safe limit of',1/dmr,'!!!'\n",
"\n",
" print '\\nPayload A trajectory:'\n",
" j = 1 ; k = 0\n",
" keplers[j][k] = xyz2kepler(throw_pos[j][k],throw_vel[j][k])\n",
" throw_tpers[j][k] = keplers[j][k][5]\n",
"\n",
" print '\\nCounterbalance A orbit:'\n",
" j = 1 ; k = 1\n",
" keplers[j][k] = xyz2kepler(throw_pos[j][k],throw_vel[j][k])\n",
" throw_tpers[j][k] = keplers[j][k][5]\n",
"\n",
" print '\\nPayload B trajectory:'\n",
" j = 2 ; k = 0\n",
" keplers[j][k] = xyz2kepler(throw_pos[j][k],throw_vel[j][k])\n",
" throw_tpers[j][k] = keplers[j][k][5]\n",
"\n",
" print '\\nCounterbalance B orbit:'\n",
" j = 2 ; k = 1\n",
" keplers[j][k] = xyz2kepler(throw_pos[j][k],throw_vel[j][k])\n",
" throw_tpers[j][k] = keplers[j][k][5]\n",
"\n",
" #Describe the payloads' outgoing hyperbolic asymptotes.\n",
" keplers_copy = copy.deepcopy(keplers) #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
" payload_lon_string = ''\n",
" for j in range(1,num_slings-1):\n",
" keplers_copy[j][0][5] = long_time\n",
" pD_far,vD_far = kepler2xyz(keplers_copy[j][0])\n",
" payload_lon_direct = restrict_rad(atan2(vD_far[1],vD_far[0])) #Longitude of the payload's outgoing asymptote, relative to the moon's ascending node. Sagemath expects arctan2(y,x).\n",
" print 'Payload '+sdescs[j]+'\\'s velocity vector after',(long_time/year).n(digits=summary_digits),'years:',vD_far\n",
" print 'Payload '+sdescs[j]+'\\'s velocity vector is aimed at',(payload_lon_direct*180/pi).n(),'degrees longitude, relative to',moon_name+'\\'s ascending node.'\n",
" payload_lon_string += 'Payload '+sdescs[j]+'\\'s longitude after '+str((long_time/year).n(digits=summary_digits))+' years is '+str((payload_lon_direct*180/pi).n(digits=summary_digits))+' degrees, '\n",
" payload_lon_string += 'and its residual velocity out of the ecliptic plane is '+str((vD_far[2]).n(digits=summary_digits))+' m/s. '\n",
" payload_lon_string += 'No periapsis rotation is required if payload '+sdescs[j]+'\\'s longitude =FINISH THIS BY LOOPING. ALSO, CHECK PREVIOUS LINES CAUSE THEY WERE HASTILY COPIED!'\n",
" print '\\n'+payload_lon_string+'\\n'\n",
" \n",
" if trajopt < 100: #This simple calculation doesn't apply to coplanar capture orbit slings.\n",
" print '\\nUsing these orbits and the highest safe mass ratio, the highest safe payload tip speed is (m/s):',dmr*delta_v_capture_to_circ\n",
" print 'Using that tip speed, this is the fastest possible payload trajectory:'\n",
" p_i = com_cs_pos0\n",
" v_i = (capture_speed_periapsis + delta_v_capture_to_circ*dmr)*com_cs_vel0/com_cs_vel0.norm()\n",
" kepler = xyz2kepler(p_i,v_i)\n",
"\n",
" print '\\nUsing these orbits and the lowest safe mass ratio, the slowest safe payload tip speed is (m/s):',delta_v_capture_to_circ/dmr\n",
" print 'Using that tip speed, this is the slowest possible payload trajectory:'\n",
" p_i = com_cs_pos0\n",
" v_i = (capture_speed_periapsis + delta_v_capture_to_circ/dmr)*com_cs_vel0/com_cs_vel0.norm()\n",
" kepler = xyz2kepler(p_i,v_i)\n",
"\n",
" print '\\nLow altitude circular orbit:'\n",
" p_lmo = (target_alt+r_pl)*com_cs_pos0/com_cs_pos0.norm() #Position of low altitude circular orbit.\n",
" v_lmo = sqrt(mu/(target_alt+r_pl))*com_cs_vel0/com_cs_vel0.norm() #Velocity of low altitude circular orbit.\n",
" kepler_lmo = xyz2kepler(p_lmo,v_lmo)\n",
"\n",
" #This simple payload deflection calculation only applies if both payload trajectories are in the same plane.\n",
" deflectionA = my_arccos(-1/keplers[1][0][1],'deflection') - pi/2 #\"1\" at the end is the Keplers index for eccentricity.\n",
" deflectionB = my_arccos(-1/keplers[2][0][1],'deflection') - pi/2\n",
" #These deflections are relative to the periapsis velocity vectors, which are determined by the argument of periapsis (keplers element with index \"3\").\n",
" print '\\nSimple payload deflection subtraction (degrees):',(((deflectionA+keplers[1][0][3])-(deflectionB+keplers[2][0][3]))*180/pi).n()\n",
"\n",
" #Compare velocity vectors at a \"very large\" time since periapsis to obtain the angle between them.\n",
" keplers_copy = copy.deepcopy(keplers) #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
" keplers_copy[1][0][5] = long_time\n",
" keplers_copy[2][0][5] = long_time\n",
" pA_far,vA_far = kepler2xyz(keplers_copy[1][0])\n",
" pB_far,vB_far = kepler2xyz(keplers_copy[2][0])\n",
" print '\\nNumeric payload deflection angle (degrees), absolute value:'\n",
" compare_vectors(vA_far,vB_far)"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 8. Calculate the delta-v from the chosen moon's orbit to the inclined capture sling orbit. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"capture orbit eccentricity = 0.717360803768281\n",
"1\n",
"capture orbit's true anomaly at Deimos (degrees) = -173.712720193813\n",
"capture orbit's flight path angle at Deimos = 15.3109755656732\n",
"Deimos position vector = (2.34632000000000e7, 0.000000000000000, 0.000000000000000)\n",
"Deimos velocity vector = (0.000000000000000, 1197.52577832669, 625.519182999978)\n",
"Rendezvous with Deimos happens at longitude (degrees) = 0.000000000000000 , Alt. calc: 0.000000000000000\n",
"Initial capture sling periapsis position vector = (3.92046738022426e6, 0.000000000000000, 0.000000000000000)\n",
"Initial capture sling periapsis velocity vector = (0.000000000000000, 4331.39333469434, 0.000000000000000)\n",
"Rotation matrix =\n",
"[-0.993985292993338 -0.109513639848871 0.000000000000000]\n",
"[ 0.109513639848871 -0.993985292993338 0.000000000000000]\n",
"[ 0.000000000000000 0.000000000000000 1.00000000000000]\n",
"Capture sling periapsis position vector after rotation = (-3.89688691760304e6, 429344.652717125, 0.000000000000000)\n",
"Capture sling periapsis velocity vector after rotation = (-474.346649699514, -4305.34127285554, 0.000000000000000)\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (-3896.88691760304, 429.344652717125, 0.000000000000000)\n",
"Velocity vector (km/s) = (-0.474346649699514, -4.30534127285554, 0.000000000000000)\n",
"Radial distance (km) = 3920.46738022426\n",
"Speed (km/s) = 4.33139333469434\n",
"Radial velocity (km/s) = -6.08138152874835e-17\n",
"Flight path angle (degrees) = 8.53773646251594e-7\n",
"Orbital period (hours) = 13.7774796305327\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 1.69810862795899e10)\n",
"Specific relative angular momentum (m^2/s) = 1.69810862795899e10\n",
"Specific orbital energy (kJ/kg) = -1543.81798121506\n",
"Eccentricity vector = (-0.713046088715551, 0.0785607927055758, 0.000000000000000)\n",
"Eccentricity = 0.717360803768281 , Alt. calc. = 0.717360803768281\n",
"Semi-latus rectum (km) = 6732.85701124926 , Alt. calc. = 6732.85701124926\n",
"Semi-minor axis (km) = 9663.89991054697\n",
"Semi-major axis (km) = 13870.9260162561\n",
"Periapsis altitude (km) = 524.467380224260\n",
"Periapsis (km) = 3920.46738022426\n",
"Apoapsis altitude (km) = 20425.3846522880\n",
"Apoapsis (km) = 23821.3846522880\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 173.712720193813 , Alt. calc. = 173.712720193813\n",
"Mean anomaly (degrees) = -2.20820288623560e-16\n",
"Eccentric anomaly (degrees) = -7.81279778486644e-16\n",
"True anomaly (degrees) = -1.92584425507893e-15 , Alt. calc. = -2.25887274392255e-6\n",
"Time since periapsis (hours) = -8.45096396810961e-18\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"Set tper= 28429.62 to find position and velocity when capture orbit sling is at the same position as Deimos\n",
"-------------------------------------------------------\n",
"Elliptical orbit\n",
"Position vector (km) = (23463.2000000000, -4.74258423497668e-11, -0.000000000000000)\n",
"Velocity vector (km/s) = (-0.198139897653759, 0.723732750843447, 0.000000000000000)\n",
"Radial distance (km) = 23463.2000000000 , Alt. calcs. = 23463.2000000000 , 23463.2000000000 , 23463.2000000000\n",
"Speed (km/s) = 0.750365586688025\n",
"Radial velocity (km/s) = -0.198139897653761\n",
"\n",
"Compare position vectors: Deimos at release vs. capture orbit sling when the Deimos sling releases.\n",
"These should be the same.\n",
"-------------------------------------------------------\n",
"Vectors are the same, within tolerance.\n",
"\n",
"\n",
"Compare velocity vectors: Deimos at release vs. capture orbit sling when the Deimos sling releases. This is the required delta-v.\n",
"-------------------------------------------------------\n",
"#1 = (0.000000000000000, 1197.52577832669, 625.519182999978)\n",
"#2 = (-198.139897653759, 723.732750843447, 0.000000000000000)\n",
"delta = (-198.139897653759, -473.793027483239, -625.519182999978)\n",
"#1 magnitude, inclination, azimuth (deg) = (1351.05227066087, 62.4200000000000, 90.0000000000000)\n",
"#2 magnitude, inclination, azimuth (deg) = (750.365586688025, 90.0000000000000, 105.310975565673)\n",
"delta magnitude, inclination, azimuth (deg) = (809.329043241954, 140.613771949563, -112.694645962782)\n",
"angle between vectors #1 and #2 (degrees) = 31.2507196854670\n",
"Normalized cross product = (-0.860770210500799, -0.235657321315456, 0.451154376710272)\n",
"-------------------------------------------------------\n",
"delta-v directly into the inclined capture orbit (m/s) = 809.329043241954\n"
]
}
],
"source": [
"if cbrs[1] < tolerance: print 'Skipping this section because capture orbit sling has been disabled.'\n",
"else:\n",
" #Throw payload directly into the inclined capture orbit\n",
" print capture_frame_string\n",
"\n",
" #Capture orbit speed at the moon (either Phobos or Deimos)\n",
" capture_speed_moon = sqrt(mu*(2/a_moon-1/a_capture))\n",
"\n",
" #Capture orbit eccentricity\n",
" ecc = (apoapsis_capture - periapsis_capture) / (apoapsis_capture + periapsis_capture)\n",
" print 'capture orbit eccentricity =',ecc\n",
"\n",
" if ecc < tolerance and abs(a_moon - a_capture) < tolerance:\n",
" ta = 0 ; fpa = 0 ; tper = 0\n",
" print 'The capture orbit is circular with the same radius as the orbit of',moon_name,'so the true anomaly (actually, the argument of latitude), flight path angle, and time since periapsis were all set to zero.'\n",
" print 'In this case, the calculated delta-v should match the circular orbit approximation of inclination change, which yields a delta-v of (m/s):',(2*v_moon0.norm()*sin(inc_moon/2)).n()\n",
" else:\n",
" #Calculate cosine of capture orbit's true anomaly at the moon (either Phobos or Deimos).\n",
" var('cos_ta') #Resets cos_ta so it's just a variable again.\n",
" eq2 = r == a*(1-ecc^2)/(1+ecc*cos_ta) # https://en.wikipedia.org/wiki/True_anomaly#Radius_from_true_anomaly\n",
" soln2 = solve(eq2.subs(r=a_moon,a=a_capture),cos_ta)\n",
" cos_ta = soln2[0].rhs() #Cosine of the true anomaly of the capture orbit at the moon.\n",
" ta = my_arccos(cos_ta,'ta')\n",
" print throw_at_LAN\n",
" if throw_in > 0: ta = -ta #Two places on the capture orbit are at the moon's radius. Choose to throw OUT away from the planet or throw IN.\n",
" print 'capture orbit\\'s true anomaly at',moon_name,'(degrees) =',(ta*180/pi).n()\n",
"\n",
" #Capture orbit's flight path angle at the moon.\n",
" var('cos_fpa') #Cosine of flight path angle.\n",
" cos_fpa = (1 + ecc*cos_ta)/sqrt(1+ecc^2+2*ecc*cos_ta) #cosine of flight path angle at the moon.\n",
" fpa = my_arccos(cos_fpa,'fpa')\n",
" print 'capture orbit\\'s flight path angle at',moon_name,'=',(fpa*180/pi).n()\n",
"\n",
" #Convert true anomaly to 'time since periapsis' for input into kepler2xyz. This calculation is valid ONLY for elliptical orbits.\n",
" EA = 2*arctan2(sqrt(1-ecc)*sin(ta/2),sqrt(1+ecc)*cos(ta/2))\n",
" EA = EA.n() #EA sometimes gives errors if it isn't converted to numerical form.\n",
" EA = restrict_rad(EA) #Restricts angle to lie in [0,2*pi).\n",
" MA = EA - ecc*sin(EA)\n",
" MA = restrict_rad(MA) #Restricts angle to lie in [0,2*pi).\n",
" tper = sqrt(a_capture^3/mu)*MA\n",
"\n",
" #Calculate the delta-v needed to go from the moon straight into the inclined capture orbit, using xyz2kepler and kepler2xyz.\n",
"\n",
" print moon_name,'position vector =',p_moon1.n()\n",
" print moon_name,'velocity vector =',v_moon1.n()\n",
"\n",
" #Rotated trajectory\n",
" inc = 0# Now this cell's calculations are done in the capture frame, so inc = 0 instead of the old value: inc_moon #Inclination. This one's already in radians.\n",
" #LAN = 0.0*pi/180 #Longitude of ascending node. Convert degrees to radians.\n",
" LAN2_moon = my_arccos(p_moon1[0]/p_moon1.norm(),'LAN2_moon') #[0] is x-component.\n",
" if p_moon1[1] < 0: LAN2_moon = 2*pi - LAN2_moon #[1] is y-component.\n",
" if throw_at_LAN: lon_throw = LAN_moon\n",
" else: lon_throw = LAN_moon + pi #Otherwise, throw at descending node.\n",
" print 'Rendezvous with',moon_name,'happens at longitude (degrees) =',(lon_throw*180/pi).n(),', Alt. calc:',(LAN2_moon*180/pi).n()\n",
"\n",
" AP = -ta #AP = -ta because arg_lat=0 at release. arg_lat is the angle between the ascending node and the capture orbit sling.\n",
" p_in = com_cs_pos0\n",
" v_in = com_cs_vel0\n",
"\n",
" print 'Initial capture sling periapsis position vector =',p_in.n()\n",
" print 'Initial capture sling periapsis velocity vector =',v_in.n()\n",
"\n",
" #https://en.wikipedia.org/wiki/Orbital_elements#Euler_angle_transformations\n",
" #http://web.archive.org/web/20171123040031/http://www.physics.csbsju.edu/orbit/orbit.3d.html\n",
" #\"I, J is in the equatorial plane of the central body. I is in the direction of the vernal equinox. J is perpendicular to I and with I defines the reference plane. K is perpendicular to the reference plane. Orbital elements of bodies (planets, comets, asteroids,...) in the solar system usually use the ecliptic as that plane.\"\n",
" #\"x, y are in the orbital plane and with x in the direction to the pericenter (periapsis). z is perpendicular to the plane of the orbit. y is mutually perpendicular to x and z.\"\n",
" #The inverse rotation is given in the references above.\n",
" #\"the transformation from the I, J, K coordinate frame to the x, y, z frame\":\n",
" rotation_moon_to_capture = rotateZ(-AP)*rotateX(-inc)*rotateZ(-lon_throw)\n",
" #The capture orbit's position and velocity vectors were initially defined in the frame x, y, z. Now we need to rotate to the frame I, J, K. That's the inverse of the rotation given in the references above. So reverse the order and sign of the rotations:\n",
" rotation_capture_to_moon = rotateZ(lon_throw)*rotateX(inc)*rotateZ(AP)\n",
" print 'Rotation matrix ='\n",
" print rotation_capture_to_moon.n()\n",
"\n",
" p_in = rotation_capture_to_moon*p_in\n",
" v_in = rotation_capture_to_moon*v_in\n",
" print 'Capture sling periapsis position vector after rotation =',p_in.n()\n",
" print 'Capture sling periapsis velocity vector after rotation =',v_in.n()\n",
"\n",
" kepler2 = xyz2kepler(p_in,v_in)\n",
" (kepler2).n()\n",
"\n",
" print '\\nSet tper=',(tper).n(digits=7),' to find position and velocity when capture orbit sling is at the same position as',moon_name\n",
" kepler2[5] = tper #Time since periapsis corresponding to true anomaly ta.\n",
" (kepler2).n()\n",
"\n",
" p2,v2 = kepler2xyz(kepler2)\n",
"\n",
" print '\\nCompare position vectors:',moon_name,'at release vs. capture orbit sling when the',moon_name,'sling releases.'\n",
" print 'These should be the same.'\n",
" dv = compare_vectors(p_moon1,p2)\n",
" print\n",
" if dv.norm() > p_moon1.norm()*tolerance:\n",
" print warnstring,'Position vectors aren\\'t the same!'\n",
" print failure\n",
" else:\n",
" print '\\nCompare velocity vectors:',moon_name,'at release vs. capture orbit sling when the',moon_name,'sling releases. This is the required delta-v.'\n",
" dv = compare_vectors(v_moon1,v2)\n",
"\n",
" delta_v_m2c_vec = v2 - v_moon1\n",
" delta_v_moon_to_capture = delta_v_m2c_vec.norm()\n",
" print '-------------------------------------------------------'\n",
" print 'delta-v directly into the inclined capture orbit (m/s) =',delta_v_moon_to_capture.n()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Direct periapsis vs infinity deflection (degrees) = 11.9784946835745\n",
"v_r0 = 0.000000000000000\n",
"\n",
"p_test = (2.34632000000000e7*cos(loc1), 2.34632000000000e7*cos(0.153222222222222*pi)*sin(loc1), 2.34632000000000e7*sin(0.153222222222222*pi)*sin(loc1))\n",
"\n",
"v_test[2] = 1351.05227066087*cos(-1/2*pi + 1.77986049845591)*cos(loc1)*sin(0.153222222222222*pi) - 1351.05227066087*sin(-1/2*pi + 1.77986049845591)*sin(0.153222222222222*pi)*sin(loc1)\n",
"\n",
"v_test3[2] = 2702.10454132174*(3.17000096369701e10*cos(0.153222222222222*pi)*cos(loc1)^2 + 3.17000096369701e10*cos(0.153222222222222*pi)*sin(loc1)^2)*(-3.17000096369701e10*cos(loc1)^2*sin(0.153222222222222*pi) - 3.17000096369701e10*sin(0.153222222222222*pi)*sin(loc1)^2)*cos(0.153222222222222*pi)*cos(loc1)*sin(-1/4*pi + 0.889930249227954)^2/(abs(3.17000096369701e10*cos(0.153222222222222*pi)*cos(loc1)^2 + 3.17000096369701e10*cos(0.153222222222222*pi)*sin(loc1)^2)^2 + abs(-3.17000096369701e10*cos(loc1)^2*sin(0.153222222222222*pi) - 3.17000096369701e10*sin(0.153222222222222*pi)*sin(loc1)^2)^2) - 1351.05227066087*(2*(-3.17000096369701e10*cos(loc1)^2*sin(0.153222222222222*pi) - 3.17000096369701e10*sin(0.153222222222222*pi)*sin(loc1)^2)^2*sin(-1/4*pi + 0.889930249227954)^2/(abs(3.17000096369701e10*cos(0.153222222222222*pi)*cos(loc1)^2 + 3.17000096369701e10*cos(0.153222222222222*pi)*sin(loc1)^2)^2 + abs(-3.17000096369701e10*cos(loc1)^2*sin(0.153222222222222*pi) - 3.17000096369701e10*sin(0.153222222222222*pi)*sin(loc1)^2)^2) - 1)*cos(loc1)*sin(0.153222222222222*pi) + 1351.05227066087*(-3.17000096369701e10*cos(loc1)^2*sin(0.153222222222222*pi) - 3.17000096369701e10*sin(0.153222222222222*pi)*sin(loc1)^2)*sin(-1/2*pi + 1.77986049845591)*sin(loc1)/sqrt(abs(3.17000096369701e10*cos(0.153222222222222*pi)*cos(loc1)^2 + 3.17000096369701e10*cos(0.153222222222222*pi)*sin(loc1)^2)^2 + abs(-3.17000096369701e10*cos(loc1)^2*sin(0.153222222222222*pi) - 3.17000096369701e10*sin(0.153222222222222*pi)*sin(loc1)^2)^2)\n",
"\n",
"loc_root (degrees) = 78.0215053164267\n"
]
}
],
"source": [
"num_flag = 1\n",
"\n",
"#Calculate hyperbolic deflection angle for direct moon sling.\n",
"set_verbose(0)\n",
"v_direct1 = (speed_moon+v_tip_direct_moon_best_case)/v_moon1.norm()*v_moon1\n",
"p_direct1 = p_moon1\n",
"kepler_direct = xyz2kepler(p_direct1,v_direct1)\n",
"deflection_direct = my_arccos(-1/kepler_direct[1],'deflection') - pi/2 #\"1\" is the Kepler index for eccentricity.\n",
"print 'Direct periapsis vs infinity deflection (degrees)'.ljust(lj),'=',(deflection_direct*180/pi).n()\n",
"\n",
"#What location on the moon's orbit should the direct moon sling throw if the outgoing hyperbolic asymptote needs to lie in the ecliptic plane and the sling throws in exactly the same direction as the moon's orbital velocity vector (to maximize the Oberth effect and obtain the values in Jokic and Longuski 2004 table 3)?\n",
"var('a_moon1 speed_moon1 vtip1 inc1 def1 loc1')\n",
"assume(a_moon1>0,speed_moon1>0,vtip1>0)\n",
"if num_flag != 0: a_moon1 = a_moon ; speed_moon1 = speed_moon ; inc1 = inc_moon\n",
"p_test0 = vector([a_moon1,0,0])\n",
"v_test0 = vector([0,speed_moon1,0])\n",
"v_r0 = p_test0.dot_product(v_test0)/p_test0.norm()\n",
"if hasattr(v_r0, \"__simplify_full__\"): v_r0 = v_r0.simplify_full() #Radial velocity\n",
"print 'v_r0 =',v_r0\n",
"#inc1=0 #Setting inc1=0 correctly yields 0 == 0, meaning loc1 has no restrictions- throwing at any location loc1 will end up in the ecliptic plane.\n",
"if num_flag != 0: def1=deflection_direct #Setting def1=0 correctly yields \"[loc1 == 1/2*pi]\" and v_r = 0.\n",
"p_test = rotateX(inc1)*rotateZ(loc1)*p_test0\n",
"v_test = rotateX(inc1)*rotateZ(def1)*rotateZ(loc1)*v_test0\n",
"print '\\np_test =',p_test\n",
"print '\\nv_test[2] =',v_test[2]\n",
"v_test2 = rotateX(inc1)*rotateZ(loc1)*v_test0\n",
"v_test3 = rotateV(p_test.cross_product(v_test2),def1)*v_test2 #Hyperbolic deflection rotates about the angular momentum vector h = r X v.\n",
"print '\\nv_test3[2] =',v_test3[2]\n",
"if num_flag == 0:\n",
" eq_loc = v_test[2] == 0\n",
" print '\\nEquation for moon orbit location:',eq_loc\n",
" loc_soln = solve(eq_loc,loc1)\n",
" print '\\nloc_soln =',loc_soln\n",
" loc_soln2 = arctan(loc_soln[0].rhs()/cos(loc1)) #This is usually necessary but check loc_soln to be sure.\n",
" if is_numeric(loc_soln2):\n",
" loc_soln2 = loc_soln2.n()\n",
" print '\\nloc_soln2 (degrees) =',(loc_soln2*180/pi)\n",
" else: print '\\nloc_soln2 =',loc_soln2\n",
"else:\n",
" loc_root = find_root(v_test3[2],0,pi)\n",
" print '\\nloc_root (degrees) =',(loc_root*180/pi).n()\n",
"#The eq_loc method (set num_flag=0) yields \"[sin(loc1) == cos(def1)*cos(loc1)/sin(def1)]\"\n",
"#That reduces to: tan(loc1) == cot(def1)\n",
"#That equation means loc1 = pi/2 - def1 because the complementary angle in that triangle has the adjacent and opposite sides switched.\n",
"#Another solution is loc1 = 3*pi/2 - def1 because that also has the same tangent.\n",
"#Interestingly, these locations don't depend on the inclination (aside from the fact that an inclination of zero means that throwing at any location loc1 will converge on an asymptote in the ecliptic plane.)\n",
"#The numerical method (set num_flag=1 to use find_root on v_test3 which was rotated using rotateV) agrees with the symbolic method."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"v_{\\star} \\ {\\mapsto}\\ \\sqrt{\\pi} v_{\\star} \\operatorname{erf}\\left(v_{\\star}\\right) e^{\\left(v_{\\star}^{2}\\right)}"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"characteristic velocity (m/s) = 2726.88419920932\n",
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"Throwing payloads from different arguments of latitude:\n",
"- - - - - - | -> 6 throws to aim:\n",
". . . . . . \n",
"Finished.\n"
]
}
],
"source": [
"v_c = sqrt((2*sigma)/(safety*rho))\n",
"tether_mass_ratio(v_star) = sqrt(pi)*v_star*exp(v_star^2)*erf(v_star) #sling mass ratio = sling mass divided by payload (+ grapple, ballast) mass.\n",
"display(latex(tether_mass_ratio))\n",
"print 'characteristic velocity (m/s) =',v_c\n",
"\n",
"numpoints_plot_direct = 4 #Either zero to disable plots, or any integer >= 2.\n",
"min_plot_direct = 0\n",
"max_plot_direct = 2*pi\n",
"summary_digits = 5\n",
"if numpoints_plot_direct < 4: verbose_direct = 1\n",
"else: verbose_direct = 0\n",
"lj=40\n",
"set_verbose(0)\n",
"print capture_frame_string\n",
"\n",
"#If throwing from an arbitrary location on the moon's orbit, what delta-v is needed so the outgoing hyperbolic asymptote lies in the ecliptic plane?\n",
"#Assume the throw doesn't change the radial velocity- since the moon is treated as being in a circular orbit, that means the radial velocity at the throw time is also zero, which also means the throw location is also the periapsis location for the outgoing payload hyperbolic trajectory.\n",
"offsets = [pi/2] #First, calculate the worst-case delta-v for direct moon sling.\n",
"#Now, if requested, add offsets that result in arglat going from min_plot_direct to max_plot_direct.\n",
"for j in range(numpoints_plot_direct): offsets += [j*(max_plot_direct-min_plot_direct)/(numpoints_plot_direct-1)+min_plot_direct - (pi/2 - deflection_direct)] #Loop through offsets to make plots.\n",
"offsets += [best_case_offset] #Then, calculate the actual delta-v from moon for requested direct moon sling throw location. Do this last so its values are used in the next sections.\n",
"arglats_direct = []\n",
"aims_direct = []\n",
"deltavees_direct = []\n",
"payload_vinfs_direct = []\n",
"payload_lons_direct = []\n",
"tether_mass_ratios_direct = []\n",
"print 'Throwing payloads from different arguments of latitude:'\n",
"if(verbose_direct): print\n",
"else: print '- ' * (numpoints_plot_direct+2)+'| ->',numpoints_plot_direct+2,'throws to aim:' #This bar shows the number of notifications (\". \") that need to be returned until these calculations are finished.\n",
"for loopoff in offsets:\n",
" if(verbose_direct):\n",
" print\n",
" print 'offset for this loop =',loopoff\n",
" else: print '.',\n",
" arglat_direct = pi/2 - deflection_direct + loopoff\n",
" arglats_direct += [(arglat_direct*180/pi).n()] #These are in degrees because their only purpose is to be plotted.\n",
" var('a_moon1 speed_moon1 vtip1 inc1 def1 loc1 aim1')\n",
" assume(a_moon1>0,speed_moon1>0,vtip1>0)\n",
" num_flag = 0\n",
" #if num_flag != 0: #All these variables used to be symbolic (at least until assignments below) unless num_flag != 0:\n",
" a_moon1 = a_moon ; speed_moon1 = speed_moon ; inc1 = inc_moon ; def1 = deflection_direct\n",
" p_test0 = vector([a_moon1,0,0])\n",
" v_test0 = vector([0,speed_moon1,0])\n",
" v_r0 = (p_test0.dot_product(v_test0)/p_test0.norm()) #Radial velocity\n",
" if hasattr(v_r0, \"__simplify_full__\"): v_r0 = v_r0.simplify_full()\n",
" #print 'v_r0 =',v_r0\n",
" #inc1=0 #This correctly yields \"[aim1 == 0]\"\n",
" #def1=0 #This correctly yields v_r = 0\n",
" #loc1=pi/2-def1 #This correctly yields \"[aim1 == 0]\"\n",
" loc1=arglat_direct\n",
" #loc1=0.0 #0 #This should also yield an aim1 expression (e.g. aim1 = -inc1) that doesn't depend on the hyperbolic deflection angle def1, because this position is already in the ecliptic plane so the deflection angle shouldn't matter there as long as the velocity vector is aimed down/up by an angle of \"inc1\" to put the velocity vector in the ecliptic plane as soon as it's released.\n",
" #aim1=-inc1\n",
" p_test1 = rotateX(inc1)*rotateZ(loc1)*p_test0 #This is the last position vector to be calculated, all changes after this point are to the velocity vectors.\n",
" v_test1 = rotateX(inc1)*rotateZ(loc1)*v_test0 #This is the velocity of the moon at the location loc1 where it throws.\n",
" if is_numeric(p_test1): p_test1 = p_test1.n()\n",
" if is_numeric(v_test1): v_test1 = v_test1.n()\n",
" if(verbose_direct): print 'p_test1 =',p_test1\n",
" #print 'v_test1 =',v_test1\n",
" v_r1 = (p_test1.dot_product(v_test1)/p_test1.norm()) #Radial velocity\n",
" if hasattr(v_r1, \"__simplify_full__\"): v_r1 = v_r1.simplify_full()\n",
" #print 'v_r1 =',v_r1\n",
" #v_test2 = rotateV(p_test1,aim1)*((speed_moon1+vtip1)/v_test1.norm())*v_test1 #Aim rotates about the position vector so it doesn't change the radial velocity.\n",
" v_test2 = rotateX(inc1)*rotateZ(loc1+def1)*rotateX(aim1)*v_test0\n",
" v_test2b = rotateX(inc1)*rotateZ(loc1)*rotateX(aim1)*v_test0\n",
" if is_numeric(v_test2): v_test2 = v_test2.n()\n",
" #print 'v_test2 =',v_test2\n",
" v_r2 = (p_test1.dot_product(v_test2)/p_test1.norm()) #Radial velocity\n",
" if hasattr(v_r2, \"__simplify_full__\"): v_r2 = v_r2.simplify_full()\n",
" #print 'v_r2 =',v_r2\n",
" v_test3 = rotateV(p_test1.cross_product(v_test2),def1)*v_test2b #Hyperbolic deflection rotates about the angular momentum vector h = r X v.\n",
" if is_numeric(v_test3): v_test3 = v_test3.n()\n",
" #print 'v_test3 =',v_test3\n",
" #compare_vectors(v_test2,v_test3,num_flag)#The last value is an optional numeric flag. 0/1 = symbolic/numeric.\n",
" #v_r3 = (p_test1.dot_product(v_test3)/p_test1.norm()) #Radial velocity\n",
" #if hasattr(v_r3, \"__simplify_full__\"): v_r3 = v_r3.simplify_full()\n",
" #print 'v_r3 =',v_r3\n",
" v_test4 = rotateV(p_test1,aim1)*v_test1\n",
" #print 'v_test4 =',v_test4\n",
" v_test5 = rotateV(p_test1.cross_product(v_test4),def1)*v_test4\n",
" #print 'v_test5 =',v_test5\n",
" aim_soln = find_root(v_test5[2],-pi/2,pi/2)\n",
" #eq_aim = v_test2[2] == 0\n",
" #print 'Equation for aim:',eq_aim\n",
" #aim_soln = solve(eq_aim,aim1)\n",
" #print aim_soln#[0].simplify_full()\n",
" #aim_soln = solve(eq_aim.subs(loc1=pi/2-def1),aim1)\n",
" if(verbose_direct): print 'aim_soln =',aim_soln\n",
" #Position and velocity of the moon when the direct moon sling throws.\n",
" p_moon2 = p_test1\n",
" v_moon2 = v_test1\n",
" if(verbose_direct): print moon_name+'\\'s velocity vector at throw time is aimed at',(restrict_rad(atan2(v_moon2[1],v_moon2[0]))*180/pi).n(),'degrees longitude, relative to',moon_name+'\\'s ascending node.'\n",
" aim_direct = aim_soln\n",
" aims_direct += [(aim_direct*180/pi).n()] #These are in degrees because their only purpose is to be plotted.\n",
" v_direct = rotateX(inc_moon)*rotateZ(arglat_direct)*rotateX(aim_direct)*v_moon0\n",
" v_direct *= (speed_moon+v_tip_direct_moon_best_case)/v_direct.norm()\n",
" if(verbose_direct): print 'Payload\\'s velocity vector at throw time is aimed at',(restrict_rad(atan2(v_direct[1],v_direct[0]))*180/pi).n(),'degrees longitude, relative to',moon_name+'\\'s ascending node.'\n",
" delta_v_direct = v_direct - v_moon2\n",
" deltavees_direct += [delta_v_direct.norm().n()]\n",
" tether_mass_ratios_direct += [tether_mass_ratio(delta_v_direct.norm().n()/v_c)] #Direct moon sling always uses default characteristic velocity.\n",
" kepler_test4 = xyz2kepler(p_test1,v_direct)\n",
" kepler_test_copy = copy.deepcopy(kepler_test4) #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
" kepler_test_copy[5] = long_time\n",
" pD_far,vD_far = kepler2xyz(kepler_test_copy)\n",
" payload_vinfs_direct += [vD_far]\n",
" payload_lon_direct = restrict_rad(atan2(vD_far[1],vD_far[0])) #Longitude of the payload's outgoing asymptote, relative to the moon's ascending node. Sagemath expects arctan2(y,x).\n",
" payload_lons_direct += [(payload_lon_direct*180/pi).n()] #These are in degrees because their only purpose is to be plotted.\n",
" if(verbose_direct):\n",
" print 'Payload\\'s velocity vector after',(long_time/(year)).n(digits=summary_digits),'years:',vD_far\n",
" print 'Payload\\'s velocity vector is aimed at',(payload_lon_direct*180/pi).n(),'degrees longitude, relative to',moon_name+'\\'s ascending node.'\n",
" print 'Numeric deflection angle:'\n",
" compare_vectors(v_direct,vD_far)\n",
" if abs(loopoff-pi/2) < tolerance: #If best_case_offset or one of the plotting offsets equals pi/2, this will run several times. That's okay.\n",
" v_tip_direct_moon_worst_case = delta_v_direct.norm().n()\n",
" v_tip_direct_moon = delta_v_direct.norm().n() #This will be assigned repeatedly, but the last one is the actual requested offset anyway.\n",
" if(verbose_direct):\n",
" print moon_name+'\\'s argument of latitude at throw (degrees)'.ljust(lj),'=',(arglat_direct*180/pi).n()\n",
" print 'aim_direct =',(aim_direct*180/pi).n(),'degrees.'\n",
" print 'delta_v_direct =',delta_v_direct.n()\n",
" print 'delta_v_direct.norm() =',delta_v_direct.norm().n()\n",
"\n",
"#Discard the first (worst case) and last (requested offset) values to leave only values for the plots.\n",
"if numpoints_plot_direct > 0: \n",
" arglats_direct = arglats_direct[1:-1]\n",
" aims_direct = aims_direct[1:-1]\n",
" deltavees_direct = deltavees_direct[1:-1]\n",
" payload_vinfs_direct = payload_vinfs_direct[1:-1]\n",
" payload_lons_direct = payload_lons_direct[1:-1]\n",
" tether_mass_ratios_direct = tether_mass_ratios_direct[1:-1]\n",
"print '\\nFinished.'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 1 graphics primitive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"Graphics object consisting of 1 graphics primitive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\n",
"text/plain": [
"Graphics object consisting of 1 graphics primitive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"Graphics object consisting of 1 graphics primitive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\n",
"text/plain": [
"Graphics object consisting of 1 graphics primitive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maximum v-inf magnitude (m/s) = 2640.00000614503\n",
"Minimum v-inf magnitude (m/s) = 2640.00000614502\n",
"Maximum v-inf out of ecliptic magnitude (m/s) = 0.0000161244219599051\n",
"v_tip_direct_moon_worst_case = 2165.14916665712\n",
"OLD CODE v_tip_direct_moon_worst_case_OLD = 2154.16953389270\n"
]
}
],
"source": [
"#These plots are in a separate cell so they can be reformatted without recomputing all those values.\n",
"if numpoints_plot_direct > 0:\n",
" x_axis_data = arglats_direct ; x_axis_title = moon_name+'\\'s arglat at throw time (degrees)'\n",
" #x_axis_data = payload_lons_direct ; x_axis_title = 'Payload\\'s longitude after '+str((long_time/year).n(digits=summary_digits))+' years (degrees)'\n",
" labels_direct = []\n",
" plots_direct = []\n",
" labels_direct += ['Aim angle (degrees)']\n",
" plots_direct += [line(zip(x_axis_data, aims_direct), rgbcolor='green')]#, legend_label = labels_direct[-1])]\n",
" labels_direct += ['Delta-v (m/s)']\n",
" plots_direct += [line(zip(x_axis_data, deltavees_direct), rgbcolor='red')]#, legend_label = labels_direct[-1])]\n",
" labels_direct += ['Tether mass ratio']\n",
" plots_direct += [line(zip(x_axis_data, tether_mass_ratios_direct), rgbcolor='red')]#, legend_label = labels_direct[-1])]\n",
" labels_direct += ['Payload longitude (degrees)']\n",
" plots_direct += [line(zip(x_axis_data, payload_lons_direct), rgbcolor='green')]#, legend_label = labels_direct[-1])]\n",
" #labels_direct += ['V-inf magnitude (m/s)'] #Too similar!\n",
" #plots_direct += [line(zip(x_axis_data, [x.norm() for x in payload_vinfs_direct]), rgbcolor='green')]#, legend_label = labels_direct[-1])]\n",
" labels_direct += ['V-inf component out of ecliptic (m/s)']\n",
" plots_direct += [line(zip(x_axis_data, [x[2] for x in payload_vinfs_direct]), rgbcolor='green')]#, legend_label = labels_direct[-1])]\n",
"\n",
" #pt3 = line([(target_c_rad,0),(target_c_rad,0.5*v_infs[mis[j]]^2/1000.0)])\n",
" if textonly == 0:\n",
" for j in range(len(labels_direct)): show(plots_direct[j], axes=true, frame=True, gridlines=false, figsize=6, axes_labels=[x_axis_title,labels_direct[j]])\n",
" else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'\n",
"\n",
"print 'Maximum v-inf magnitude (m/s) =',max([x.norm() for x in payload_vinfs_direct])\n",
"print 'Minimum v-inf magnitude (m/s) =',min([x.norm() for x in payload_vinfs_direct])\n",
"print 'Maximum v-inf out of ecliptic magnitude (m/s) =',max([abs(x[2]) for x in payload_vinfs_direct])\n",
"print 'v_tip_direct_moon_worst_case =',v_tip_direct_moon_worst_case.n()\n",
"\n",
"#OLD CODE. This was just a guess at the worst case.\n",
"v_i = vector([speed_moon*cos(inc_moon),0,speed_moon*sin(inc_moon)]).n()\n",
"v_f = vector([speed_moon+v_tip_direct_moon_best_case,0,0]).n()\n",
"delta_v = v_f - v_i\n",
"v_tip_direct_moon_worst_case_OLD = delta_v.norm().n()\n",
"print 'OLD CODE v_tip_direct_moon_worst_case_OLD =',v_tip_direct_moon_worst_case_OLD"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 9. Print the moon sling's payload and counterbalance orbits. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Payload is thrown IN at Mars so it goes into the inclined capture orbit:\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (23463.2000000000, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (-0.198139897653759, 0.723732750843447, 0.000000000000000)\n",
"Radial distance (km) = 23463.2000000000\n",
"Speed (km/s) = 0.750365586688025\n",
"Radial velocity (km/s) = -0.198139897653759\n",
"Flight path angle (degrees) = 15.3109755656728\n",
"Orbital period (hours) = 13.7774796305327\n",
"Specific relative angular momentum vector = (0.000000000000000, -0.000000000000000, 1.69810862795900e10)\n",
"Specific relative angular momentum (m^2/s) = 1.69810862795900e10\n",
"Specific orbital energy (kJ/kg) = -1543.81798121506\n",
"Eccentricity vector = (-0.713046088715551, 0.0785607927055736, 0.000000000000000)\n",
"Eccentricity = 0.717360803768281 , Alt. calc. = 0.717360803768281\n",
"Semi-latus rectum (km) = 6732.85701124928 , Alt. calc. = 6732.85701124928\n",
"Semi-minor axis (km) = 9663.89991054698\n",
"Semi-major axis (km) = 13870.9260162561\n",
"Periapsis altitude (km) = 524.467380224270\n",
"Periapsis (km) = 3920.46738022427\n",
"Apoapsis altitude (km) = 20425.3846522880\n",
"Apoapsis (km) = 23821.3846522880\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 173.712720193813 , Alt. calc. = 173.712720193813\n",
"Mean anomaly (degrees) = 206.348450132754\n",
"Eccentric anomaly (degrees) = 195.419880880776\n",
"True anomaly (degrees) = 186.287279806187 , Alt. calc. = 186.287279806187\n",
"Time since periapsis (hours) = 7.89711546804449\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"\n",
"Counterbalance is 4.0000 times as massive as the payload. It's thrown OUT to another inclined orbit:\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (23463.2000000000, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (0.0495349744134398, 1.31597403519750, 0.781898978749973)\n",
"Radial distance (km) = 23463.2000000000\n",
"Speed (km/s) = 1.53153758947483\n",
"Radial velocity (km/s) = 0.0495349744134398\n",
"Flight path angle (degrees) = 1.85345762980858\n",
"Orbital period (hours) = 50.1365753583964\n",
"Specific relative angular momentum vector = (0.000000000000000, -1.83458521182064e10, 3.08769619826459e10)\n",
"Specific relative angular momentum (m^2/s) = 3.59159723691404e10\n",
"Specific orbital energy (kJ/kg) = -652.538544070710\n",
"Eccentricity vector = (0.283679096133375, -0.0357120647312778, -0.0212186762015015)\n",
"Eccentricity = 0.286704400681545 , Alt. calc. = 0.286704400681544\n",
"Semi-latus rectum (km) = 30119.2193683966 , Alt. calc. = 30119.2193683966\n",
"Semi-minor axis (km) = 31439.0595360450\n",
"Semi-major axis (km) = 32816.7357998695\n",
"Periapsis altitude (km) = 20012.0332300433\n",
"Periapsis (km) = 23408.0332300433\n",
"Apoapsis altitude (km) = 38829.4383696957\n",
"Apoapsis (km) = 42225.4383696957\n",
"Inclination (degrees) = 30.7170827436030\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 351.669182294540 , Alt. calc. = 351.669182294542\n",
"Mean anomaly (degrees) = 4.43133606841860\n",
"Eccentric anomaly (degrees) = 6.20760350695309\n",
"True anomaly (degrees) = 8.33081770545808 , Alt. calc. = 8.33081770545961\n",
"Time since periapsis (hours) = 0.617144485368470\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"time_from_moon_to_capture_periapsis = 352.82 minutes.\n",
"time_from_capture_periapsis_to_moon = 473.83 minutes.\n"
]
}
],
"source": [
"set_verbose(2)\n",
"if cbrs[1] < tolerance:\n",
" #If cbrs[1]=0, send the payload directly into the specified hyperbolic escape trajectory:\n",
" delta_v_from_moon = v_tip_direct_moon\n",
" print 'Payload is thrown directly from',moon_name,'into a hyperbolic trajectory:'\n",
" v_ms_1 = v_moon2+delta_v_direct\n",
" p_ms_1 = p_moon2\n",
" keplers[0][0] = xyz2kepler(p_ms_1,v_ms_1)\n",
" throw_tpers[0][0] = keplers[0][0][5]\n",
"\n",
" #Describe the payload's outgoing hyperbolic asymptote.\n",
" keplers_copy = copy.deepcopy(keplers) #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
" keplers_copy[0][0][5] = long_time\n",
" pD_far,vD_far = kepler2xyz(keplers_copy[0][0])\n",
" payload_lon_direct = restrict_rad(atan2(vD_far[1],vD_far[0])) #Longitude of the payload's outgoing asymptote, relative to the moon's ascending node. Sagemath expects arctan2(y,x).\n",
" print 'Payload\\'s velocity vector after',(keplers_copy[0][0][5]/(3600*24*365.25)).n(digits=summary_digits),'years:',vD_far\n",
" print 'Payload\\'s velocity vector is aimed at',(payload_lon_direct*180/pi).n(),'degrees longitude, relative to',moon_name+'\\'s ascending node.'\n",
" payload_lon_string = moon_name+'\\'s argument of latitude at throw time is '+str((arglat_direct*180/pi).n(digits=summary_digits))+' degrees, '\n",
" payload_lon_string += 'which is offset from the best case by '+str((best_case_offset*180/pi).n(digits=summary_digits))+' degrees. '\n",
" payload_lon_string += 'The payload\\'s longitude after '+str((long_time/year).n(digits=summary_digits))+' years is '+str((payload_lon_direct*180/pi).n(digits=summary_digits))+' degrees, '\n",
" payload_lon_string += 'and its residual velocity out of the ecliptic plane is '+str((vD_far[2]).n(digits=summary_digits))+' m/s.'\n",
" print '\\n'+payload_lon_string+'\\n'\n",
"\n",
" print '\\n\\nCounterbalance is',cbrs[0].n(digits=summary_digits),'times as massive as the payload:'\n",
" v_ms_2 = v_moon2-delta_v_direct/cbrs[0]\n",
" p_ms_2 = p_moon2 #Approximate the moon sling's counterbalance and payload as though they come from the same point. For more accuracy, calculate the angle of the plane the moon sling swings in, then use that to calculate the release positions. But if you're gonna do that, might as well also stop assuming the moons are in circular orbits...\n",
" keplers[0][1] = xyz2kepler(p_ms_2,v_ms_2)\n",
" throw_tpers[0][1] = keplers[0][1][5]\n",
"else:\n",
" #If cbrs[1]>0, send the payload+balance to capture orbit sling:\n",
" delta_v_from_moon = delta_v_moon_to_capture\n",
" if throw_in > 0: print 'Payload is thrown IN at',pname,'so it goes into the inclined capture orbit:'\n",
" else: print 'Payload is thrown OUT at',pname,'so it goes into the inclined capture orbit:'\n",
" v_ms_1 = v_moon1 + delta_v_m2c_vec\n",
" p_ms_1 = p_moon1\n",
" keplers[0][0] = xyz2kepler(p_ms_1,v_ms_1)\n",
" throw_tpers[0][0] = keplers[0][0][5]\n",
"\n",
" if throw_in > 0: print '\\n\\nCounterbalance is',cbrs[0].n(digits=summary_digits),'times as massive as the payload. It\\'s thrown OUT to another inclined orbit:'\n",
" else: print '\\n\\nCounterbalance is',cbrs[0].n(digits=summary_digits),'times as massive as the payload. It\\'s thrown IN to another inclined orbit:'\n",
" v_ms_2 = v_moon1 - delta_v_m2c_vec/cbrs[0]\n",
" p_ms_2 = p_moon1 #Approximate the moon sling's counterbalance and payload as though they come from the same point. For more accuracy, calculate the angle of the plane the moon sling swings in, then use that to calculate the release positions. But if you're gonna do that, might as well also stop assuming the moons are in circular orbits...\n",
" keplers[0][1] = xyz2kepler(p_ms_2,v_ms_2)\n",
" throw_tpers[0][1] = keplers[0][1][5]\n",
" print\n",
"\n",
" if throw_tpers[0][0] < 0: time_from_moon_to_capture_periapsis = -throw_tpers[0][0] #The negative of \"time since periapsis\".\n",
" else: time_from_moon_to_capture_periapsis = -throw_tpers[0][0] + 2*pi*sqrt(keplers[0][0][0]^3/mu) #Time until next periapsis.\n",
" #print 'throw_tpers[0][0]/60 =',throw_tpers[0][0]/60,'minutes.'\n",
" print 'time_from_moon_to_capture_periapsis =',(time_from_moon_to_capture_periapsis/60).n(digits=summary_digits),'minutes.'\n",
" time_from_capture_periapsis_to_moon = period_capture - time_from_moon_to_capture_periapsis\n",
" print 'time_from_capture_periapsis_to_moon =',(time_from_capture_periapsis_to_moon/60).n(digits=summary_digits),'minutes.'\n",
" if abs(period_capture - 2*pi*sqrt(keplers[0][0][0]^3/mu)) > tolerance:\n",
" print warnstring,'Requested capture orbit period',period_capture/units,uname,'doesn\\'t match calculated capture period',(2*pi*sqrt(keplers[0][0][0]^3/mu)/units).n(),uname+'.'\n",
" print failure"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 10. Solve for the ballast mass needed to keep an asymmetric sling's center of mass at the rotational axis. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sling area at hub = pmass*v_tip^2*e^(v_tip^2/vel_c^2)/(l*sgma)\n",
"Sling area at tip = pmass*v_tip^2/(l*sgma)\n",
"cbrs[0] = 4.00000000000000\n",
"cbrs[1] = 0.992363104788\n",
"cbrs[2] = 0.917526189989\n",
"balance1_hub_area = pm1*r*w^2*e^(r^2*w^2/vlc^2)/sig\n",
"balance2_hub_area = pm2*r*w^2*e^(r^2*w^2/(cbr^2*vlc^2))/(cbr*sig)\n",
"\n",
"Solution 1: Add \"ballast\" mass at tip that isn't ever released which makes the sling cross sectional areas (and therefore the forces) equal at the hub.\n",
"balance_factor = cbr*e^(r^2*w^2/vlc^2 - r^2*w^2/(cbr^2*vlc^2))\n",
"\n",
"#Solution 2: Divide the shorter arm's design's characteristic velocity by factor=cbr. This spreads the mass out over the sling, it's not just at the tip.\n",
"balance2_reinforced_hub_area = pm2*r*w^2*e^(r^2*w^2/vlc^2)/(cbr*sig)\n",
"solnbalance2 = [\n",
"pm2 == cbr*pm1\n",
"]\n",
"mass of capture sling A1 (tons) = 0.358497023470001\n",
"mass of capture sling B1 (tons) = 0.479357317433260\n",
"mass of direct moon sling (tons) = 1.71348638694640\n",
"mass of direct moon sling (WORST CASE - tons) = 2.44039888464366\n",
"\n",
"mass of capture sling A's counterbalance arm (tons) = 0.361020967138970\n",
"mass of capture sling B's counterbalance arm (tons) = 0.517369339614712\n"
]
}
],
"source": [
"if cbrs[1] > tolerance:\n",
" #Skip moon sling (index 0) grapples for now- its required tip acceleration hasn't yet been calculated.\n",
" #Also skip the moment ballast sling (index 3) because its payload hasn't yet been calculated.\n",
" for j in range(1,3): #range(n1,n2) goes up to n2-1.\n",
" payload_masses [j][0] = payload_mass\n",
" payload_masses [j][1] = payload_mass*cbrs[j]\n",
" grapple_masses [j][0] = payload_masses[j][0]*grapple_fraction*(omegas[j]^2*radii[j]/g)\n",
" grapple_masses [j][1] = payload_masses[j][1]*grapple_fraction*(omegas[j]^2*radii[j]/cbrs[j]/g)\n",
" tip_masses_empty[j][0] = grapple_masses[j][0]\n",
" tip_masses_full [j][0] = grapple_masses[j][0] + payload_masses[j][0]\n",
" tip_masses_empty[j][1] = grapple_masses[j][1]\n",
" tip_masses_full [j][1] = grapple_masses[j][1] + payload_masses[j][1]\n",
"\n",
"#Total cross sectional area of sling at distance \"x\" (in meters) from the hub, which is hopefully also the center of mass.\n",
"#See eq 10 from Puig-Suari et al. 1995: http://web.archive.org/web/20171006015505/https://engineering.purdue.edu/people/james.m.longuski.1/JournalArticles/1995/ATetherSlingforLunarandInterplanetaryExploration.pdf\n",
"#Depends on:\n",
"# x : distance from center of mass (also called the \"hub\") in meters\n",
"# l : radius of the sling in meters\n",
"# v_tip : tip velocity in m/s\n",
"# vel_c : characteristic velocity of the sling material in m/s - doesn't overwrite the actual v_c\n",
"# sgma : tensile strength of the sling material in Pa - missing the \"i\" so it doesn't overwrite the actual \"sigma\"\n",
"# pmass : mass (in kg) of everything attached to the end of the sling, including grapple - doesn't overwrite actual \"payload_mass\"\n",
"sling_area(x,l,v_tip,vel_c,sgma,pmass) = pmass*v_tip^2/(sgma*l)*exp((v_tip/vel_c)^2*(1-x^2/l^2)) #Total cross sectional area of sling, in m^2.\n",
"sling_diameter(x,l,v_tip,vel_c,sgma,pmass) = 2*sqrt(sling_area(x,l,v_tip,vel_c,sgma,pmass)/pi) #Equivalent cross sectional diameter, in meters.\n",
"print 'Sling area at hub =',sling_area(0,l,v_tip,vel_c,sgma,pmass)\n",
"print 'Sling area at tip =',sling_area(l,l,v_tip,vel_c,sgma,pmass)\n",
"\n",
"print 'cbrs[0] =',cbrs[0]\n",
"if cbrs[1] > tolerance:\n",
" print 'cbrs[1] =',cbrs[1]\n",
" print 'cbrs[2] =',cbrs[2]\n",
"var('r w vlc sig pm1 pm2 cbr')\n",
"assume(r>0,w>0,vlc>0,sig>0,pm1>0,pm2>0,cbr>0)\n",
"balance1_hub_area = sling_area(0,r,w*r,vlc,sig,pm1)\n",
"print 'balance1_hub_area =',balance1_hub_area\n",
"balance2_hub_area = sling_area(0,r/cbr,w*r/cbr,vlc,sig,pm2)\n",
"print 'balance2_hub_area =',balance2_hub_area\n",
"\n",
"print '\\nSolution 1: Add \\\"ballast\\\" mass at tip that isn\\'t ever released which makes the sling cross sectional areas (and therefore the forces) equal at the hub.'\n",
"eqbalance1 = balance1_hub_area == balance2_hub_area\n",
"solnbalance1 = solve(eqbalance1,pm2)\n",
"#print 'solnbalance1 = ',solnbalance1\n",
"balance_factor(r,w,vlc,sig,cbr) = (solnbalance1[0].rhs()/pm1).simplify_full() #Divide by pm1 to get the correct ratio of pm2 (counterbalance tip mass) to pm1 (payload tip mass).\n",
"print 'balance_factor =',balance_factor(r,w,vlc,sig,cbr) #more compact: cbr*e^((r*w/vlc)^2*(1-1/cbr^2)) , where r*w = payload tip speed\n",
"\n",
"print '\\n#Solution 2: Divide the shorter arm\\'s design\\'s characteristic velocity by factor=cbr. This spreads the mass out over the sling, it\\'s not just at the tip.'\n",
"balance2_reinforced_hub_area = sling_area(0,r/cbr,w*r/cbr,vlc/cbr,sig,pm2)\n",
"print 'balance2_reinforced_hub_area =',balance2_reinforced_hub_area\n",
"eqbalance2 = balance1_hub_area == balance2_reinforced_hub_area\n",
"solnbalance2 = solve(eqbalance2,pm2)\n",
"print 'solnbalance2 = ',solnbalance2\n",
"\n",
"if ballast_choice == 0: #Totally ignore balancing problem. Set all ballast masses to zero and set all characteristic velocities to default.\n",
" for j in range(num_slings): ballast_masses[j] = [0,0] ; v_cs[j] = [v_c,v_c]\n",
"elif ballast_choice == 1: #Set all characteristic velocities to default, put extra ballast mass on the tips of the shorter slings.\n",
" for j in range(num_slings): v_cs[j] = [v_c,v_c]\n",
" for j in range(1,3): #Skip moon sling and moment ballast sling.\n",
" if cbrs[j] > tolerance:\n",
" if cbrs[j] > 1:\n",
" ballast_masses[j][0] = 0\n",
" ballast_masses[j][1] = tip_masses_full[j][0]*balance_factor(radii[j],omegas[j],v_cs[j][0],sigma,cbrs[j]) - tip_masses_full[j][1]\n",
" else: #Reverse balance_factor: use radius/cbr and 1/cbr instead.\n",
" ballast_masses[j][1] = 0\n",
" ballast_masses[j][0] = tip_masses_full[j][1]*balance_factor(radii[j]/cbrs[j],omegas[j],v_cs[j][1],sigma,1/cbrs[j]) - tip_masses_full[j][0]\n",
" tip_masses_empty[j][0] += ballast_masses[j][0]\n",
" tip_masses_full [j][0] += ballast_masses[j][0]\n",
" tip_masses_empty[j][1] += ballast_masses[j][1]\n",
" tip_masses_full [j][1] += ballast_masses[j][1]\n",
" #That ballast mass is actually the total mass which includes grapple mass. Total = (1+f)*bm, so gm = bm*f/(1+f) and bm /= 1+f .\n",
" #There are three sections in this notebook which make this correction. This is #1.\n",
" grapple_masses [j][0] += ballast_masses[j][0]*grapple_fraction*(omegas[j]^2*radii[j]/g)/(1+grapple_fraction*(omegas[j]^2*radii[j]/g))\n",
" ballast_masses [j][0] /= 1+grapple_fraction*(omegas[j]^2*radii[j]/g)\n",
" grapple_masses [j][1] += ballast_masses[j][1]*grapple_fraction*(omegas[j]^2*radii[j]/cbrs[j]/g)/(1+grapple_fraction*(omegas[j]^2*radii[j]/cbrs[j]/g))\n",
" ballast_masses [j][1] /= 1+grapple_fraction*(omegas[j]^2*radii[j]/cbrs[j]/g)\n",
"elif ballast_choice == 2: #Set all ballast masses to zero, divide characteristic velocity of shorter slings by cbrs[j].\n",
" for j in range(num_slings):\n",
" ballast_masses[j] = [0,0]\n",
" if cbrs[j] > tolerance:\n",
" if cbrs[j] > 1: v_cs[j][0] = v_c; v_cs[j][1] = v_c/cbrs[j];\n",
" else: v_cs[j][0] = v_c*cbrs[j]; v_cs[j][1] = v_c; #Multiply by cbr because *payload* v_c needs to be divided by 1/cbr.\n",
"else: print '!!!WARNING!!! ballast_choice',ballast_choice,'isn\\'t recognized!'\n",
"\n",
"mass_ratio_direct_moon = tether_mass_ratio(v_tip_direct_moon/v_c) #Direct moon sling always uses default characteristic velocity.\n",
"direct_moon_radius = v_tip_direct_moon^2/tip_accel_max\n",
"direct_moon_omega = v_tip_direct_moon/direct_moon_radius #Angular rotation rate of the direct moon sling.\n",
"if cbrs[1] > tolerance:\n",
" tether_masses[1][0] = (tip_masses_full[1][0])*tether_mass_ratio(radii[1]*omegas[1]/v_cs[1][0])\n",
" tether_masses[2][0] = (tip_masses_full[2][0])*tether_mass_ratio(radii[2]*omegas[2]/v_cs[2][0])\n",
"direct_moon_tether_mass = payload_mass*(1+grapple_fraction*(direct_moon_omega^2*direct_moon_radius/g))*mass_ratio_direct_moon #Direct moon sling doesn't use ballast.\n",
"if cbrs[1] > tolerance:\n",
" print 'mass of capture sling A1 (tons) =',(tether_masses[1][0]/1000.0).n()\n",
" print 'mass of capture sling B1 (tons) =',(tether_masses[2][0]/1000.0).n()\n",
"print 'mass of direct moon sling (tons) =',(direct_moon_tether_mass/1000.0).n()\n",
"\n",
"mass_ratio_direct_moon_worst_case = tether_mass_ratio(v_tip_direct_moon_worst_case/v_c) #Direct moon sling always uses default characteristic velocity.\n",
"direct_moon_radius_worst_case = v_tip_direct_moon_worst_case^2/tip_accel_max\n",
"direct_moon_omega_worst_case = v_tip_direct_moon_worst_case/direct_moon_radius_worst_case #Angular rotation rate of the direct moon sling, worst case.\n",
"print 'mass of direct moon sling (WORST CASE - tons) =',(payload_mass/1000.0*(1.0+grapple_fraction*(direct_moon_omega_worst_case^2*direct_moon_radius_worst_case/g))*mass_ratio_direct_moon_worst_case).n() #Direct moon sling doesn't use ballast.\n",
"\n",
"if cbrs[1] > tolerance:\n",
" tether_masses[1][1] = (tip_masses_full[1][1])*tether_mass_ratio(radii[1]*omegas[1]/cbrs[1]/v_cs[1][1])\n",
" print '\\nmass of capture sling A\\'s counterbalance arm (tons) =',(tether_masses[1][1]/1000.0).n()\n",
" tether_masses[2][1] = (tip_masses_full[2][1])*tether_mass_ratio(radii[2]*omegas[2]/cbrs[2]/v_cs[2][1])\n",
" print 'mass of capture sling B\\'s counterbalance arm (tons) =',(tether_masses[2][1]/1000.0).n()\n",
"#debug_sling(1)\n",
"#debug_sling(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 11. Define the slings' moments of inertia and rotational kinetic energy. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished defining moment of inertia and rotational kinetic energy.\n"
]
}
],
"source": [
"radii[0] = RDF(delta_v_from_moon^2/tip_accel_max)\n",
"#if cbrs[0] < 1: radii[0] /= cbrs[0] #If payload moves slower, accel limit applies to the counterbalance arm, not the payload arm. Disabled because this is harder to fix for the coplanar configuration, so the accel limit now applies ONLY to the payload arm.\n",
"omegas[0] = RDF(delta_v_from_moon/radii[0]) #Angular rotation rate of the moon sling.\n",
"\n",
"#Moment of inertia of a single arm of the sling including payload, relative to rotation around the end of the sling with the larger area.\n",
"#See eq 20 from Puig-Suari et al. 1995: http://web.archive.org/web/20171006015505/https://engineering.purdue.edu/people/james.m.longuski.1/JournalArticles/1995/ATetherSlingforLunarandInterplanetaryExploration.pdf\n",
"#Depends on:\n",
"# l : radius of the sling in meters\n",
"# v_star : tip velocity divided by the characteristic velocity of the sling material in m/s\n",
"# pmass : mass (in kg) of everything attached to the end of the sling, including grapple\n",
"var('l v_star pmass')\n",
"assume(l>0,v_star>0,pmass>0)\n",
"sling_moment(l,v_star,pmass) = pmass*l^2*sqrt(pi)/(2*v_star)*exp(v_star^2)*erf(v_star)\n",
"\n",
"#Rotational kinetic energy of sling and payload, from equations 23 and 24 in Puig-Suari et al. 1995. http://web.archive.org/web/20171006015505/https://engineering.purdue.edu/people/james.m.longuski.1/JournalArticles/1995/ATetherSlingforLunarandInterplanetaryExploration.pdf\n",
"#When rotational energy (including payload mass \"pmass\") is divided by the sling's characteristic energy pmass*vlc^2 (where \"vlc\" is the characteristic velocity of the sling material), the resulting ratio equals one quarter the sling mass ratio.\n",
"sling_energy(v_tip,vlc,pmass) = pmass*vlc^2*tether_mass_ratio(v_tip/vlc)/4\n",
"\n",
"for j in range(1,3): #Skip moon sling and moment ballast sling.\n",
" if cbrs[j] > tolerance:\n",
" moments_full [j][0] = sling_moment(radii[j],radii[j]*omegas[j]/v_cs[j][0],tip_masses_full[j][0])\n",
" moments_full [j][1] = sling_moment(radii[j]/cbrs[j],radii[j]/cbrs[j]*omegas[j]/v_cs[j][1],tip_masses_full[j][1])\n",
" moments_empty[j][0] = moments_full[j][0] - payload_masses[j][0]*radii[j]^2\n",
" moments_empty[j][1] = moments_full[j][1] - payload_masses[j][1]*(radii[j]/cbrs[j])^2\n",
"#debug_sling(1)\n",
"#debug_sling(2)\n",
"print 'Finished defining moment of inertia and rotational kinetic energy.'"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 12. Solve for the third capture sling which zeroes the total rotational angular momentum of the capture sling system whether it's full or empty. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Capture sling A counterbalance ratio : 0.992363104788\n",
"Capture sling B counterbalance ratio : 0.917526189989\n",
"\n",
"OmegaB/OmegaA : 0.894361901311\n",
"moments_fullA/moments_fullB : 0.577420455531257\n",
"moments_emptyA/moments_emptyB : 0.496076469368486\n",
"\n",
"Capture sling C is attached to capture sling A\n",
"\n",
"Required moment of inertia for capture sling C full (kg*m^2) : 1.48461261711015e13\n",
"Required moment of inertia for capture sling C empty (kg*m^2) : 5.85776794921929e12\n",
"\n",
"Capture sling C payload mass (tons) : 0.455074003194002\n",
"Capture sling C balance mass (tons) : 0.451598650718035\n",
"Moment diff, full - empty (kg*m^2) : 8.98835822188219e12 , Alt. calc: 8.98835822188219e12 \n",
"\n",
"\n",
"Actual moment of inertia for capture sling C full (kg*m^2) : 1.48461261711015e13\n",
"Actual moment of inertia for capture sling C empty (kg*m^2) : 5.85776794921932e12\n"
]
}
],
"source": [
"if cbrs[1] < tolerance: print 'Skipping this section because capture orbit sling has been disabled.'\n",
"else:\n",
" #Moments of inertia for capture slings A and B need to have the correct ratio whether they're both full or both empty. That ratio is the inverse of the ratio between their desired final angular rotation rates at release. This way, the capture sling A+B system has zero rotational angular momentum. That means both slings can be spun up (while full) and spun down (while empty) from/to a stationary state.\n",
" lj = 61\n",
" print 'Capture sling A counterbalance ratio'.ljust(lj),':',cbrs[1]\n",
" print 'Capture sling B counterbalance ratio'.ljust(lj),':',cbrs[2]\n",
"\n",
" print '\\nOmegaB/OmegaA'.ljust(lj+1),':',omegas[2]/omegas[1]\n",
" print 'moments_fullA/moments_fullB'.ljust(lj),':',(sum(moments_full[1])/sum(moments_full[2])).n()\n",
" print 'moments_emptyA/moments_emptyB'.ljust(lj),':',(sum(moments_empty[1])/sum(moments_empty[2])).n()\n",
"\n",
" #Is moment of inertia ballast needed for full capture sling A or B?\n",
" if sum(moments_full[1])/sum(moments_full[2]) < omegas[2]/omegas[1] - tolerance: C_is_on = 1 ; C_is_not_on = 2 #Attached to capture sling A.\n",
" elif sum(moments_full[1])/sum(moments_full[2]) > omegas[2]/omegas[1] + tolerance: C_is_on = 2 ; C_is_not_on = 1 #Attached to capture sling B, not attached to capture sling A.\n",
" else: C_is_on = -1 ; C_is_not_on = -1 #Capture sling C is disabled.\n",
" if C_is_on >= 0:\n",
" print '\\n',cap1st(descs[3]),'is attached to',descs[C_is_on]\n",
" mis[3] = mis[C_is_on]\n",
" cbrs [3] = cbrs[C_is_on]\n",
" orig_cbrs [3] = orig_cbrs[C_is_on]\n",
" radii [3] = radii[C_is_on]\n",
" omegas [3] = omegas[C_is_on]\n",
" throw_times[3] = throw_times[C_is_on]\n",
" theta0s [3] = theta0s[C_is_on]\n",
" throw_tpers[3][0] = throw_tpers[C_is_on][0]\n",
" throw_tpers[3][1] = throw_tpers[C_is_on][1]\n",
" throw_pos [3][0] = throw_pos[C_is_on][0]\n",
" throw_pos [3][1] = throw_pos[C_is_on][1]\n",
" throw_vel [3][0] = throw_vel[C_is_on][0]\n",
" throw_vel [3][1] = throw_vel[C_is_on][1]\n",
"\n",
" ballast_moment_full = omegas[C_is_not_on]/omegas[C_is_on]*sum(moments_full[C_is_not_on]) - sum(moments_full[C_is_on])\n",
" if sum(moments_empty[1])/sum(moments_empty[2]) < omegas[2]/omegas[1]:\n",
" if C_is_on != 1: print '!!!WARNING!!! Moment of inertia ballast is needed for empty capture sling A, but based on the full moments C_is_on =',C_is_on\n",
" else:\n",
" if C_is_on != 2: print '!!!WARNING!!! Moment of inertia ballast is needed for empty capture sling B, but based on the full moments C_is_on =',C_is_on\n",
" ballast_moment_empty = omegas[C_is_not_on]/omegas[C_is_on]*sum(moments_empty[C_is_not_on]) - sum(moments_empty[C_is_on])\n",
"\n",
" print '\\nRequired moment of inertia for capture sling C full (kg*m^2)'.ljust(lj+1),':',(ballast_moment_full).n()\n",
" print 'Required moment of inertia for capture sling C empty (kg*m^2)'.ljust(lj),':',(ballast_moment_empty).n()\n",
" payload_masses[3][0] = (ballast_moment_full-ballast_moment_empty)/(radii[3]^2 + cbrs[3]*(radii[3]/cbrs[3])^2)\n",
" payload_masses[3][1] = payload_masses[3][0]*cbrs[3]\n",
" print '\\nCapture sling C payload mass (tons)'.ljust(lj+1),':',(payload_masses[3][0]/1000.0).n()\n",
" print 'Capture sling C balance mass (tons)'.ljust(lj),':',(payload_masses[3][1]/1000.0).n()\n",
" moment_payload_only = payload_masses[3][0]*radii[3]^2\n",
" moment_balance_only = payload_masses[3][1]*(radii[3]/cbrs[3])^2\n",
" print 'Moment diff, full - empty (kg*m^2)'.ljust(lj),':',(ballast_moment_full-ballast_moment_empty).n(),', Alt. calc: ',(moment_payload_only + moment_balance_only).n(),'\\n'\n",
"\n",
" var('mmass1')\n",
" if cbrs[3] > 1:\n",
" eq_moment = ballast_moment_full == sling_moment(radii[3],radii[3]*omegas[3]/v_cs[3][0],mmass1+payload_masses[3][0]) + sling_moment(radii[3]/cbrs[3],radii[3]/cbrs[3]*omegas[3]/v_cs[3][1],(mmass1+payload_masses[3][0])*balance_factor(radii[3],omegas[3],v_cs[3][0],sigma,cbrs[3]))\n",
" soln_moment = solve(eq_moment,mmass1)\n",
" tip_masses_full [3][0] = soln_moment[0].rhs()+payload_masses[3][0].n() #Includes grapples and ballast.\n",
" tip_masses_full [3][1] = tip_masses_full[3][0]*balance_factor(radii[3],omegas[3],v_cs[3][0],sigma,cbrs[3]) #Includes grapples and ballast.\n",
" else:\n",
" eq_moment = ballast_moment_full == sling_moment(radii[3],radii[3]*omegas[3]/v_cs[3][0],(mmass1+payload_masses[3][1])*balance_factor(radii[3]/cbrs[3],omegas[3],v_cs[3][0],sigma,1/cbrs[3])) + sling_moment(radii[3]/cbrs[3],radii[3]/cbrs[3]*omegas[3]/v_cs[3][1],mmass1+payload_masses[3][1]) #Reverse balance_factor: use radius/cbr and 1/cbr instead.\n",
" soln_moment = solve(eq_moment,mmass1)\n",
" tip_masses_full [3][1] = soln_moment[0].rhs()+payload_masses[3][1].n() #Includes grapples and ballast.\n",
" tip_masses_full [3][0] = tip_masses_full[3][1]*balance_factor(radii[3]/cbrs[3],omegas[3],v_cs[3][0],sigma,1/cbrs[3]) #Includes grapples and ballast.\n",
" tip_masses_empty [3][0] = tip_masses_full[3][0] - payload_masses[3][0]\n",
" tip_masses_empty [3][1] = tip_masses_full[3][1] - payload_masses[3][1]\n",
" #These are the grapple masses for the payloads/balances:\n",
" grapple_masses [3][0] = payload_masses[3][0]*grapple_fraction*(omegas[3]^2*radii[3]/g)\n",
" grapple_masses [3][1] = payload_masses[3][0]*grapple_fraction*(omegas[3]^2*radii[3]/g/cbrs[3])\n",
" #The ballast (and its grapple) is the difference:\n",
" ballast_masses [3][0] = tip_masses_empty[3][0] - grapple_masses[3][0]\n",
" ballast_masses [3][1] = tip_masses_empty[3][1] - grapple_masses[3][1]\n",
" #That ballast mass is actually the total mass which includes grapple mass. Total = (1+f)*bm, so gm = bm*f/(1+f) and bm /= 1+f .\n",
" #There are three sections in this notebook which make this correction. This is #2.\n",
" grapple_masses [3][0] += ballast_masses[3][0]*grapple_fraction*(omegas[3]^2*radii[3]/g)/(1+grapple_fraction*(omegas[3]^2*radii[3]/g))\n",
" ballast_masses [3][0] /= 1+grapple_fraction*(omegas[3]^2*radii[3]/g)\n",
" grapple_masses [3][1] += ballast_masses[3][1]*grapple_fraction*(omegas[3]^2*radii[3]/g/cbrs[3])/(1+grapple_fraction*(omegas[3]^2*radii[3]/g/cbrs[3]))\n",
" ballast_masses [3][1] /= 1+grapple_fraction*(omegas[3]^2*radii[3]/g/cbrs[3])\n",
"\n",
" tether_masses [3][0] = tip_masses_full[3][0]*tether_mass_ratio(radii[3]*omegas[3]/v_cs[3][0])\n",
" tether_masses [3][1] = tip_masses_full[3][1]*tether_mass_ratio(radii[3]/cbrs[3]*omegas[3]/v_cs[3][1])\n",
"\n",
" j=3\n",
" if cbrs[j] > tolerance:\n",
" moments_full [j][0] = sling_moment(radii[j],radii[j]*omegas[j]/v_cs[j][0],tip_masses_full[j][0])\n",
" moments_full [j][1] = sling_moment(radii[j]/cbrs[j],radii[j]/cbrs[j]*omegas[j]/v_cs[j][1],tip_masses_full[j][1])\n",
" moments_empty[j][0] = moments_full[j][0] - payload_masses[j][0]*radii[j]^2\n",
" moments_empty[j][1] = moments_full[j][1] - payload_masses[j][1]*(radii[j]/cbrs[j])^2\n",
" print '\\nActual moment of inertia for capture sling C full (kg*m^2)'.ljust(lj+1),':',(sum(moments_full[j])).n()\n",
" print 'Actual moment of inertia for capture sling C empty (kg*m^2)'.ljust(lj),':',(sum(moments_empty[j])).n()\n",
"\n",
" #debug_sling(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 13. Solve for the moon sling payload based on mt4ct and the total mass to be thrown. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"moon sling rotation period (minutes) = 8.63942295815758\n",
"moon sling rotation rate (rpm) = 0.115748471262860\n",
"mass of sling on moon which tosses payload (+ its capture sling counterbalance) directly into inclined capture orbit (tons) = 1.12524487307745\n",
"sum of masses of both slings (moon sling and JUST capture sling A) WITHOUT COUNTERBALANCE SLINGS (tons) = 1.48374189654745\n",
"fraction of original mass for direct launch from the moon WITHOUT COUNTERBALANCE SLINGS = 0.865919862480859\n",
"\n",
"mass of moon sling's counterbalance arm (tons) = 0.289063829557017\n"
]
}
],
"source": [
"#Finally, size the moon sling payload based on the total amount that needs to be sent and the value of mt4ct:\n",
"if cbrs[1] < tolerance:\n",
" payload_masses[0][0] = payload_mass\n",
" payload_masses[0][1] = payload_mass*cbrs[0]\n",
"else:\n",
" if abs(mt4ct+1) < tolerance: #If mt4ct == -1, automatically size the moon payload to match the largest of the capture payloads or capture counterbalances. This way nothing needs to be subdivided.\n",
" mt4ct = sum(sum(temp) for temp in payload_masses[1:])/max(map(max, *payload_masses[1:])) #This result follows from this equation: sum(sum(temp) for temp in payload_masses[1:])/mt4ct = max(map(max, *payload_masses[1:])) - the left hand side is the sum of all payloads and balances thrown by all the capture orbit slings, the right hand side is the largest payload or balance thrown by any of the capture orbit slings.\n",
" elif abs(mt4ct+2) < tolerance: #If mt4ct == -2, automatically size the moon payload to match the capture payload, so if the capture counterbalance is larger it will have to be subdivided.\n",
" mt4ct = (sum(sum(temp) for temp in payload_masses[1:]))/payload_masses[1][0] #This result follows from this equation: (sum(sum(temp) for temp in payload_masses[1:]))/mt4ct = payload_masses[1][0]))\n",
" elif mt4ct < tolerance: print '!!!!WARNING!!! mt4ct (\"moon throws 4 each capture throw\") =',mt4ct\n",
" payload_masses[0][0] = sum(sum(temp) for temp in payload_masses[1:])/mt4ct\n",
" payload_masses[0][1] = payload_masses[0][0]*cbrs[0]\n",
"\n",
"mt4ct = RDF(mt4ct) #Otherwise mt4ct.ceil() throws \"AttributeError: 'float' object has no attribute 'ceil'\"\n",
"\n",
"grapple_masses [0][0] = payload_masses[0][0]*grapple_fraction*(omegas[0]^2*radii[0]/g)\n",
"grapple_masses [0][1] = payload_masses[0][1]*grapple_fraction*(omegas[0]^2*radii[0]/g/cbrs[0])\n",
"\n",
"tip_masses_empty[0][0] = grapple_masses[0][0]\n",
"tip_masses_full [0][0] = grapple_masses[0][0] + payload_masses[0][0]\n",
"tip_masses_empty[0][1] = grapple_masses[0][1]\n",
"tip_masses_full [0][1] = grapple_masses[0][1] + payload_masses[0][1]\n",
"\n",
"#Only address moon sling part of ballast_choice 1; others are already finished. Couldn't derive the moon ballast mass without first deriving the moon sling's radius and omega above.\n",
"if ballast_choice == 1: #Set all characteristic velocities to default, put extra ballast mass on the tips of the shorter slings.\n",
" if cbrs[0] > 1: ballast_masses[0][0]=0; ballast_masses[0][1] = tip_masses_full[0][0]*balance_factor(radii[0],omegas[0],v_cs[0][0],sigma,cbrs[0]) - tip_masses_full[0][1]\n",
" else: ballast_masses[0][1]=0; ballast_masses[0][0] = tip_masses_full[0][1]*balance_factor(radii[0]/cbrs[0],omegas[0],v_cs[0][1],sigma,1/cbrs[0]) - tip_masses_full[0][0] #Reverse balance_factor: use radius/cbr and 1/cbr instead.\n",
"\n",
"tip_masses_empty[0][0] += ballast_masses[0][0]\n",
"tip_masses_full [0][0] += ballast_masses[0][0]\n",
"tip_masses_empty[0][1] += ballast_masses[0][1]\n",
"tip_masses_full [0][1] += ballast_masses[0][1]\n",
"\n",
"#That ballast mass is actually the total mass which includes grapple mass. Total = (1+f)*bm, so gm = bm*f/(1+f) and bm /= 1+f .\n",
"#There are three sections in this notebook which make this correction. This is #2.\n",
"grapple_masses [0][0] += ballast_masses[0][0]*grapple_fraction*(omegas[0]^2*radii[0]/g)/(1+grapple_fraction*(omegas[0]^2*radii[0]/g))\n",
"ballast_masses [0][0] /= 1+grapple_fraction*(omegas[0]^2*radii[0]/g)\n",
"grapple_masses [0][1] += ballast_masses[0][1]*grapple_fraction*(omegas[0]^2*radii[0]/g/cbrs[0])/(1+grapple_fraction*(omegas[0]^2*radii[0]/g/cbrs[0]))\n",
"ballast_masses [0][1] /= 1+grapple_fraction*(omegas[0]^2*radii[0]/g/cbrs[0])\n",
"\n",
"mass_ratio_moon1 = tether_mass_ratio(radii[0]*omegas[0]/v_cs[0][0])\n",
"tether_masses[0][0] = tip_masses_full[0][0]*mass_ratio_moon1\n",
"print 'moon sling rotation period (minutes) =',(2*pi/omegas[0]/60).n()\n",
"print 'moon sling rotation rate (rpm) =',((2*pi/omegas[0]/60)^(-1)).n()\n",
"if cbrs[1] > tolerance:\n",
" print 'mass of sling on moon which tosses payload (+ its capture sling counterbalance) directly into inclined capture orbit (tons) =',(tether_masses[0][0]/1000.0).n()\n",
" print 'sum of masses of both slings (moon sling and JUST capture sling A) WITHOUT COUNTERBALANCE SLINGS (tons) =',((tether_masses[0][0]+tether_masses[1][0])/1000.0).n()\n",
" print 'fraction of original mass for direct launch from the moon WITHOUT COUNTERBALANCE SLINGS =',((tether_masses[0][0]+tether_masses[1][0])/direct_moon_tether_mass).n()\n",
"else: print 'mass of sling on moon which tosses payload directly into hyperbolic escape trajectory (tons) =',(tether_masses[0][0]/1000.0).n()\n",
"\n",
"mass_ratio_moon2 = tether_mass_ratio(radii[0]*omegas[0]/cbrs[0]/v_cs[0][1])\n",
"tether_masses[0][1] = tip_masses_full[0][1]*mass_ratio_moon2\n",
"print '\\nmass of moon sling\\'s counterbalance arm (tons) =',(tether_masses[0][1]/1000.0).n()\n",
"j=0\n",
"if cbrs[j] > tolerance:\n",
" moments_full [j][0] = sling_moment(radii[j],radii[j]*omegas[j]/v_cs[j][0],tip_masses_full[j][0])\n",
" moments_full [j][1] = sling_moment(radii[j]/cbrs[j],radii[j]/cbrs[j]*omegas[j]/v_cs[j][1],tip_masses_full[j][1])\n",
" moments_empty[j][0] = moments_full[j][0] - payload_masses[j][0]*radii[j]^2\n",
" moments_empty[j][1] = moments_full[j][1] - payload_masses[j][1]*(radii[j]/cbrs[j])^2\n",
"\n",
"#for j in range(num_slings): debug_sling(j)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Empty and full tip masses match their ballast, grapple and payload masses.\n"
]
}
],
"source": [
"#Check that the tip_masses make sense.\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" for k in range(2): #Loop through sling arms.\n",
" if abs(tip_masses_empty[j][k] - ballast_masses[j][k] - grapple_masses[j][k]) > tolerance:\n",
" print warnstring,'tip_masses_empty[',j,'][',k,'] =',tip_masses_empty[j][k],', but ballast_mass + grapple_mass =',ballast_masses[j][k] + grapple_masses[j][k]\n",
" print failure\n",
" if abs(tip_masses_full[j][k] - ballast_masses[j][k] - grapple_masses[j][k] - payload_masses[j][k]) > tolerance:\n",
" print warnstring,'tip_masses_full[',j,'][',k,'] =',tip_masses_full[j][k],', but ballast_mass + grapple_mass + payload_mass =',ballast_masses[j][k] + grapple_masses[j][k] + payload_masses[j][k]\n",
" print failure\n",
"print 'Empty and full tip masses match their ballast, grapple and payload masses.'"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 14. Check the centers of mass of all slings while empty and full. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cbrs[0] = 4.00000000000000\n",
"cbrs[1] = 0.992363104788\n",
"cbrs[2] = 0.917526189989\n",
"\n",
"Check that equivalent sling diameter at the hub (x = 0) is equal for both arms of moon and capture slings:\n",
"\n",
"Moon sling1 tip diameter (mm) : 3.60080122699790\n",
"Moon sling1 hub diameter (mm) : 3.76293935359100\n",
"Moon sling2 hub diameter (mm) : 3.76293935359101\n",
"Moon sling2 tip diameter (mm) : 3.75259516366609\n",
"\n",
"Capture sling A1 tip diameter (mm) : 1.64362808787441\n",
"Capture sling A1 hub diameter (mm) : 1.75476136488611\n",
"Capture sling A2 hub diameter (mm) : 1.75476136488611\n",
"Capture sling A2 tip diameter (mm) : 1.64196741527596\n",
"\n",
"Capture sling B1 tip diameter (mm) : 1.68101986276946\n",
"Capture sling B1 hub diameter (mm) : 1.82429962111474\n",
"Capture sling B2 hub diameter (mm) : 1.82429962111474\n",
"Capture sling B2 tip diameter (mm) : 1.65538740718731\n",
"\n",
"Capture sling C1 tip diameter (mm) : 1.21771873859756\n",
"Capture sling C1 hub diameter (mm) : 1.30005431980189\n",
"Capture sling C2 hub diameter (mm) : 1.30005431980189\n",
"Capture sling C2 tip diameter (mm) : 1.21648839205096\n"
]
}
],
"source": [
"print 'cbrs[0] =',cbrs[0]\n",
"if cbrs[1] > tolerance:\n",
" print 'cbrs[1] =',cbrs[1]\n",
" print 'cbrs[2] =',cbrs[2]\n",
"\n",
"print '\\nCheck that equivalent sling diameter at the hub (x = 0) is equal for both arms of moon and capture slings:'\n",
"lj = 34 #Left justify descriptions by this number of characters.\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" tip_diams[j][0] = sling_diameter(radii[j],radii[j],omegas[j]*radii[j],v_cs[j][0],sigma,tip_masses_full[j][0])\n",
" hub_diams[j][0] = sling_diameter(0,radii[j],omegas[j]*radii[j],v_cs[j][0],sigma,tip_masses_full[j][0])\n",
" hub_diams[j][1] = sling_diameter(0,radii[j]/cbrs[j],omegas[j]*radii[j]/cbrs[j],v_cs[j][1],sigma,tip_masses_full[j][1])\n",
" tip_diams[j][1] = sling_diameter(radii[j]/cbrs[j],radii[j]/cbrs[j],omegas[j]*radii[j]/cbrs[j],v_cs[j][1],sigma,tip_masses_full[j][1])\n",
"\n",
" desc = '\\n'+cap1st(descs[j])+'1 tip diameter (mm)' ; print desc.ljust(lj+1),':',(tip_diams[j][0]*1000).n()\n",
" desc = cap1st(descs[j])+'1 hub diameter (mm)' ; print desc.ljust(lj),':',(hub_diams[j][0]*1000).n()\n",
" if abs(hub_diams[j][0] - hub_diams[j][1]) > tolerance: print '!!!WARNING!!! Equivalent sling diameters aren\\'t equal at the',descs[j],'hub!'\n",
" desc = cap1st(descs[j])+'2 hub diameter (mm)' ; print desc.ljust(lj),':',(hub_diams[j][1]*1000).n()\n",
" desc = cap1st(descs[j])+'2 tip diameter (mm)' ; print desc.ljust(lj),':',(tip_diams[j][1]*1000).n()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Sling moment of inertia = (l, v_star, pmass) |--> 1/2*sqrt(pi)*l^2*pmass*erf(v_star)*e^(v_star^2)/v_star\n",
"\n",
"Sling center of mass = (l*vel_c*e^(v_tip^2/vel_c^2) - l*vel_c)*e^(-v_tip^2/vel_c^2)/(sqrt(pi)*v_tip*erf(v_tip/vel_c))\n",
"\n",
"Treat \"x\" as positive for sling1 (the payload) and negative for sling2 (the counterbalance).\n",
"\n",
"Center of mass for empty moon sling (m) : -1.56718079881075e-12\n",
"Center of mass for full moon sling (m) : 0.000000000000000\n",
"\n",
"Center of mass for empty capture sling A (m) : 6.08719719972044e-12\n",
"Center of mass for full capture sling A (m) : 4.63295776665470e-12\n",
"\n",
"Center of mass for empty capture sling B (m) : -4.77935227808139e-12\n",
"Center of mass for full capture sling B (m) : -8.57266458970959e-12\n",
"\n",
"Center of mass for empty capture sling C (m) : 4.33803930189554e-12\n",
"Center of mass for full capture sling C (m) : 8.44056408431307e-12\n"
]
}
],
"source": [
"#Check the centers of mass of all slings while empty and full.\n",
"\n",
"var('x v_tip vel_c sgma ro')\n",
"assume(x>0,v_tip>0,vel_c>0,sgma>0,ro>0)\n",
"# v_c = sqrt((2*sigma)/(safety*rho))\n",
"# rho = 2*sigma/(safety*v_c^2)\n",
"#sling_area(x,l,v_tip,vel_c,sgma,pmass)\n",
"#print 'tether_mass_ratio*pmass =',tether_mass_ratio*pmass\n",
"#print 'ind integral =',integral(sling_area(x,l,v_tip,vel_c,sgma,pmass)*2*sgma/(vel_c^2),x)\n",
"integrated_tether_mass(pmass,v_tip,vel_c) = integrate(sling_area(x,l,v_tip,vel_c,sgma,pmass)*2*sgma/(vel_c^2), x, 0, l)\n",
"#print 'integrated_tether_mass(pmass,v_tip,vel_c) =',integrated_tether_mass(pmass,v_tip,vel_c)\n",
"\n",
"print '\\nSling moment of inertia =',sling_moment\n",
"#print 'ind integral =', integral(x^2*sling_area(x,l,v_tip,vel_c,sgma,pmass)*2*sgma/(vel_c^2), x)\n",
"#print 'def integral =',integrate(x^2*sling_area(x,l,v_tip,vel_c,sgma,pmass)*2*sgma/(vel_c^2), x, 0, l)\n",
"\n",
"#print '\\nSling center of mass:'\n",
"sling_center_of_mass(l,v_tip,vel_c) = integrate(x*sling_area(x,l,v_tip,vel_c,sgma,pmass)*2*sgma/(vel_c^2)/integrated_tether_mass(pmass,v_tip,vel_c), x, 0, l).simplify_full()\n",
"#print 'ind integral with rho/ro replaced via characteristic velocity equation =',integral(x*sling_area(x,l,v_tip,vel_c,sgma,pmass)*2*sgma/(vel_c^2)/integrated_tether_mass(pmass,v_tip,vel_c),x).simplify_full()\n",
"#print 'ind integral in terms of density rho/ro =',integral(x*sling_area(x,l,v_tip,vel_c,sgma,pmass)*ro/integrated_tether_mass(pmass,v_tip,vel_c),x).simplify_full() #In terms of density \"ro\" (which doesn't overwrite actual \"rho\").\n",
"#print 'def integral =',integrate(x*sling_area(x,l,v_tip,vel_c,sgma,pmass)*2*sgma/(vel_c^2)/integrated_tether_mass(pmass,v_tip,vel_c), x, 0, l).simplify_full()\n",
"print '\\nSling center of mass =',sling_center_of_mass(l,v_tip,vel_c)\n",
"\n",
"print '\\nTreat \\\"x\\\" as positive for sling1 (the payload) and negative for sling2 (the counterbalance).'\n",
"lj = 50 #Left justify descriptions by this number of characters.\n",
"\n",
"def com_for_sling_empty(j):\n",
" \"\"\"\n",
" Input : Sling index \"j\", otherwise uses global variables.\\n\n",
" Output: Center of mass for empty sling with index j.\n",
" \"\"\"\n",
" numerator1 = integrate(x*sling_area(x,radii[j],omegas[j]*radii[j],v_cs[j][0],sigma,tip_masses_full[j][0])*2*sigma/(v_cs[j][0]^2), x, 0, radii[j])+tip_masses_empty[j][0]*radii[j]-integrate(x*sling_area(x,radii[j]/cbrs[j],omegas[j]*radii[j]/cbrs[j],v_cs[j][1],sigma,tip_masses_full[j][1])*2*sigma/(v_cs[j][1]^2), x, 0, radii[j]/cbrs[j])-tip_masses_empty[j][1]*radii[j]/cbrs[j]\n",
" denominator1 = integrated_tether_mass(tip_masses_full[j][1],radii[j]/cbrs[j]*omegas[j],v_cs[j][1])+tip_masses_empty[j][1]+integrated_tether_mass(tip_masses_full[j][0],radii[j]*omegas[j],v_cs[j][0])+tip_masses_empty[j][0]\n",
" return numerator1/denominator1\n",
"\n",
"def com_for_sling_full(j):\n",
" \"\"\"\n",
" Input : Sling index \"j\", otherwise uses global variables.\\n\n",
" Output: Center of mass for full sling with index j.\n",
" \"\"\"\n",
" numerator1 = integrate(x*sling_area(x,radii[j],omegas[j]*radii[j],v_cs[j][0],sigma,tip_masses_full[j][0])*2*sigma/(v_cs[j][0]^2), x, 0, radii[j])+tip_masses_full[j][0]*radii[j]-integrate(x*sling_area(x,radii[j]/cbrs[j],omegas[j]*radii[j]/cbrs[j],v_cs[j][1],sigma,tip_masses_full[j][1])*2*sigma/(v_cs[j][1]^2), x, 0, radii[j]/cbrs[j])-tip_masses_full[j][1]*radii[j]/cbrs[j]\n",
" denominator1 = integrated_tether_mass(tip_masses_full[j][1],radii[j]/cbrs[j]*omegas[j],v_cs[j][1])+tip_masses_full[j][1]+integrated_tether_mass(tip_masses_full[j][0],radii[j]*omegas[j],v_cs[j][0])+tip_masses_full[j][0]\n",
" return numerator1/denominator1\n",
"\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" coms_empty[j] = com_for_sling_empty(j)\n",
" if abs(coms_empty[j]) < tolerance: desc = '\\nCenter of mass for empty '+descs[j]+' (m)' ; print desc.ljust(lj+1),':',(coms_empty[j]).n()\n",
" else: desc = '\\n!!!WARNING!!! Center of mass for empty '+descs[j]+' (m)' ; print desc.ljust(lj),':',(coms_empty[j]).n()\n",
" coms_full[j] = com_for_sling_full(j)\n",
" if abs(coms_full[j]) < tolerance: desc = 'Center of mass for full '+descs[j]+' (m)' ; print desc.ljust(lj),':',(coms_full[j]).n()\n",
" else: desc = '!!!WARNING!!! Center of mass for full '+descs[j]+' (m)' ; print desc.ljust(lj),':',(coms_full[j]).n()"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 15. Calculate required power and solar panel mass for spinning the slings. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The moon sling cycle starts at the moment it throws a payload. It takes 2.5002 days to spin down and then 1.0000 days to attach a new payload. The moon sling then waits (retracted and shielded) for 5.4438 days. It takes 10.000 days to spin up, which is the end of the cycle. The moon sling cycle takes 18.944 days, which is 3.0000 resonance revisit times.\n",
"\n",
"The capture sling cycle starts at the moment the moon sling throws its payload to the capture sling. Each capture throw requires one moon throw. The capture sling takes 3.0000 days to rendezvous with and attach the payload. The capture sling then waits (retracted and shielded by the counterbalance masses) for 0.44843 days. It takes 10.000 days to spin up and throw at periapsis, then 3.1398 days to spin down. The capture sling then waits (retracted but largely unshielded) for 2.3558 days until the next revisit time after the moon sling finishes spinning up, which is the end of the cycle. The capture sling cycle takes 18.944 days, which is 3.0000 resonance revisit times.\n",
"\n",
"Technically, if mt4ct isn't an integer, the last throw can use a shorter spin up time because the payload is smaller. However, it would take an extreme case to change the time between throws because that's usually limited by the moon's orbital resonance. Before accounting for this, need to decide if the sling keeps using the full-size grapple with the smaller payload, or if a modular grapple design allows the grapple to be resized \"on the fly\" so it's the right mass for the smaller payload.\n",
"\n",
"Moon sling spinup time (days) : 10.0000000000000\n",
"Moon sling spindown time (days) : 2.50019786600602\n",
"Moon sling average power (kW) : 3.04301344198121 , Alt. calc: 3.04301344198121\n",
"Moon sling average torque (N*m) : 502099.726314180\n",
"Moon sling const accel spinup revolutions : 833.388993092595\n",
"Moon sling const accel spindown revolutions : 208.363738208302\n",
"\n",
"Capture sling A spinup time (days) : 10.0000000000000\n",
"Capture sling A spindown time (days) : 3.13981188825139\n",
"Capture sling A average power (kW) : 1.54810821222550 , Alt. calc: 1.54810821222550\n",
"Capture sling A average torque (N*m) : 311330.681650522\n",
"Capture sling A const accel spinup revolutions : 683.775075323667\n",
"Capture sling A const accel spindown revolutions : 214.692511039124\n",
"\n",
"Capture sling B spinup time (days) : 10.0000000000000\n",
"Capture sling B spindown time (days) : 3.13981188825138\n",
"Capture sling B average power (kW) : 2.14454779902594 , Alt. calc: 2.14454779902594\n",
"Capture sling B average torque (N*m) : 482217.589817167\n",
"Capture sling B const accel spinup revolutions : 611.542376435553\n",
"Capture sling B const accel spindown revolutions : 192.012802370185\n",
"\n",
"Capture sling C spinup time (days) : 10.0000000000000\n",
"Capture sling C spindown time (days) : 3.13981188825139\n",
"Capture sling C average power (kW) : 0.849744151434373 , Alt. calc: 0.849744151434373\n",
"Capture sling C average torque (N*m) : 170886.908166645\n",
"Capture sling C const accel spinup revolutions : 683.775075323667\n",
"Capture sling C const accel spindown revolutions : 214.692511039124\n",
"\n",
"Total required average sling power (kW) : 7.58541360466702\n"
]
}
],
"source": [
"#Calculate required power for spinning moon and capture slings, including the slings themselves.\n",
"summary_digits = 5 #Many print statements in this notebook will print \"summary_digits\" significant figures.\n",
"lj = 50 #Left justify descriptions by this number of characters.\n",
"\n",
"ms_moments_ratio = sum(moments_empty[0])/sum(moments_full[0]) #This is needed whether or not the capture sling is disabled.\n",
"\n",
"if cbrs[1] < tolerance:\n",
" if ms_spinup_time < 0:\n",
" print warnstring,'If cbrs[1] == 0, ms_spinup_time must be > 0!'\n",
" print failure\n",
" spinup_times [0] = ms_spinup_time #The spinup time is standardized to allow for comparisons where the spinup times are always the same.\n",
" spindown_times [0] = ms_spinup_time*ms_moments_ratio\n",
" ms_cycle_time = spinup_times[0]+spindown_times[0]+ms_attach_time\n",
" desc = 'The moon sling cycle starts at the moment it throws a payload. It takes '+str((spindown_times[0]/units).n(digits=summary_digits))+' '+uname+' to spin down and then '+str((ms_attach_time/units).n(digits=summary_digits))+' '+uname+' to attach a new payload. '\n",
" desc += 'It takes '+str((spinup_times[0]/units).n(digits=summary_digits))+' '+uname+' to spin up, which is the end of the cycle. '\n",
" desc += 'The moon sling cycle takes '+str((ms_cycle_time/units).n(digits=summary_digits))+' '+uname+'.'\n",
" ms_cycle_description = desc ; print ms_cycle_description+'\\n'\n",
"else:\n",
" #The \"cycle time\" is how long each sling takes to go through a complete cycle, ending in the same state it started in. This determines the maximum possible throw rate, disregarding interplanetary launch windows.\n",
" #The moon sling's cycle starts when it throws a payload to the capture orbit. Then it spins down, attaches a new payload, then (possibly) waits in a retracted configuration, shielded by local mass, until the capture orbit resonance allows for another throw, then spins up and throws.\n",
" #The capture sling's cycle starts when the moon sling throws its first (or only) payload to the capture orbit. After all moon sling payloads have been thrown, the capture sling rendezvouses with and attaches the last (or only) payload, then (possibly) waits in a retracted configuration, shielded by the counterbalances, until the capture sling's time to reach periapsis allows for another throw, then it spins up in time to throw when it reaches periapsis. Then it spins down and (possibly) waits for the capture orbit resonance to allow for receiving another payload. At the same time, the moon sling resets so it's ready to throw again.\n",
" #Can't loop through the slings because each one is treated differently. For instance:\n",
" if C_is_on >= 0: #If you just looped through the capture slings, they'd all incorrectly get different spin down times.\n",
" cs_moments_ratio_C_is_on = (sum(moments_empty[C_is_on])+sum(moments_empty[3]))/(sum(moments_full[C_is_on])+sum(moments_full[3]))\n",
" cs_moments_ratio_C_is_not_on = sum(moments_empty[C_is_not_on])/sum(moments_full[C_is_not_on])\n",
" else:\n",
" print warnstring,'This code requires the 3rd capture sling to be defined. One way to fix this would be to set C_is_on = 1 and C_is_not_on = 2 even if the 3rd capture sling isn\\'t defined. However, that would likely cause unintended side effects. Be careful.'\n",
" print failure\n",
"\n",
" if ms_spinup_time < 0:\n",
" if cs_spinup_time > 0:\n",
" print warnstring,'If ms_spinup_time is < 0, cs_spinup_time also have to be < 0, and vice versa.'\n",
" print failure\n",
" extra_ms_cycles = (abs(ms_spinup_time)).ceil() - 1 #If spinup_time == -1, no extra cycles. If spinup_time == -2, one extra cycle. Etc.\n",
" num_ms_cycles = extra_ms_cycles + (ms_attach_time/(period_moon*resn_p)).ceil() #If the attach time is 1.1x the revisit time (period_moon*resn_p), need to wait 2 full revisit times.\n",
" ms_cycle_time = period_moon*resn_p*num_ms_cycles\n",
" spinup_times [0] = (ms_cycle_time-ms_attach_time)/(1+ms_moments_ratio)\n",
" spindown_times [0] = spinup_times[0]*ms_moments_ratio\n",
" wait_times [0] = 0 ; wait1 = 0 #Never any wait time for the moon sling when ms_spinup_time < 0.\n",
" #First, calculate time starting at the launch of the last moon payload. The extra time needed to throw all the previous moon payloads (if there are any) will be accounted for later.\n",
" #How many complete orbits do the capture slings need to go through until it's ready to throw at periapsis?\n",
" extra_cs_orbits = (abs(cs_spinup_time)).ceil() - 1 #If spinup_time == -1, no extra cycles. If spinup_time == -2, one extra cycle. Etc.\n",
" num_cs_orbits = extra_cs_orbits + max([0,RDF(((cs_attach_time-time_from_moon_to_capture_periapsis)/period_capture)).ceil()]) #use max() because if the time difference is negative, zero full orbits are needed before the throw.\n",
" cs_cycle_time = time_from_moon_to_capture_periapsis + num_cs_orbits*period_capture\n",
" spinup_times [C_is_on] = cs_cycle_time-cs_attach_time #No need for spin-down yet.\n",
" spinup_times [C_is_not_on] = spinup_times[C_is_on] #Spin up times are identical.\n",
" spindown_times[C_is_on] = spinup_times[C_is_on] *cs_moments_ratio_C_is_on\n",
" spindown_times[C_is_not_on] = spinup_times[C_is_not_on]*cs_moments_ratio_C_is_not_on\n",
" #How long until the next revisit time with the moon?\n",
" time_to_next_revisit = time_from_capture_periapsis_to_moon + (resn_s - (num_cs_orbits+1)%resn_s)*period_capture\n",
" #How many times do the capture slings need to pass up orbital resonance revisits until it's spun down for the next payload from the moon sling?\n",
" num_cs_cycles = max([0,RDF(((spindown_times[C_is_on]-time_to_next_revisit)/(period_capture*resn_s))).ceil()])\n",
" cs_cycle_time += time_to_next_revisit + num_cs_cycles*(period_capture*resn_s)\n",
" if ms_cycle_time < cs_cycle_time: #If the moon sling is ready to throw, just need to account for the extra time needed to throw all the moon payloads OTHER than the last one:\n",
" cs_cycle_time += ms_cycle_time*(mt4ct.ceil()-1) #The capture sling's cycle starts when the moon sling throws its first (or only) payload to the capture orbit. So the time needed to spinup the moon sling to throw its first payload isn't included in the capture sling cycle time.\n",
" else: #If the moon sling is NOT ready to throw, just need to account for the time needed to throw ALL the moon payloads:\n",
" cs_cycle_time = ms_cycle_time*(mt4ct.ceil())\n",
" spinup_times [3] = spinup_times[C_is_on] #Capture sling C is attached to the sling \"C_is_on\".\n",
" spindown_times [3] = spindown_times[C_is_on]\n",
" wait_times [C_is_on] = cs_cycle_time - (spinup_times[C_is_on] +spindown_times[C_is_on] +cs_attach_time)\n",
" wait_times [C_is_not_on] = cs_cycle_time - (spinup_times[C_is_not_on]+spindown_times[C_is_not_on]+cs_attach_time)\n",
" wait_times [3] = wait_times[C_is_on]\n",
" else:\n",
" if cs_spinup_time < 0:\n",
" print warnstring,'If ms_spinup_time is < 0, cs_spinup_time also have to be < 0, and vice versa.'\n",
" print failure\n",
" spinup_times [0] = ms_spinup_time #The spinup time is standardized to allow for comparisons where the spinup times are always the same.\n",
" spindown_times [0] = ms_spinup_time*ms_moments_ratio\n",
" spinup_times [C_is_on] = cs_spinup_time #The spinup time is standardized to allow for comparisons where the spinup times are always the same.\n",
" spinup_times [C_is_not_on] = spinup_times[C_is_on] #Spin up times are identical.\n",
" spindown_times[C_is_on] = spinup_times[C_is_on] *cs_moments_ratio_C_is_on\n",
" spindown_times[C_is_not_on] = spinup_times[C_is_not_on]*cs_moments_ratio_C_is_not_on\n",
" spinup_times [3] = spinup_times[C_is_on] #Capture sling C is attached to the sling \"C_is_on\".\n",
" spindown_times [3] = spindown_times[C_is_on]\n",
" ms_cycle_time = period_moon*resn_p*RDF((ms_attach_time+spinup_times[0]+spindown_times[0])/(period_moon*resn_p)).ceil() #If the spinup time is 1.1x the revisit time (period_moon*resn_p), need to wait 2 full revisit times. Ideally, one could then increase the spinup time to exactly 2 revisit times, but this way the spinup time is standardized to allow for comparisons where the spinup times are always the same.\n",
" wait_times [0] = ms_cycle_time - (ms_attach_time+spinup_times[0]+spindown_times[0])\n",
" #First, calculate time starting at the launch of the last moon payload. The extra time needed to throw all the previous moon payloads (if there are any) will be accounted for later.\n",
" #How many complete orbits do the capture slings need to go through until it's ready to throw at periapsis?\n",
" num_cs_orbits = max([0,RDF(((cs_attach_time+spinup_times[1]-time_from_moon_to_capture_periapsis)/period_capture)).ceil()]) #use max() because if the time difference is negative, zero full orbits are needed before the throw.\n",
" cs_cycle_time = time_from_moon_to_capture_periapsis + num_cs_orbits*period_capture\n",
" wait1 = cs_cycle_time - (cs_attach_time+spinup_times[1]) #This wait time happens prior to the throw, so the sling can be shielded by the counterbalance masses during this wait time.\n",
" #How long until the next revisit time with the moon?\n",
" time_to_next_revisit = time_from_capture_periapsis_to_moon + (resn_s - (num_cs_orbits+1)%resn_s)*period_capture\n",
" #How many times do the capture slings need to pass up orbital resonance revisits until it's spun down for the next payload from the moon sling?\n",
" num_cs_cycles = max([0,RDF(((spindown_times[C_is_on]-time_to_next_revisit)/(period_capture*resn_s))).ceil()])\n",
" cs_cycle_time += time_to_next_revisit + num_cs_cycles*(period_capture*resn_s)\n",
" if ms_cycle_time < cs_cycle_time: #If the moon sling is ready to throw, just need to account for the extra time needed to throw all the moon payloads OTHER than the last one:\n",
" cs_cycle_time += ms_cycle_time*(mt4ct.ceil()-1) #The capture sling's cycle starts when the moon sling throws its first (or only) payload to the capture orbit. So the time needed to spinup the moon sling to throw its first payload isn't included in the capture sling cycle time.\n",
" else: #If the moon sling is NOT ready to throw, replace all this with the time needed to throw ALL the moon payloads:\n",
" cs_cycle_time = ms_cycle_time*(mt4ct.ceil())\n",
" #The following wait times account for both \"wait1\" which happens prior to the throw, and the wait which happens after the throw.\n",
" wait_times [C_is_on] = cs_cycle_time - (spinup_times[C_is_on] +spindown_times[C_is_on] +cs_attach_time)\n",
" wait_times [C_is_not_on] = cs_cycle_time - (spinup_times[C_is_not_on]+spindown_times[C_is_not_on]+cs_attach_time)\n",
" wait_times [3] = wait_times[C_is_on]\n",
"\n",
" if abs(spindown_times[C_is_not_on] - spindown_times[C_is_on]) > tolerance:\n",
" print warnstring,'Spindown times for the two main capture slings are not the same!'\n",
" print failure\n",
"\n",
" print\n",
" desc = 'The moon sling cycle starts at the moment it throws a payload. It takes '+str((spindown_times[0]/units).n(digits=summary_digits))+' '+uname+' to spin down and then '+str((ms_attach_time/units).n(digits=summary_digits))+' '+uname+' to attach a new payload. '\n",
" if wait_times[0] > tolerance: desc += 'The moon sling then waits (retracted and shielded) for '+str((wait_times[0]/units).n(digits=summary_digits))+' '+uname+'. '\n",
" if ms_spinup_time < 0:\n",
" desc += 'There\\'s no wait time because its spinup time was optimized for '\n",
" if extra_ms_cycles == 1: desc += 'one extra revisit time. '\n",
" elif extra_ms_cycles != 0: desc += str(extra_ms_cycles)+' extra revisit times. '\n",
" else: desc += 'the shortest possible cycle time. '\n",
" desc += 'It takes '+str((spinup_times[0]/units).n(digits=summary_digits))+' '+uname+' to spin up, which is the end of the cycle. '\n",
" desc += 'The moon sling cycle takes '+str((ms_cycle_time/units).n(digits=summary_digits))+' '+uname+', which is '+str((ms_cycle_time/(period_moon*resn_p)).n(digits=summary_digits))+' resonance revisit times.'\n",
" ms_cycle_description = desc ; print ms_cycle_description+'\\n'\n",
"\n",
" if mt4ct.ceil() != 1:\n",
" desc = 'The capture sling cycle starts at the moment the moon sling throws its first payload to the capture sling. '\n",
" desc += 'Each capture throw requires '+str(mt4ct.ceil())+' moon throws. The moon sling cycle starts at the moment it throws the first payload, so',str((ms_cycle_time*(mt4ct.ceil()-1)/units).n(digits=summary_digits)),uname,'pass until the moon sling throws its last payload to the capture sling. '\n",
" desc += 'The capture sling takes '+str((cs_attach_time/units).n(digits=summary_digits))+' '+uname+' to rendezvous with and attach the last payload. '\n",
" else:\n",
" desc = 'The capture sling cycle starts at the moment the moon sling throws its payload to the capture sling. '\n",
" desc += 'Each capture throw requires one moon throw. '\n",
" desc += 'The capture sling takes '+str((cs_attach_time/units).n(digits=summary_digits))+' '+uname+' to rendezvous with and attach the payload. '\n",
" if cs_spinup_time < 0:\n",
" desc += 'There\\'s no wait time because its spinup time was optimized for '\n",
" if extra_cs_orbits == 1: desc += 'one extra capture orbit. '\n",
" elif extra_cs_orbits != 0: desc += str(extra_cs_orbits)+' extra capture orbits. '\n",
" else: desc += 'the shortest possible cycle time. '\n",
" else: desc += 'The capture sling then waits (retracted and shielded by the counterbalance masses) for '+str((wait1/units).n(digits=summary_digits))+' '+uname+'. '\n",
" desc += 'It takes '+str((spinup_times[1]/units).n(digits=summary_digits))+' '+uname+' to spin up and throw at periapsis, '\n",
" desc += 'then '+str((spindown_times[1]/units).n(digits=summary_digits))+' '+uname+' to spin down. '\n",
" if wait_times[1] > tolerance: desc += 'The capture sling then waits (retracted but largely unshielded) for '+str(((wait_times[1]-wait1)/units).n(digits=summary_digits))+' '+uname+' until the next revisit time after the moon sling finishes spinning up, which is the end of the cycle. '\n",
" desc += 'The capture sling cycle takes '+str((cs_cycle_time/units).n(digits=summary_digits))+' '+uname+', which is '+str((cs_cycle_time/(period_moon*resn_p)).n(digits=summary_digits))+' resonance revisit times.'\n",
" cs_cycle_description = desc ; print cs_cycle_description+'\\n'\n",
"\n",
" print 'Technically, if mt4ct isn\\'t an integer, the last throw can use a shorter spin up time because the payload is smaller. However, it would take an extreme case to change the time between throws because that\\'s usually limited by the moon\\'s orbital resonance. Before accounting for this, need to decide if the sling keeps using the full-size grapple with the smaller payload, or if a modular grapple design allows the grapple to be resized \"on the fly\" so it\\'s the right mass for the smaller payload.'\n",
"\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" avg_powers[j] = 0.5*sum(moments_full[j])*omegas[j]^2/spinup_times[j]\n",
" avg_powers_alt[j] = (sling_energy(radii[j]*omegas[j],v_cs[j][0],tip_masses_full[j][0]) + sling_energy(radii[j]/cbrs[j]*omegas[j],v_cs[j][1],tip_masses_full[j][1]))/spinup_times[j]\n",
" avg_torques[j] = (moments_full[j][0]+moments_full[j][1])*omegas[j]/spinup_times[j]\n",
"\n",
" desc = '\\n'+cap1st(descs[j])+' spinup time ('+uname+')' ; print desc.ljust(lj+1),':',(spinup_times[j]/units).n()\n",
" desc = cap1st(descs[j])+' spindown time ('+uname+')' ; print desc.ljust(lj),':',(spindown_times[j]/units).n()\n",
" desc = cap1st(descs[j])+' average power (kW)' ; print desc.ljust(lj),':',(avg_powers[j]/1000).n(),', Alt. calc:',(avg_powers_alt[j]/1000).n()\n",
" desc = cap1st(descs[j])+' average torque (N*m)' ; print desc.ljust(lj),':',(avg_torques[j]).n()\n",
" desc = cap1st(descs[j])+' const accel spinup revolutions' ; print desc.ljust(lj),':',(0.5*(omegas[j]/spinup_times[j])*spinup_times[j]^2/(2*pi)).n() #theta = 0.5*a*t^2, where a = angular acceleration. Revolutions = theta/(2*pi)\n",
" desc = cap1st(descs[j])+' const accel spindown revolutions' ; print desc.ljust(lj),':',((omegas[j]*spindown_times[j]-0.5*(omegas[j]/spindown_times[j])*spindown_times[j]^2)/(2*pi)).n() #theta = omega0*t - 0.5*a*t^2, where a = angular acceleration. Revolutions = theta/(2*pi)\n",
"\n",
"if cbrs[1] > tolerance: print '\\nTotal required average sling power (kW)'.ljust(lj+1),':',(sum(avg_powers)/1000).n()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Moon sling solar panel area, 100% efficiency (m^2) : 4.34184122114847\n",
"Moon sling solar panel mass via Juno (tons) : 0.195238608442057\n",
"\n",
"Capture sling A solar panel area, 100% efficiency (m^2) : 2.20887622706750\n",
"Capture sling A solar panel mass via Juno (tons) : 0.0993260459854694\n",
"\n",
"Capture sling B solar panel area, 100% efficiency (m^2) : 3.05988987957666\n",
"Capture sling B solar panel mass via Juno (tons) : 0.137593387608140\n",
"\n",
"Capture sling C solar panel area, 100% efficiency (m^2) : 1.21243440243416\n",
"Capture sling C solar panel mass via Juno (tons) : 0.0545192680942643\n",
"\n",
"Marswhip_gradient_factor : 1.03059231937514\n",
"!!!!NOTE: This added mass ISN'T included in calculations for solar panel area, torque, center of mass, or moment of inertia!!\n"
]
}
],
"source": [
"#Estimate required solar panel area at the origin planet's aphelion, assuming 100% efficient solar panels.\n",
"\n",
"#Estimate required solar panel mass, using Juno's solar panels and assuming that Jupiter is at perihelion and the origin planet is at aphelion.\n",
"# http://web.archive.org/web/20160813171308/https://www.edn.com/Pdf/ViewPdf?contentItemId=4442465\n",
"juno_jupiter_power = 420 #Juno solar panel power in watts after radiation degradation: https://en.wikipedia.org/wiki/Juno_(spacecraft)#Solar_panels\n",
"juno_pl_power = juno_jupiter_power*(a_jupiter*(1-ecc_jupiter)/a_pl*(1+ecc_pl))^2\n",
"juno_panel_mass = 340 #Juno solar panel mass in kg, from https://en.wikipedia.org/wiki/Juno_(spacecraft)#Solar_panels\n",
"juno_panel_area = 3*(2.7*8.9) #in m^2, from https://en.wikipedia.org/wiki/Juno_(spacecraft)#Solar_panels\n",
"\n",
"lj = 55 #Left justify descriptions by this number of characters.\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" desc = '\\n'+cap1st(descs[j])+' solar panel area, 100% efficiency (m^2)' ; print desc.ljust(lj+1),':',(avg_powers[j]/(solar_constant*(a_earth/a_pl*(1+ecc_pl))^2)).n()\n",
" junopanel_masses[j] = (avg_powers[j]/juno_pl_power)*juno_panel_mass\n",
" desc = cap1st(descs[j])+' solar panel mass via Juno (tons)' ; print desc.ljust(lj),':',(junopanel_masses[j]/1000.0).n()\n",
"\n",
"if estimate_misc == 0:\n",
" radiator_masses = [0.0 for i in range(num_slings)]\n",
" motor_masses = [0.0 for i in range(num_slings)]\n",
" hub_masses = [0.0 for i in range(num_slings)]\n",
" estimate_misc_str = ''\n",
"else:\n",
" #Extremely crude stand ins for estimates of various other masses. These only exist as a reminder to calculate them correctly!\n",
" #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
" radiator_masses = copy.deepcopy(junopanel_masses)\n",
" motor_masses = copy.deepcopy(junopanel_masses)\n",
" hub_masses = copy.deepcopy(junopanel_masses)\n",
" estimate_misc_str = ' = solar panel mass '\n",
"\n",
"# Hoyt 2000: DESIGN AND SIMULATION OF A TETHER BOOST FACILITY FOR LEO -> GTO TRANSPORT http://web.archive.org/web/20171118235227/http://www.tethers.com/papers/HOYT_MMOSTT_Final.pdf\n",
"#see p 65 of PDF, numbered page 6 of article (regarding a tether in Earth orbit):\n",
"#\"In order for the tether facility to reboost its orbit within 30 days, the facility will require a solar power generation capability of 100 kW. Because the facility will pass through the radiation belts frequently, its solar power system will utilize a concentrator-type solar panel design, such as the Scarlet design, with 150 mil Aluminum backside and 100 mil glass cover slides to shield the arrays from the belt particles. In order for the solar array to produce the desired power levels after 10 years of operation, they system will be deployed with 137 kW of initial power generation capability. Using Scarlet-type panel technology, this solar array would mass approximately 1,370 kg. The tether facility will collect this solar power during the roughly 80% of its orbit that it is in the sunlight, and store it in a battery system. Then, during perigee pass, it will drive the electrodynamic tether at an average power level of 300 kW (modulated as to be described later). In order to provide a maximum battery depth-of-discharge of 30%, the control station will have a battery system with 5,700 A•hr of capacity (120 V power system). Using advanced Li ion batteries, this will require approximately 4,600 kg of batteries. The control system will also require the capability to transform the 120 V battery voltage up to the 20+kV needed to drive tether currents on the order of 15 A.\"\n",
"\n",
"#see p305 of PDF, numbered page N-5 of article (regarding a tether in Earth orbit):\n",
"#\"In order for the tether facility to reboost its orbit within 30 days, the facility will require a solar power generation capability of 5.5 kW. Because the facility will pass through the radiation belts frequently, its solar power system will utilize a concentrator-type solar panel design, such as the Scarlet design, with 150 mil Aluminum backside and 100 mil glass cover slides to shield the arrays from the belt particles. In order for the solar array to produce the desired power levels after 10 years of operation, they system will be deployed with 7.5 kW of initial power generation capability. Using Scarlet-type panel technology, this solar array would mass approximately 75 kg. The tether facility will collect this solar power during the roughly 80% of its orbit that it is in the sunlight, and store it in a battery system. Then, during perigee pass, it will drive the electrodynamic tether at an average power level of 300 kW (modulated as to be described later). In order to provide a maximum battery depth-of-discharge of 30%, the control station will have a battery system with 315 A•hr of capacity (120 V power system). Using advanced Li ion batteries, this will require approximately 255 kg of batteries.\"\n",
"\n",
"#Gravity gradient forces increase the capture sling mass over the Moravec free space tether equation, but that calculation won't be done here.\n",
"#Instead, use the estimate from Hoyt et al. 1999: http://web.archive.org/web/20171118235227/http://www.tethers.com/papers/HOYT_MMOSTT_Final.pdf\n",
"#That article (IAF-99-A.5.10) begins on page 240 of the PDF. Its title is RAPID INTERPLANETARY TETHER TRANSPORT SYSTEMS by Hoyt, Forward, Nordley and Uphoff.\n",
"#Alternative URL for just that one paper: http://web.archive.org/web/20161011035046/http://www.tethers.com/papers/InterplanetaryTetherTrnsprt.pdf\n",
"#Turn to page 266 of the first PDF or page 28 of the second PDF (in both cases, that's page 27 of the article).\n",
"#One arm of the Marswhip has a mass of 4609 kg before gravity gradients are considered, and 4750 kg after.\n",
"#That arm of the Marswhip is 427 km long and the Marswhip center of mass has a periapsis radius of 4025 km.\n",
"#So this is unlikely to be a severe underestimate of the gravity gradient effects on the capture orbit sling.\n",
"Marswhip_gradient_factor = 4750.0/4609.0\n",
"print '\\nMarswhip_gradient_factor'.ljust(lj+1),':',Marswhip_gradient_factor\n",
"print '!!!!NOTE: This added mass ISN\\'T included in calculations for solar panel area, torque, center of mass, or moment of inertia!!'"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 16. Animate the capture sling throw. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"!!!WARNING!!! The longest capture sling arm is 135.15 km long, so it can reach down to an altitude of 389.32 km.\n",
"Highest capture sling payload tip speed is 1102.9 m/s.\n",
"Highest capture sling tip speed is 1202.06474378 m/s.\n",
"!!!WARNING!!! FIX orbit lines (which means moving calc_payload_pos() back up to this cell)!!!! ALSO: CONSIDER AN OPTION THAT FOLLOWS COUNTERBALANCE B AS IT AEROBRAKES OR LIFTS A PACKAGE FROM MARS.\n",
"!!!WARNING!!! Size of lights and payloads/balances, hub, solar panels, radiators is exaggerated by a factor of 9000. for visibility.\n",
"slings.mp4 , resolution: 1280 x 720 , numframes: 1000 , fps: 30 , simulation timespan: 33. minutes, video length: 33. seconds, speedup: 60. , desired filesize: 100. MB, bitrate: 25165824 bits/second, viewtype: 1 , atmo_layers: 0 , orbit_lines: 0\n",
"CPU times: user 22 µs, sys: 1 µs, total: 23 µs\n",
"Wall time: 34.1 µs\n",
"\n",
"If this cell crashes, try running the following command in a terminal before running this cell again (which would delete the frames):\n",
"ffmpeg -f image2 -r 30.000000 -i anim%04d.png -c:a aac -c:v libx264 -pix_fmt yuv420p -movflags faststart -b:v 25165824 slings.mp4 \n",
"\n",
"----------------------------------------------------------| -> 1000 frames to calculate:\n",
"...........................................................\n",
"Finished rendering frames.\n",
"Now using ffmpeg to create the animation...\n",
"\n",
"walltime() -start = 7227.18821502 seconds.\n",
"!!!WARNING!!! Size of lights and payloads/balances, hub, solar panels, radiators is exaggerated by a factor of 9000. for visibility.\n"
]
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"!!!WARNING!!! Size of lights and payloads/balances, hub, solar panels, radiators is exaggerated by a factor of 9000. for visibility.\n"
]
}
],
"source": [
"notif = 10 #Only print a notification if the frame number is a multiple of \"notif\" to avoid printing too many. Also used elsewhere.\n",
"if cbrs[1] < tolerance: print 'Skipping this section because the capture sling has been disabled.'\n",
"else:\n",
" print capture_frame_string\n",
"\n",
" def tach(vec):\n",
" \"\"\"\n",
" Input : Vector in the Sage right hand coordinate system.\\n\n",
" Output: Tuple in the Tachyon left hand coordinate system.\n",
" \"\"\"\n",
" #NOTE: P = P.transform(scale=[-1,1,1]) # Tachyon does not have a right-hand-rule coordinate system # https://ask.sagemath.org/question/7586/the-tachyon-object-used-for-rendering-plots/\n",
" #FROM TACHYON DOCS: Note that the coordinates are by default such that z is up, positive y is to the {left} and x is toward you. This is not oriented according to the right hand rule.\n",
" vec[0] = -vec[0]\n",
" vec = rotateX(-pi/2)*vec #Since I can't seem to rotate the Mars texture in Tachyon to match its actual orientation, might as well rotate that polar ice cap out of the way...\n",
" return tuple(vec)\n",
"\n",
" initial_verbose=get_verbose()\n",
" set_verbose(0)\n",
"\n",
" #These values are used to automatically place the camera for different capture slings. Also, \"longest_capture_arm\" is used elsewhere.\n",
" var('temp1 temp2 temp3')\n",
" cbradii = [temp1/temp2 for temp1,temp2 in zip(radii[1:3],cbrs[1:3])] #Make a list of the capture sling counterbalance radii. Ignore moon sling and capture sling C.\n",
" longest_capture_arm = max(radii+cbradii)\n",
" if periapsis_capture-longest_capture_arm-r_pl < target_alt: longest_arm_string = warnstring #Also used elsewhere.\n",
" else: longest_arm_string = ''\n",
" longest_arm_string += 'The longest capture sling arm is '+str((longest_capture_arm/1000.0).n(digits=5))+' km long, so it can reach down to an altitude of '+str(((periapsis_capture-longest_capture_arm-r_pl)/1000.0).n(digits=5))+' km.'\n",
" print longest_arm_string\n",
" original_arm = 135150 #The camera height values were originally tuned for a longest capture arm of 135.15 km.\n",
" capture_payload_speeds = [temp1*temp2 for temp1,temp2 in zip(radii[1:3],omegas[1:3])] #Make a list of the capture sling payload tip speeds. Ignore moon sling and capture sling C.\n",
" capture_balance_speeds = [temp1*temp2/temp3 for temp1,temp2,temp3 in zip(radii[1:3],omegas[1:3],cbrs[1:3])] #Make a list of the capture sling payload tip speeds. Ignore moon sling and capture sling C.\n",
" highest_capture_payload_speed = max(capture_payload_speeds) #The counterbalance tip speeds don't really affect the animation, so only consider the payload tip speeds.\n",
" highest_capture_tip_speed = max(capture_payload_speeds+capture_balance_speeds)\n",
" print 'Highest capture sling payload tip speed is',highest_capture_payload_speed.n(digits=5),'m/s.'\n",
" if abs(highest_capture_tip_speed-highest_capture_payload_speed) > tolerance: print 'Highest capture sling tip speed is ',highest_capture_tip_speed.n(digits=5),'m/s.'\n",
" original_capture_payload_speed = 1102.9 #The camera zoomout speed was originally tuned for this payload tip speed.\n",
"\n",
" #Based on example code ( https://gist.githubusercontent.com/williamstein/7386062/raw/91eaa632c358a382c3c4c69f1d9de95668302643/gistfile1.txt http://archive.li/DkvSw ) used here in a video by William Stein: https://www.youtube.com/watch?v=lhirRHCW1q0\n",
" #Animation posted by Andrey Novoseltsev. https://groups.google.com/forum/#!topic/sage-notebook/ovxWawqLld0 http://archive.li/HNSZE\n",
" print warnstring,'FIX orbit lines (which means moving calc_payload_pos() back up to this cell)!!!! ALSO: CONSIDER AN OPTION THAT FOLLOWS COUNTERBALANCE B AS IT AEROBRAKES OR LIFTS A PACKAGE FROM MARS. WAIT- COUNTERBALANCE B ROTATES IN THE WRONG DIRECTION TO PICK UP A PAYLOAD!'\n",
" aspectr = 16/9\n",
" xresn = 1280 ; yresn = xresn//aspectr\n",
" fps = 30 #frames per second for the output animation.\n",
" filesize = 100 #Desired animation file size, in megabytes. Videos > 15MB fail in CoCalc and videos > 20MB can't be easily emailed.\n",
" numframes = -60 #Total number of frames in the output animation. If negative, that's the target speedup. Speedup is simulation timespan divided by animation length.\n",
" #Simulation timespan suggestions for different viewtypes:\n",
" #( 0) Side view :\n",
" #( 1) Overhead view going up : -1000 to 1000\n",
" #(100) Payload view : -2200 to 1200\n",
" start_time = -1000 #Time at the start of the animation, relative to the time at which the capture slings reach periapsis.\n",
" end_time = -start_time #Time at the end of the animation, relative to the time at which the capture slings reach periapsis.\n",
" if numframes < 0: numframes = ((end_time-start_time)*fps/abs(numframes)).ceil() #Because abs(numframes) is interpreted as the target speedup, where sim_time = end_time-start_time, and speedup = sim_time/vid_time, and fps = numframes/vid_time . So: speedup = sim_time*fps/numframes\n",
" atmo_layers = 0#40 #Set atmo_layers to 0 to turn off the atmosphere for faster rendering, or any positive integer for that number of atmo layers. !!!WARNING!!! THIS ALSO INCREASES THE RAY DEPTH, SO IT DRAMATICALLY SLOWS DOWN THE RENDERING!\n",
" orbit_lines = 0#10 #Number of lines used to display the largest orbit of the payloads and counterbalances. 0=disable\n",
" disable_sling_B = 0 #Set to 1 to disable rendering of capture sling B.\n",
" #Viewtype:\n",
" #0 = Side view.\n",
" #1 = Overhead view directly onto the ecliptic/rotational plane.\n",
" #2 = Simple overhead view where the camera is always directly above the hub.\n",
" #10 = HUB AND HAWSEPIPE DON'T SHOW UP HERE! View from another orbit. Sling overtakes camera.\n",
" #100 = Payload view - default rotation after throw - choose payload/balance by setting pc_j and pc_k below.\n",
" #150 = 3rd persion payload view - default rotation after throw - choose payload/balance by setting pc_j and pc_k below.\n",
" viewtype = 1\n",
" goingup = 1 #For viewtype 1. Set to 1 to watch the sling move up the screen or to 0 to watch the sling move to the right.\n",
" if goingup: #For viewtype 1. Watch sling move up the screen. Need to zoom out quickly after periapsis to keep payloads/balances in view.\n",
" cam_const1 = 0.2 #For viewtype 1. Controls the height of the camera above the ecliptic plane while at periapsis.\n",
" cam_const2 = 2.3 #For viewtype 1. Exponent that controls how quickly height of the camera above the ecliptic plane increases while moving to/from periapsis.\n",
" else: #For viewtype 1. Watch the sling move to the right. Need to zoom out while at periapsis to keep shadows in view.\n",
" cam_const1 = 0.7 #For viewtype 1. Same as cam_const1 above.\n",
" cam_const2 = 0.9 #For viewtype 1. Same as cam_const2 above.\n",
" pc_j = 1; pc_k = 0 #For viewtypes > 100. Payload camera sling and arm indices. pc_j=1/2 means sling A/B. pc_k=0/1 means payload/balance.\n",
" if longest_capture_arm > original_arm: cam_const1 *= (longest_capture_arm/original_arm)^(0.7) #Raise camera height at periapsis more for slings with longer arms.\n",
" if highest_capture_tip_speed > original_capture_payload_speed: cam_const2 *= (highest_capture_tip_speed/original_capture_payload_speed)^(0.5) #Zoom camera out faster for slings with higher maximum payload tip speeds.\n",
" if viewtype==1: exagg = 9e3\n",
" else: exagg = 4e3\n",
" if longest_capture_arm > original_arm: exagg *= (longest_capture_arm/original_arm)^(0.6) #Exaggerate sizes more for slings with longer arms.\n",
" #exagg = 1.0 #Just in case you want to see what it looks like with a realistic scale.\n",
" exagg_warning = warnstring+'Size of lights and payloads/balances, hub, solar panels, radiators is exaggerated by a factor of '+str(exagg.n(digits=2))+' for visibility.'\n",
" if exagg > 1+tolerance: print exagg_warning\n",
" light_rad = 0.1*exagg #Size of lights (their radii, in meters) along tether.\n",
" payload_width = 6*light_rad\n",
" light_spacing = max([10000,light_rad*2]) #Try to space the lights using actual meters, but use light_rad*2 if it's bigger.\n",
" hub_rad = 3*light_rad #Radius of the hub.\n",
" hub_length = 20*light_rad #Distance along the hub's rotation axis from the centers of slings A and B. If set to 2*light_rad, the lights touch.\n",
" solar_width = 25*light_rad #Width of solar panels.\n",
" radiator_rad = 0.7*solar_width #Radiator radius.\n",
" light1_color = (0,1,0) #Color of lights on capture sling A.\n",
" light2_color = (1,0,0) #Color of lights on capture sling B.\n",
" pc_opacity = 0.0 #If using the payload cam, lights on that sling arm use this opacity until the throw, so the payload cam can see.\n",
" pc_ambient = 0.02\n",
" payload0_color = (1.0,1.0,1.0) #Color of payloads.\n",
" payload1_color = (1.0,1.0,0.0) #Color of counterbalances.\n",
" grapple_color = (0.0,0.5,1.0) #Color of grapples. Used to be orange: (1.0,0.4,0.0)\n",
" hub_color = (0.6,0.6,0.6) #Color of hub.\n",
" radiator_color = (0.7,0.7,0.7) #Color of radiators.\n",
" solar_color = (0.0,0.0,0.7) #Color of solar panels.\n",
" solar_ambient = 0.0 #Ambient lighting coefficient for solar panels.\n",
" solar_diffuse = 0.9 #Diffuse shading coefficient for solar panels.\n",
" solar_specular = 0.1 #Specular shading coefficient for solar panels.\n",
" hub_ambient = 0.6 #Ambient lighting coefficient for hub textures (hub, radiators).\n",
" hub_diffuse = 0.4 #Diffuse shading coefficient for hub textures (hub, radiators).\n",
" payload_ambient = 0.2 #Ambient lighting coefficient for payload and balance textures.\n",
" payload_diffuse = 0.8 #Diffuse shading coefficient for payload and balance textures.\n",
" stars_ambient = 0.4 #Controls the ambient brightness of the stars texture, if the starsfile exists.\n",
" atmo_opacity = 0.001 #These control the appearance of the atmosphere, if atmo_layers > 0.\n",
" atmo_ambient = 0.0\n",
" atmo_diffuse = 0.005\n",
" backup_mars = (0.6,0.3,0.2) #Only used to texture Mars if the Mars file isn't found.\n",
" tiny = 10*tolerance #Apparently two triangles can't share the same vertices, so offset them by a tiny amount for one triangle.\n",
"\n",
" retrograde_anchor = 1 #\"1/0\" Enables/disables the retrograde anchor.\n",
" hawsepipe_multiplier = 3 #This number multiplies the \"offset\" (calculated below) that determines the length of the hub.\n",
" hawsepipe_coils = 20 #Number of coils in the hawsepipe. Needs to be even.\n",
" hawsepipe_gap = 0.7 #Fraction of hawsepipe that is open.\n",
" hawsepipe_supports = 8 #Number of axial supports running along the length of the hawsepipe.\n",
" support_width = 10*pi/180 #Width of axial supports running along the length of the hawsepipe, in radians.\n",
" anchor_color = (0.0,1.0,1.0) #Color of anchor links.\n",
" anchor_spread = 2000.0 #The anchor has links extending out to \"spread\" time since periapsis.\n",
" anchor_links = 50 #EVEN number of deployed anchor links.\n",
" if retrograde_anchor and trajopt < 100:\n",
" print warnstring,'Disabling retrograde anchor because its geometry in the perpendicular setup (i.e. trajopt < 100) is much more complicated.'\n",
" retrograde_anchor = 0\n",
" if retrograde_anchor:\n",
" if anchor_links % 2 != 0:\n",
" anchor_links += 1\n",
" print warnstring,'The requested anchor_links number was odd so it was increased to',str(anchor_links)+'.'\n",
" if hawsepipe_coils % 2 != 0:\n",
" hawsepipe_coils += 1\n",
" print warnstring,'The requested hawsepipe_coils number was odd so it was increased to',str(hawsepipe_coils)+'.'\n",
" kepler_anchor = copy.deepcopy(kepler_com_cs) #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
" kepler_anchor[2] = pi #Change inclination to retrograde.\n",
" hub_stub = hub_length - 4*hub_rad\n",
" if hub_stub < 2*light_rad:\n",
" print warnstring,'hub_stub is',hub_stub,', but it needs to be >= light_rad which is',light_rad\n",
" print failure\n",
"\n",
" if viewtype in [0,10,11]: #No need for a bright hub because we're in close.\n",
" hub_ambient = 0.01\n",
" hub_diffuse = 0.99\n",
" if numframes/notif > 60: notif = (numframes/60).ceil() #Only print a notification if the frame number is a multiple of \"notif\" to avoid printing too many. Also used elsewhere.\n",
"\n",
" #Size the payloads and counterbalances according to their masses.\n",
" combined_payload_masses = payload_masses[:num_slings-1] #Don't need deepcopy because we're taking a subset.\n",
" combined_payload_masses[C_is_on][0] += payload_masses[3][0]\n",
" combined_payload_masses[C_is_on][1] += payload_masses[3][1]\n",
" smallest_cs_mass = (min(map(min, *combined_payload_masses[1:]))).n() #Smallest capture sling mass ignores moon sling.\n",
" payload_density = smallest_cs_mass/(payload_width)^3 #This density makes the least massive payload have the standard payload_width.\n",
" combined_payload_widths = [[(y/payload_density)^(1/3) for y in x] for x in combined_payload_masses]\n",
" #Also size the grapples according to their empty tip masses and the same payload_density as above.\n",
" combined_grapple_masses = tip_masses_empty[:num_slings-1] #Don't need deepcopy because we're taking a subset.\n",
" combined_grapple_masses[C_is_on][0] += tip_masses_empty[3][0]\n",
" combined_grapple_masses[C_is_on][1] += tip_masses_empty[3][1]\n",
" combined_grapple_heights = copy.deepcopy(combined_grapple_masses) #Use deepcopy unless you want to copy by reference so changes to the copied list also change the original list.\n",
" for j in range(1,3): #This ignores the moon sling grapples, but they're not used in the animation anyway.\n",
" for k in range(2): #Loop through capture orbit sling arms.\n",
" combined_grapple_heights[j][k] = 3*(combined_grapple_masses[j][k]/payload_density)/combined_payload_widths[j][k]^2 #Square pyramid volume = 1/3*width^2*height: http://mathworld.wolfram.com/SquarePyramid.html\n",
"\n",
" mars_pos = nullvec\n",
" sun_pos = a_pl*com_cs_pos0/com_cs_pos0.norm()\n",
" #Sunlight RGB values from: http://www.vendian.org/mncharity/dir3/starcolor/sun.html http://archive.li/0jCa2\n",
" #http://web.archive.org/web/20171206190251/http://casa.colorado.edu/~ajsh/colour/Tspectrum.html\n",
" #https://www.rapidtables.com/convert/color/hex-to-rgb.html\n",
" #sunlight=(255/255,241/255,237/255) # \"#fff1ed - the site, CIE 1931 CMFs, and Rec.709.\"\n",
" sunlight=(255/255,242/255,239/255) # \"#fff2ef - the same chromaticity, but converted to pixel color using sRGB instead.\"\n",
" #sunlight=(255/255,243/255,234/255) # \"#fff3ea - my numbers (for comparison)\"\n",
"\n",
" anim_file = 'slings.mp4' #Use a \".gif\" extension to play on Android. Use \".mp4\" or \".ogg\" for desktop computers.\n",
" os.system('rm *.'+anim_file[-3:]) #remove the old animation\n",
" os.system('rm anim*.png') #remove the old frames\n",
" filename_pattern = 'anim%04d.png'\n",
" #Converted textures to PPM format using ImageMagick in a terminal: \"convert name.dds name.ppm\"\n",
" starsfile = 'starmap_8k.ppm' #Deep star maps from https://svs.gsfc.nasa.gov/3895\n",
" marsfile = 'Mars_2k-050104.ppm' #Mars surface textures from http://www.celestiamotherlode.net/catalog/mars.php http://archive.li/rTqZj\n",
" if os.path.isfile(starsfile): stars_here = 1\n",
" else:\n",
" backup_starsfile = 'starmap_4k.ppm'\n",
" if os.path.isfile(backup_starsfile):\n",
" stars_here = 1\n",
" print warnstring,'Stars texture file',starsfile,'wasn\\'t found, so the backup file',backup_starsfile,'was used.'\n",
" starsfile = backup_starsfile\n",
" else:\n",
" stars_here = 0\n",
" print warnstring,'Stars texture files',starsfile,'and',backup_starsfile,'weren\\'t found, so the sky is textured black.'\n",
" if os.path.isfile(marsfile): mars_here = 1\n",
" else:\n",
" mars_here = 0\n",
" print warnstring,' Mars texture file',marsfile,'wasn\\'t found, so Mars is textured reddish-brown.'\n",
" mars_string = marsfile+' center 0.0 0.0 0.0 rotate 0.0 0.0 0.0 scale 1.0 1.0 1.0' #Really wish these rotations worked...\n",
" offset = hub_length/2*cs_axis\n",
" #Disable solar panels for the perpendicular setup (i.e. trajopt < 100) because in that case the solar panel geometry is much more complicated. Even the radiators will be facing the Sun in the perpendicular setup, but keep drawing them because otherwise it's hard to see the hub from a viewpoint along the capture sling axis cs_axis\n",
" if trajopt >= 100:\n",
" #Solar panels are oriented along the capture sling axis, and the other vector that's also perpendicular to the sun_pos vector. Note: this is only the \"direction to the Sun\" if the capture sling center of mass is at (0,0,0) which is the center of Mars here. This fact is ignored because otherwise this other solar panel axis would have to be recalculated in every frame for very little benefit.\n",
" other_solar_axis = cs_axis.cross_product(sun_pos/sun_pos.norm())\n",
" other_solar_axis /= other_solar_axis.norm()\n",
"\n",
" periapsis_clearance = periapsis_capture-r_pl-atmo_alt\n",
" timespan = RDF(end_time-start_time) #Sling time\n",
" numframes_REAL = RDF(numframes)\n",
" anim_length = numframes_REAL/RDF(fps) #Length of the animation, in seconds.\n",
" speedup = timespan/anim_length\n",
" bitrate = floor(filesize*8*2^20/anim_length) #Bitrate in bits per second\n",
" print anim_file,', resolution:',xresn,'x',yresn,', numframes:',numframes,', fps:',fps,', simulation timespan:',(timespan/60.0).n(digits=2),'minutes, video length:',(anim_length).n(digits=2),'seconds, speedup:',(speedup).n(digits=2),', desired filesize:',(filesize).n(digits=2),'MB, bitrate:',bitrate,'bits/second, viewtype:',viewtype,', atmo_layers:',atmo_layers,', orbit_lines:',orbit_lines\n",
"\n",
" if viewtype == 10: #View from another orbit. Sling overtakes camera.\n",
" camera_zrot = 0.5*pi/180\n",
" camera_pos0 = rotateZ(camera_zrot)*1.005*com_cs_pos0 \n",
" camera_vel0 = rotateY(0.5*pi/180)*rotateZ(camera_zrot)*0.95*com_cs_vel0\n",
" kepler_cam = xyz2kepler(camera_pos0,camera_vel0)\n",
"\n",
" #if orbit_lines > 0: payload_pos = calc_payload_pos(orbit_lines,[1,2]) #Calculate \"orbit_lines\" payload/balance positions for capture slings, which have indices 1 and 2.\n",
"\n",
" def draw_rectangle(t,rect_texture_string,rect_center,rect_side1,rect_side2):\n",
" \"\"\"\n",
" Input : Tachyon scene t, texture string, a position vector of rectangle's center, two direction vectors of the edges connected to the lower left corner (pointing \"right\" and then \"up\").\\n\n",
" Output: Tachyon scene t with an added rectangle made out of triangles displaced a \"tiny\" distance from each other to avoid problems in Tachyon where two triangles share the same vertices.\n",
" \"\"\"\n",
" side1_n = rect_side1/rect_side1.norm() #Points \"right\".\n",
" side2_n = rect_side2/rect_side2.norm() #Points \"up\".\n",
" rectA1 = rect_center - rect_side1/2 - rect_side2/2 #Rectangle corners starting at lower left, going clockwise.\n",
" rectA2 = rectA1 + rect_side2\n",
" rectA3 = rectA2 + rect_side1\n",
" rectA4 = rectA3 - rect_side2\n",
" t.triangle(tach(rectA1-tiny*side1_n),tach(rectA2),tach(rectA3-tiny*side1_n),rect_texture_string) #upper left triangle.\n",
" t.triangle(tach(rectA1),tach(rectA3),tach(rectA4),rect_texture_string) #lower right triangle.\n",
" return t\n",
"\n",
" def draw_box(t,box_texture_string,box_center,box_side1,box_side2,side3_length):\n",
" \"\"\"\n",
" Input : Tachyon scene t, texture string, a position vector of box's center, two direction vectors of the edges connected to the top side's left near corner (pointing \"right\" and then \"into the screen\"). The vector pointing \"up\" is calculated as the cross product of the \"right\" and \"into the screen\" vectors, then its magnitude is scaled to side3_length for the last box dimension.\\n\n",
" Output: Tachyon scene t with an added box made out of rectangles (made out of triangles) displaced a \"tiny\" distance from each other to avoid problems in Tachyon where two triangles share the same vertices.\n",
" \"\"\"\n",
" side1_n = box_side1/box_side1.norm() #Points \"right\".\n",
" side2_n = box_side2/box_side2.norm() #Points \"into the screen\".\n",
" side3_n = side1_n.cross_product(side2_n) #Points \"up\".\n",
" box_side3 = side3_length*side3_n\n",
" #Top and bottom sides:\n",
" t = draw_rectangle(t,box_texture_string,box_center + box_side3*(0.5 + tiny),box_side1,box_side2)\n",
" t = draw_rectangle(t,box_texture_string,box_center - box_side3*(0.5 + tiny),box_side1,box_side2)\n",
" #Front (i.e. \"near\") and back (i.e. \"into the screen\") sides:\n",
" t = draw_rectangle(t,box_texture_string,box_center - box_side2*(0.5 + tiny),box_side1,box_side3)\n",
" t = draw_rectangle(t,box_texture_string,box_center + box_side2*(0.5 + tiny),box_side1,box_side3)\n",
" #Left and right sides:\n",
" t = draw_rectangle(t,box_texture_string,box_center - box_side1*(0.5 + tiny),box_side2,box_side3)\n",
" t = draw_rectangle(t,box_texture_string,box_center + box_side1*(0.5 + tiny),box_side2,box_side3)\n",
" return t\n",
"\n",
" def draw_pyramid(t,pyramid_texture_string,pyramid_center,pyramid_side1,pyramid_side2,pyramid_height):\n",
" \"\"\"\n",
" Input : Tachyon scene t, texture string, a position vector of pyramid's center, two direction vectors of the edges connected to the pyramid base's left near corner (pointing \"right\" and then \"into the screen\"). The vector pointing \"up\" is calculated as the cross product of the \"right\" and \"into the screen\" vectors, then its magnitude is scaled using pyramid_height.\\n\n",
" Output: Tachyon scene t with an added pyramid made out of rectangles (made out of triangles) displaced a \"tiny\" distance from each other to avoid problems in Tachyon where two triangles share the same vertices.\n",
" \"\"\"\n",
" #In retrospect, it probably would have made things easier if the \"up\" vector were specified, not derived. Instead, maybe the \"right\" vector could be derived.\n",
" sides_n = [pyramid_side1/pyramid_side1.norm()] #Points \"right\".\n",
" sides_n += [pyramid_side2/pyramid_side2.norm()] #Points \"into the screen\".\n",
" sides_n += [sides_n[0].cross_product(sides_n[1])] #Points \"up\".\n",
" pyramid_side3 = pyramid_height*sides_n[2]\n",
" #Base:\n",
" t = draw_rectangle(t,pyramid_texture_string,pyramid_center - pyramid_side3*(0.5 + tiny),pyramid_side1,pyramid_side2)\n",
" #Sides:\n",
" apex = pyramid_center + 0.5*pyramid_side3\n",
" base_vertices = [pyramid_center - 0.5*pyramid_side3 - pyramid_side1/2 - pyramid_side2/2] #Base vertices starting at near left, going clockwise.\n",
" base_vertices += [base_vertices[0] + pyramid_side2] #far left\n",
" base_vertices += [base_vertices[1] + pyramid_side1] #far right\n",
" base_vertices += [base_vertices[2] - pyramid_side2] #near right\n",
" #Need to displace vertices because apparently triangles can't share the same vertices.\n",
" displacement_n = [-sides_n[1]] #Front side is first.\n",
" displacement_n += [-sides_n[0]] #Then left side.\n",
" displacement_n += [+sides_n[1]] #Then far side.\n",
" displacement_n += [+sides_n[0]] #Then right side.\n",
" for sidenum in range(4): t.triangle(tach(base_vertices[sidenum]-tiny*displacement_n[sidenum]),tach(base_vertices[sidenum-1]+tiny*displacement_n[sidenum]),tach(apex+tiny*displacement_n[sidenum]),pyramid_texture_string)\n",
" return t\n",
"\n",
" def draw_hub(t,hub_texture_string,hub_center,hub_axis,hub_radius):\n",
" \"\"\"\n",
" Input : Tachyon scene t, texture string, a position vector of hub's center, a vector of the hub's axis (with a magnitude equal to the length of the hub), and the hub radius.\\n\n",
" Output: Tachyon scene t with an added hub.\n",
" \"\"\"\n",
" if retrograde_anchor: #Draw a hub that doesn't intersect with the hawsepipe.\n",
" hub_length = hub_axis.norm()\n",
" hub_axis_n = hub_axis/hub_length\n",
" t.fcylinder(tach(hub_center+hub_axis/2),tach(hub_center+(hub_axis/2-hub_stub*hub_axis_n)),hub_radius,hub_texture_string) #top stub where sling A attaches.\n",
" t.fcylinder(tach(hub_center-hub_axis/2),tach(hub_center-(hub_axis/2-hub_stub*hub_axis_n)),hub_radius,hub_texture_string) #bottom stub where sling B attaches.\n",
" else: t.fcylinder(tach(hub_center+hub_axis/2),tach(hub_center-hub_axis/2),hub_radius,hub_texture_string)\n",
" return t\n",
"\n",
" def draw_radiator(t,rad_texture_string,rad_center,rad_axis,rad_radius):\n",
" \"\"\"\n",
" Input : Tachyon scene t, texture string, a position vector of radiator's center, a vector of the radiator's axis, and the radiator radius.\\n\n",
" Output: Tachyon scene t with an added radiator.\n",
" \"\"\"\n",
" t.ring(tach(rad_center),tach(rad_axis),0,rad_radius,rad_texture_string)\n",
" #For some reason this sun shade shows up at a completely different place(s) around Mars, and it seems to be oriented with its axis perpendicular to the requested cs_axis/rad_axis. t.fcylinder(tach(rad_center+light_rad*rad_axis),tach(rad_center-light_rad*rad_axis),rad_radius+tiny,'hub') #Shade for radiator.\n",
" return t\n",
"\n",
" def draw_hawsepipe(t,hawsepipe_texture_string,hawsepipe_center,hawsepipe_axis,hawsepipe_radius,hawsepipe_coils,hawsepipe_gap):\n",
" \"\"\"\n",
" Input : Tachyon scene t, texture string, a position vector of hawsepipe's center, a vector of the hawsepipe's axis (with a magnitude equal to the length of the hawsepipe), the number of coils and the gap fraction for each coil.\\n\n",
" Output: Tachyon scene t with an added hawsepipe (for the anchor links to pass through).\n",
" \"\"\"\n",
" hawsepipe_length = hawsepipe_axis.norm()\n",
" hawsepipe_axis_n = hawsepipe_axis/hawsepipe_length\n",
" #end1 = hawsepipe_center - hawsepipe_axis/2\n",
" #end2 = hawsepipe_center + hawsepipe_axis/2\n",
" coil_width = hawsepipe_length/hawsepipe_coils*(1-hawsepipe_gap)\n",
" #centers = hawsepipe_center - hawsepipe_axis/2 + (coil_width/2 + j*hawsepipe_length/hawsepipe_coils)*hawsepipe_axis_n\n",
" #This used to be \"for j in range(hawsepipe_coils)\" but then the last coil vanished at some point. Hmmm...\n",
" for j in range(hawsepipe_coils+1): t.fcylinder(tach(hawsepipe_center - hawsepipe_axis/2 + (coil_width/2 + j*hawsepipe_length/hawsepipe_coils)*hawsepipe_axis_n + coil_width/2*hawsepipe_axis_n),tach(hawsepipe_center - hawsepipe_axis/2 + (coil_width/2 + j*hawsepipe_length/hawsepipe_coils)*hawsepipe_axis_n - coil_width/2*hawsepipe_axis_n),hawsepipe_radius,hawsepipe_texture_string)\n",
" #Draw axial supports:\n",
" if hawsepipe_supports:\n",
" support_center_vec0 = hawsepipe_axis_n.cross_product(cs_axis)\n",
" support_center_vec0 = hawsepipe_radius*cos(support_width/2)*support_center_vec0/support_center_vec0.norm()\n",
" support_side_vec0 = hawsepipe_radius*2*sin(support_width/2)*cs_axis # https://en.wikipedia.org/wiki/Chord_(geometry)#In_trigonometry\n",
" for j in range(hawsepipe_supports):\n",
" support_rotate = rotateV(hawsepipe_axis_n,2*pi*(j+0.2)/hawsepipe_supports)\n",
" support_center_vec = support_rotate*support_center_vec0 # Rotate vector pointing from hawsepipe center to support center.\n",
" support_side_vec = support_rotate*support_side_vec0 # Rotate vector pointing along the edge of the support.\n",
" t = draw_rectangle(t,hawsepipe_texture_string,hawsepipe_center+support_center_vec,hawsepipe_axis,support_side_vec)\n",
" #Draw the part of the hub that mounts the hawsepipe:\n",
" inner_rad = hawsepipe_radius+tiny\n",
" outer_rad = hawsepipe_radius+hub_rad+tiny\n",
" t.fcylinder(tach(hawsepipe_center - hub_rad*hawsepipe_axis_n),tach(hawsepipe_center + hub_rad*hawsepipe_axis_n),inner_rad,'hub') #inner cylinder\n",
" t.fcylinder(tach(hawsepipe_center - hub_rad*hawsepipe_axis_n),tach(hawsepipe_center + hub_rad*hawsepipe_axis_n),outer_rad,'hub') #outer cylinder\n",
" t.ring(tach(hawsepipe_center - hub_rad*hawsepipe_axis_n),tach(hawsepipe_axis_n),inner_rad,outer_rad,'hub') #end cap\n",
" t.ring(tach(hawsepipe_center + hub_rad*hawsepipe_axis_n),tach(hawsepipe_axis_n),inner_rad,outer_rad,'hub') #end cap\n",
" return t\n",
"\n",
" %time\n",
" @parallel() #Supposed to be able to put the number of cores to use inside these parentheses, but it never seemed to work...\n",
" def cs_throw_frame(n, filename):\n",
" \"\"\"\n",
" Input : Index of current frame, and the filename to save this animation frame to.\\n\n",
" Output: A single frame of the capture sling throw animation, saved to disk as \"filename\".\n",
" \"\"\"\n",
" time = start_time + (RDF(n)/numframes_REAL)*timespan\n",
" tips_pos, tips_vel = sling_tips(time)\n",
" kepler_com_cs[5] = time\n",
" com_cs_pos,com_cs_vel = kepler2xyz(kepler_com_cs)\n",
" com_cs_pos_n = com_cs_pos/com_cs_pos.norm() #Define a unit vector pointing along the orbital position vector.\n",
" com_cs_vel_n = com_cs_vel/com_cs_vel.norm() #Define a unit vector pointing along the orbital velocity vector.\n",
" centers = [-1, com_cs_pos + offset,com_cs_pos - offset] #Centers of mass for slings M,A,B. (Moon sling at index 0 is just a placeholder.)\n",
" if viewtype == 0: #Side view\n",
" lookat = mars_pos + (r_pl + 0.1*(com_cs_pos.norm()-r_pl))*(sun_pos/sun_pos.norm()) #Look in the general vicinity of the subsolar point of Mars.\n",
" camera_pos = com_cs_pos + 2.8*light_spacing*(com_cs_pos-lookat)/(com_cs_pos-lookat).norm()# + vector([0,0,0.01*longest_capture_arm])\n",
" up_dir = H_cs_unit\n",
" elif viewtype == 1: #Overhead view\n",
" lookat = (r_pl + atmo_alt + 0.6*(com_cs_pos.norm()-r_pl-atmo_alt))*com_cs_pos/com_cs_pos.norm()\n",
" camera_pos = com_cs_pos + H_cs_unit*cam_const1*((com_cs_pos.norm()-r_pl)/(periapsis_clearance))^cam_const2*(com_cs_pos.norm()-r_pl)\n",
" if goingup: up_dir = rotateZ(pi/2)*sun_pos\n",
" else: up_dir = -sun_pos\n",
" elif viewtype == 2: #Simple overhead view where the camera is always directly above the hub.\n",
" lookat = com_cs_pos\n",
" camera_pos = com_cs_pos + H_cs_unit*longest_capture_arm*2\n",
" if goingup: up_dir = rotateZ(pi/2)*sun_pos\n",
" else: up_dir = -sun_pos\n",
" elif viewtype == 10: #View from another orbit. Sling overtakes camera.\n",
" kepler_cam[5] = time\n",
" camera_pos,camera_vel = kepler2xyz(kepler_cam)\n",
" lookat = com_cs_pos\n",
" up_dir = H_cs_unit\n",
" elif viewtype >= 100 and viewtype < 200: #Payload cam\n",
" tips_vel_n = tips_vel[pc_j][pc_k]/tips_vel[pc_j][pc_k].norm()\n",
" tips_pos_n = tips_pos[pc_j][pc_k]/tips_pos[pc_j][pc_k].norm()\n",
" if viewtype == 100:\n",
" cam_offset = nullvec\n",
" cam_offset_n = nullvec\n",
" elif viewtype == 150:\n",
" cam_offset = payload_width*10*(0.4*cs_axis - 0.3*tips_vel_n + 0.6*tips_pos_n) #Position the payload in the bottom of the screen; leave the hub visible.\n",
" cam_offset_n = cam_offset/cam_offset.norm()\n",
" else:\n",
" print warnstring,'viewtype',viewtype,'was not recognized in payload cam section!'\n",
" print failure\n",
" if time < throw_times[pc_j]: camera_pos = centers[pc_j] + tips_pos[pc_j][pc_k] + cam_offset\n",
" else:\n",
" keplers[pc_j][pc_k][5] = time - throw_times[pc_j] + throw_tpers[pc_j][pc_k]\n",
" mass_pos,mass_vel = kepler2xyz(keplers[pc_j][pc_k])\n",
" if pc_j == 1: someneg = 1\n",
" else: someneg = -1\n",
" camera_pos = mass_pos+someneg*offset + cam_offset\n",
" lookat = camera_pos - tips_pos_n - 1.5*cam_offset_n #Payload camera looks back along sling toward the hub.\n",
" up_dir = cs_axis\n",
" else:\n",
" print warnstring,'viewtype',viewtype,'was not recognized!'\n",
" print failure\n",
" t = Tachyon(xres=xresn,yres=yresn, aspectratio=aspectr, camera_center=tach(camera_pos), look_at=tach(lookat), updir=tach(up_dir), raydepth=max([atmo_layers+3,5]), antialiasing=True)\n",
" t.light(tach(sun_pos), radius_sun, sunlight) #Draw the Sun.\n",
" t.texture('light1', ambient=1.0, diffuse=0.0, color=light1_color)\n",
" t.texture('light2', ambient=1.0, diffuse=0.0, color=light2_color)\n",
" t.texture('light1_pc',ambient=pc_ambient, diffuse=0.0, opacity=pc_opacity, color=light1_color) #Mostly transparent so payload cam can see.\n",
" t.texture('light2_pc',ambient=pc_ambient, diffuse=0.0, opacity=pc_opacity, color=light2_color)\n",
" t.texture('payload0', ambient=payload_ambient, diffuse=payload_diffuse, color=payload0_color)\n",
" t.texture('payload1', ambient=payload_ambient, diffuse=payload_diffuse, color=payload1_color)\n",
" t.texture('grapple', ambient=payload_ambient, diffuse=payload_diffuse, color=grapple_color)\n",
" t.texture('hub', ambient=hub_ambient, diffuse=hub_diffuse, color=hub_color)\n",
" t.texture('radiator', ambient=hub_ambient, diffuse=hub_diffuse, color=hub_color)\n",
" t.texture('mirror', ambient=0.0, diffuse=0.0,specular=1.0,color=hub_color)\n",
" t.texture('anchor', ambient=payload_ambient, diffuse=payload_diffuse, color=anchor_color)\n",
" t.texture('solar', ambient=solar_ambient, diffuse=solar_diffuse, specular=solar_specular, color=solar_color)\n",
" t = draw_hub(t,'hub',com_cs_pos,2*(offset.norm()+light_rad)*cs_axis,hub_rad)\n",
" t = draw_radiator(t,'radiator',centers[1]+light_rad*cs_axis,cs_axis,radiator_rad) #radiator A\n",
" t = draw_radiator(t,'radiator',centers[2]-light_rad*cs_axis,-cs_axis,radiator_rad) #radiator B\n",
" if trajopt >= 100: #Draw top and bottom solar panels\n",
" t = draw_rectangle(t,'solar',centers[1]+(light_rad+solar_width/2)*cs_axis,solar_width*cs_axis,solar_width*other_solar_axis)\n",
" t = draw_rectangle(t,'solar',centers[2]-(light_rad+solar_width/2)*cs_axis,solar_width*cs_axis,solar_width*other_solar_axis)\n",
" if retrograde_anchor:\n",
" t = draw_hawsepipe(t,'hub',com_cs_pos,2*hawsepipe_multiplier*offset.norm()*com_cs_vel_n,hub_rad,hawsepipe_coils,hawsepipe_gap)\n",
" for j in range(anchor_links): #Loop through all anchor links.\n",
" kepler_anchor[5] = time + j/anchor_links*anchor_spread\n",
" anchor_pos,anchor_vel = kepler2xyz(kepler_anchor)\n",
" t.sphere(tach(anchor_pos), 1.5*light_rad, 'anchor') # This illuminates the planet: t.light(tach(anchor_pos), payload_width/2, anchor_color)\n",
" for j in range(1,3-disable_sling_B): #Loop through capture orbit slings (include sling B only if it isn't disabled).\n",
" for k in range(2): #Loop through capture orbit sling arms.\n",
" lightpos = hub_rad+light_rad #Put the first light right next to the hub.\n",
" tips_pos_n = tips_pos[j][k]/tips_pos[j][k].norm() #For speed, define a unit vector in the direction of this tip's position.\n",
" tips_vel_n = tips_vel[j][k]/tips_vel[j][k].norm() #For speed, define a unit vector in the direction of this tip's velocity.\n",
" #Draw lights along the slings, but stop before getting to the edges of the grapples.\n",
" while lightpos < tips_pos[j][k].norm() - (combined_payload_widths[j][k]/2+combined_grapple_heights[j][k]):\n",
" if time > throw_times[j] or viewtype != 100 or j != pc_j or k != pc_k: texture_string = 'light%s'%j\n",
" else: texture_string = 'light%s_pc'%j\n",
" t.sphere(tach(centers[j] + lightpos*tips_pos_n), light_rad, texture_string)\n",
" lightpos += light_spacing\n",
" #Draw the payloads, counterbalances and grapples.\n",
" grapple_position = centers[j] + tips_pos[j][k] - tips_pos_n*(combined_payload_widths[j][k]+combined_grapple_heights[j][k])/2\n",
" grapple_axis1 = (-1)^(j+1)*combined_payload_widths[j][k]*tips_vel_n #Negative for sling B so it points right using the overhead view.\n",
" grapple_axis2 = -combined_payload_widths[j][k]*cs_axis #Negative for both slings so it points into the screen using the overhead view.\n",
" if viewtype != 100 or j != pc_j or k != pc_k: #Skip if this is the payload with the payload cam.\n",
" t = draw_pyramid(t,'grapple',grapple_position,grapple_axis1,grapple_axis2,combined_grapple_heights[j][k])\n",
" if time < throw_times[j]: payload_position = centers[j] + tips_pos[j][k]\n",
" else:\n",
" payload_position = centers[j] + tips_pos[j][k]\n",
" keplers[j][k][5] = time - throw_times[j] + throw_tpers[j][k]\n",
" mass_pos,mass_vel = kepler2xyz(keplers[j][k])\n",
" if j == 1: someneg = 1\n",
" else: someneg = -1\n",
" payload_position = mass_pos+someneg*offset\n",
" if orbit_lines > 0: #Only draw orbit lines for this payload if it isn't the payload cam.\n",
" for o_n in range(1,len(payload_pos[j][k])):\n",
" t.fcylinder(tach((1-tiny)*payload_pos[j][k][o_n-1]),tach(payload_pos[j][k][o_n]),light_rad,'payload%s'%k)\n",
" t = draw_box(t,'payload%s'%k,payload_position,combined_payload_widths[j][k]*tips_vel_n,combined_payload_widths[j][k]*cs_axis,combined_payload_widths[j][k])\n",
" elif time > throw_times[j]: #Render the grapple even if this is the payload with the payload cam, but only render it AFTER the throw.\n",
" t = draw_pyramid(t,'grapple',grapple_position,grapple_axis1,grapple_axis2,combined_grapple_heights[j][k])\n",
" #If the commands below are moved above the capture sling commands (i.e. right below the texture definitions) then the hub and hawsepipe both fail to render. This seems like a clue to the underlying problem, but no time to decipher it.\n",
" #marsfunc = t.texfunc(type=8, imagefile='Mars_2k-050104.ppm', rotate=(0,0,0)) #This doesn't actually rotate the texture when (0,0,0) is changed, but this page implies it should: https://trac.sagemath.org/ticket/799\n",
" #t.texture('mars', imagefile='Mars_2k-050104.ppm', texfunc=marsfunc)\n",
" if mars_here: t.texture('mars', ambient=0, imagefile=marsfile, texfunc=8)\n",
" else: t.texture('mars', ambient=0, color=backup_mars)\n",
" t.sphere(tach(mars_pos), r_pl, 'mars') #Draw Mars.\n",
" if atmo_layers > 0:\n",
" t.texture('atmo', opacity = atmo_opacity, ambient = atmo_ambient, diffuse = atmo_diffuse, color=(1,1,1))\n",
" for k in range(atmo_layers): t.sphere(tach(mars_pos), r_pl+atmo_alt*(k+1.0)/atmo_layers, 'atmo')\n",
" if stars_here: t.texture('stars', ambient=stars_ambient, diffuse=0.0, imagefile=starsfile, texfunc=8)\n",
" else: t.texture('stars', ambient=0 , diffuse=0.0, color=(0,0,0))\n",
" #t.show() #This won't show the rotations applied below, but apparently nothing will...\n",
" t.sphere(tach(sun_pos), a_pl*10, 'stars') #Here the stars are a texture on a sphere that's 10x larger than the planet's orbit.\n",
" t_str = t.str()\n",
" if mars_here:\n",
" rot_string = 'rotate {:03.1f} {:03.1f} {:03.1f}'.format(0,0,0)\n",
" new_mars_string = mars_string.replace('rotate 0.0 0.0 0.0',rot_string)\n",
" #new_mars_string = mars_string.replace(marsfile,starsfile) #just testing to see if this works because I can't seem to change the texture rotation at all. Texturing Mars with the starfield kind of makes it look like an event horizon...\n",
" t_str = t_str.replace(mars_string,new_mars_string)\n",
" #if n == 0: print t_str\n",
" tachyon_rt(t_str,outfile=filename,verbose=0)\n",
" #t.save(filename) #Old way of saving with no obvious way to alter the Tachyon string used.\n",
" if n%notif==0: print '.',\n",
"\n",
" if anim_file[-3:] == 'gif': anim_command = 'ffmpeg -f image2 -r %f -i %s -b:v %d %s' % (fps, filename_pattern, bitrate, anim_file)\n",
" elif anim_file[-3:] == 'ogg': anim_command = 'ffmpeg -f image2 -r %f -i %s -b:v %d %s' % (fps, filename_pattern, bitrate, anim_file)\n",
" elif anim_file[-3:] == 'mp4': anim_command = 'ffmpeg -f image2 -r %f -i %s -c:a aac -c:v libx264 -pix_fmt yuv420p -movflags faststart -b:v %d %s' % (fps, filename_pattern, bitrate, anim_file)\n",
" else: print warnstring,'Animation file extension/\"',anim_file[-3:],'/\" is not supported.'\n",
"\n",
" if numframes > 1:\n",
" print '\\nIf this cell crashes, try running the following command in a terminal before running this cell again (which would delete the frames):'\n",
" print anim_command,'\\n'\n",
" start = walltime()\n",
" if numframes > 1: print '-' * (numframes//notif)+'| ->',numframes,'frames to calculate:' #This bar shows the number of notifications (\".\") that need to be returned until the rendering is finished.\n",
" else: print numframes,'frame to calculate...'\n",
" len(list(cs_throw_frame([(n,filename_pattern%i) for i, n in enumerate(range(numframes))])))\n",
"\n",
" print '\\nFinished rendering frames.'\n",
" if numframes > 1:\n",
" print 'Now using ffmpeg to create the animation...'\n",
" os.system(anim_command)\n",
"\n",
" print '\\nwalltime() -start =',walltime() -start,'seconds.'\n",
" if exagg > 1+tolerance: print exagg_warning\n",
"\n",
" #salvus.file(anim_file) #This doesn't work in Jupyter notebooks.\n",
" if textonly == 0: display(Image(filename=filename_pattern%0))\n",
" else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'\n",
" if exagg > 1+tolerance: print exagg_warning\n",
" #if numframes > 1: #Displaying the animation doesn't seem to work after converting to ipython. So in CoCalc, just download the animation from \"files\".\n",
" # from IPython.display import HTML\n",
" # html_string = ''% (xresn, yresn, anim_file)\n",
" # print html_string\n",
" # HTML(html_string)\n",
" set_verbose(initial_verbose)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.system('rm anim*.png') #remove the old frames"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 17. Make a 3D plot of the payloads and counterbalances to assess the risk of collisions. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"selected_payload_periods = [[15787.8287031397*pi, 57452.2833455143*pi]]\n",
"longest_orbit_period (seconds) = 180491.671290227\n",
"Calculating payload positions. \n",
"orbit_timestep for payload [0][0] = 16.5329755566393 minutes.\n",
". \n",
"orbit_timestep for payload [0][1] = 60.1638904300756 minutes.\n",
".\n",
"time = 184101.504716031\n",
"n= 51\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print capture_frame_string\n",
"initial_verbose=get_verbose()\n",
"set_verbose(0)\n",
"\n",
"def calc_payload_pos(numpos,sindices):\n",
" \"\"\"\n",
" Input : Number of positions to calculate, list of sling indices.\\n\n",
" Output: Positions of each sling's payload and counterbalance after being thrown. If they're in elliptical orbits then record positions for complete orbits.\n",
" \"\"\"\n",
" #What's the longest period of any selected payload or counterbalance?\n",
" payload_periods = [[-1.0 for i in range(2)] for j in range(num_slings)] #Periods of payloads or counterbalances.\n",
" for j in range(num_slings): #Loop through all slings.\n",
" for k in range(2): #Loop through sling arms.\n",
" if hasattr(keplers[j][k], \"__len__\"): #Skip this sling if its keplers entry doesn't have a length (so it's likely just a number like -1).\n",
" if keplers[j][k][0] > 0: payload_periods[j][k] = 2*pi*sqrt(keplers[j][k][0]^3/mu) #'0' is the keplers index for the semi-major axis.\n",
" else: payload_periods[j][k] = -1 #Hyperbolic trajectories don't have periods.\n",
" selected_payload_periods = [payload_periods[x] for x in sindices]\n",
" print 'selected_payload_periods =',selected_payload_periods\n",
" if len(selected_payload_periods) > 1: longest_orbit_period = (max(map(max, *selected_payload_periods))).n()\n",
" else: longest_orbit_period = max(selected_payload_periods[0])\n",
"\n",
" print 'longest_orbit_period (seconds) =',(longest_orbit_period).n()\n",
" payload_pos = [[[] for i in range(2)] for j in range(num_slings)] #Payload positions at different times.\n",
" print 'Calculating payload positions.',\n",
" for j in sindices: #Loop through specified sling indices.\n",
" for k in range(2): #Loop through sling arms.\n",
" n=-1 #The while loop increments 'n' at the beginning. Set n=-1 so the first while loop starts calculating at n=0.\n",
" if payload_periods[j][k] > 0: orbit_timestep = payload_periods[j][k]/numpos\n",
" else: orbit_timestep = longest_orbit_period/numpos\n",
" print '\\norbit_timestep for payload ['+str(j)+']['+str(k)+'] =',(orbit_timestep/60).n(),'minutes.'\n",
" while true:\n",
" n += 1\n",
" time = RDF(n)*orbit_timestep\n",
" #print 'keplers[',j,'][',k,'] =',keplers[j][k]\n",
" keplers[j][k][5] = time - throw_times[j] + throw_tpers[j][k]\n",
" mass_pos,mass_vel = kepler2xyz(keplers[j][k])\n",
" if j == 0: payload_pos[j][k] += [mass_pos]\n",
" else:\n",
" if j == 1: someneg = 1\n",
" else: someneg = -1\n",
" payload_pos[j][k] += [mass_pos+someneg*offset]\n",
" if time > orbit_timestep*numpos: break\n",
" if n%(notif*10) == 0: print '.',\n",
" print\n",
" print 'time =',time.n()\n",
" print 'n=',n\n",
" return payload_pos\n",
"\n",
"from sage.plot.plot3d.shapes import Torus, Sphere\n",
"Mars_Sphere = Sphere(r_pl,color=(1,0,0))\n",
"if moon_name == 'Phobos':\n",
" moon_color = (1,0,1)\n",
" other_color = (0,1,0)\n",
" a_other = a_deimos\n",
" inc_other = inc_deimos\n",
"else:\n",
" moon_color = (0,1,0)\n",
" other_color = (1,0,1)\n",
" a_other = a_phobos\n",
" inc_other = inc_phobos\n",
"exagg = 1\n",
"if exagg > 1+tolerance: print warnstring+'Exclusion zone around moons and payloads is exaggerated by a factor of '+str(exagg)+', just for visibility when r_max is too small.'\n",
"numpos = 50 #Values much higher than ~50 seem to crash the jmol viewer.\n",
"Chosen_Moon_Torus = Torus(a_moon, r_max*exagg, color = moon_color).rotateX(inc_moon)\n",
"Other_Moon_Torus = Torus(a_other, r_max*exagg, color = other_color).rotateX(inc_other)\n",
"everything = Mars_Sphere + Chosen_Moon_Torus + Other_Moon_Torus\n",
"#Select payload index 0 (moon sling) to calculate \"numpos\" payload/balance positions.\n",
"if cbrs[1] < tolerance: pindices = [0] #If the capture sling is disabled, only plot payloads from the moon sling.\n",
"else: pindices = [0] #Or take your pick. Plot the moon sling and/or select payload indices 1 and 2 (capture slings) to calculate \"numpos\" payload/balance positions.\n",
"payload_pos = calc_payload_pos(numpos,pindices)\n",
"payload_colors = [(0,0,1),(1,1,0)]\n",
"for j in pindices: #Loop through whichever payload indices were chosen.\n",
" for k in range(2):\n",
" for o_n in range(len(payload_pos[j][k])):\n",
" everything += Sphere(r_max*exagg,color=payload_colors[k]).translate(payload_pos[j][k][o_n])\n",
"\n",
"if textonly == 0:\n",
" interactive_plot = 0 #Set to 1 for an interactive (but slow) 3D plot. Set to 0 for a quick 3D snapshot.\n",
" if interactive_plot != 0: show(everything, aspect_ratio=(1,1,1), zoom=1.3, frame=false, viewer='jmol') #Default viewer is 'jmol'; also 'tachyon', 'threejs'.\n",
" else:\n",
" plotfilename='3D_orbit_plot.png'\n",
" everything.rotateX(0*pi/180).rotateY(0*pi/180).rotateZ(0*pi/180).save(plotfilename, aspect_ratio=(1,1,1), zoom=1.3, frame=false, figsize=8, dpi=300)\n",
" display(Image(filename=plotfilename))\n",
"else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'\n",
"set_verbose(initial_verbose)"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 18. Calculate mass of equivalent rocket. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"delta_vs = [1846.1946611564279, 1846.1946611564279]\n",
"\n",
"prop_fracs = [0.6430547940009077, 0.6430547940009077]\n",
"\n",
"payload_masses = [[4816.56194868958, 19266.2477947583], [1455.07400319400, 1443.96175550632], [1000.00000000000, 917.526189989254], [455.074003194002, 451.598650718035]]\n",
"\n",
"rocket_payloads = [1910.14800638800, 1000.00000000000]\n",
"\n",
"rocket_propellants = [1228.32983275908, 643.054794000908]\n",
"\n",
"Iterating to determine structural mass...\n",
"rocket_structures = [184.249474913862, 96.4582191001361] , rocket_propellants = [1346.81234089459, 705.082714214040]\n",
"rocket_structures = [202.021851134189, 105.762407132106] , rocket_propellants = [1358.24095262386, 711.065816932284]\n",
"rocket_structures = [203.736142893579, 106.659872539843] , rocket_propellants = [1359.34333615805, 711.642936365179]\n",
"rocket_structures = [203.901500423708, 106.746440454777] , rocket_propellants = [1359.44967011053, 711.698604277884]\n",
"rocket_structures = [203.917450516579, 106.754790641683] , rocket_propellants = [1359.45992689421, 711.703973905605]\n",
"rocket_structures = [203.918989034132, 106.755596085841] , rocket_propellants = [1359.46091624530, 711.704491850332]\n",
"rocket_structures = [203.919137436795, 106.755673777550] , rocket_propellants = [1359.46101167634, 711.704541810358]\n",
"rocket_structures = [203.919151751452, 106.755681271554] , rocket_propellants = [1359.46102088145, 711.704546629413]\n",
"rocket_structures = [203.919153132218, 106.755681994412] , rocket_propellants = [1359.46102088145, 711.704546629413]\n",
"\n",
"Finished.\n",
"\n",
"iterations = [-8, -8]\n",
"\n",
"Rocket A sends a 1.910 ton payload through a delta-v of 1846. m/s. Its total mass (structure plus propellant, no payload) is 1.563 tons. Its structural mass is 0.2039 tons, which is 15.00% of its propellant mass of 1.359 tons. Its propellant mass fraction is 0.3914 and its payload fraction is 0.5499.\n",
"\n",
"Rocket B sends a 1.000 ton payload through a delta-v of 1846. m/s. Its total mass (structure plus propellant, no payload) is 0.8185 tons. Its structural mass is 0.1068 tons, which is 15.00% of its propellant mass of 0.7117 tons. Its propellant mass fraction is 0.3914 and its payload fraction is 0.5499.\n",
"\n",
"Total rocket mass is 2.382 tons, or 0.8185 times the combined payload mass of 2.910 tons.\n",
"\n"
]
}
],
"source": [
"summary_digits = 15 #Many print statements in this notebook will print \"summary_digits\" significant figures.\n",
"\n",
"#Each payload has a (potentially different) total rocket delta-v. First, each rocket leaves the moon:\n",
"delta_vs = [radii[0]*omegas[0]]\n",
"if cbrs[1] > tolerance:\n",
" delta_vs *= 2 #If the capture sling is used, duplicate the first entry so there are two rockets, each with the same initial delta-v.\n",
" #Then each rocket performs an instantaneous burn at periapsis. The first burn was already calculated for the perpendicular sling at top.\n",
" assume(v_tip>0,r>0)\n",
" eq_v_tip2 = v_infs[mis[2]]^2 == 2*((v+v_tip)^2/2 - mu/r)\n",
" soln_v_tip2 = solve(eq_v_tip2.subs(v=capture_speed_periapsis,r=periapsis_capture),v_tip)\n",
" v_tip_capture_payload2 = soln_v_tip2[0].rhs().n()\n",
" delta_vs[0] += v_tip_capture_payload1\n",
" delta_vs[1] += v_tip_capture_payload2\n",
"\n",
"print '\\ndelta_vs =',delta_vs\n",
"\n",
"prop_fracs = [exp(x/(g*Isp))-1.0 for x in delta_vs] #These are ratios of propellant mass to payload mass. See eq 17 from Puig-Suari et al. 1995: http://web.archive.org/web/20171006015505/https://engineering.purdue.edu/people/james.m.longuski.1/JournalArticles/1995/ATetherSlingforLunarandInterplanetaryExploration.pdf They're related to the \"propellant mass fraction\" which is the ratio of propellant mass to the total initial mass of the rocket. https://en.wikipedia.org/wiki/Tsiolkovsky_rocket_equation\n",
"print '\\nprop_fracs =',prop_fracs\n",
"print '\\npayload_masses =',payload_masses\n",
"if cbrs[1] > tolerance:\n",
" rocket_payloads = [payload_masses[1][0],payload_masses[2][0]]\n",
" rocket_payloads[C_is_on-1] += payload_masses[3][0] #Subtract 1 because C_is_on usually indexes a list where the 0'th index is the moon sling.\n",
"else: rocket_payloads = [payload_mass]\n",
"\n",
"print '\\nrocket_payloads =',rocket_payloads\n",
"rocket_propellants = [temp1*temp2 for temp1,temp2 in zip(rocket_payloads,prop_fracs)]\n",
"print '\\nrocket_propellants =',rocket_propellants\n",
"\n",
"if s2p > tolerance:\n",
" iterations = [0 for x in delta_vs]\n",
" print '\\nIterating to determine structural mass...'\n",
" while true: #Loop until encountering the break command.\n",
" if max(iterations) >= 0:\n",
" rocket_structures = [s2p*x for x in rocket_propellants]\n",
" for j in range(len(delta_vs)):\n",
" if iterations[j] >= 0:\n",
" rocket_propellant = (rocket_payloads[j]+rocket_structures[j])*prop_fracs[j]\n",
" #Iterate this rocket's structural mass until its propellant changes by less than \"tolerance\".\n",
" if abs(rocket_propellant - rocket_propellants[j]) > tolerance:\n",
" rocket_propellants[j] = rocket_propellant\n",
" iterations[j] += 1\n",
" else: iterations[j] *= -1 #This rocket's iterations are now negative. When all iterations are negative, break out of the while loop.\n",
" print 'rocket_structures =',rocket_structures,', rocket_propellants =',rocket_propellants\n",
" else: break\n",
" print '\\nFinished.'\n",
" print '\\niterations =',iterations\n",
"else: rocket_structures = [0.0 for x in delta_vs]\n",
"\n",
"rocket_desc = ''\n",
"for j in range(len(delta_vs)):\n",
" rocket_desc += '\\nRocket '+sdescs[j+1]+' sends a '+str((rocket_payloads[j]/1000).n(summary_digits))+' ton payload through a delta-v of '+str((delta_vs[j]).n(summary_digits))+' m/s. Its total mass (structure plus propellant, no payload) is '+str(((rocket_structures[j]+rocket_propellants[j])/1000).n(summary_digits))+' tons. Its structural mass is '+str((rocket_structures[j]/1000).n(summary_digits))+' tons, which is '+str((rocket_structures[j]/rocket_propellants[j]*100.0).n(summary_digits))+'% of its propellant mass of '+str((rocket_propellants[j]/1000).n(summary_digits))+' tons. Its propellant mass fraction is '+str((rocket_propellants[j]/(rocket_payloads[j]+rocket_structures[j]+rocket_propellants[j])).n(summary_digits))+' and its payload fraction is '+str((rocket_payloads[j]/(rocket_payloads[j]+rocket_structures[j]+rocket_propellants[j])).n(summary_digits))+'.\\n'\n",
" if j > 0: rocket_desc += '\\nTotal rocket mass is '+str(((sum(rocket_structures)+sum(rocket_propellants))/1000).n(summary_digits))+' tons, or '+str((((sum(rocket_structures)+sum(rocket_propellants))/sum(rocket_payloads))).n(summary_digits))+' times the combined payload mass of '+str(((sum(rocket_payloads))/1000).n(summary_digits))+' tons.\\n'\n",
"print rocket_desc"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## 19. Print summary. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Summary for a 1.91 ton payload and a 1.00 ton payload thrown from Deimos around Mars by a Zylon sling with safety factor 1.00 on a M-E Hohmann mission with v_inf = 2640.0 m/s.\n",
"\n",
"COPLANAR SETUP. Capture slings release payloads A and B at r = periapsis_capture +/- sling A/B radius. Counterbalance A has a periapsis altitude of 375.79 km and the same semi-latus rectum as the specified counterbalance orbit. Counterbalance B aerobrakes at a periapsis altitude of 110.00 km.\n",
"\n",
"1 = payload arm of sling, 2 = counterbalance arm of sling. Center of mass > 0 if it's on the payload arm.\n",
"A/B = capture orbit sling which releases its payload farthest/nearest to Mars\n",
"\n",
"Capture orbit is in a 11:5 resonance with Deimos.\n",
"Capture orbit apoapsis (km) : 23821.\n",
"Capture orbit periapsis (km) : 3920.5\n",
"Capture orbit periapsis altitude (km) : 524.47\n",
"Capture orbit period (days) : 0.57406\n",
"Capture orbit revisit time (days) : 6.3147\n",
"!!!WARNING!!! The longest capture sling arm is 135.15 km long, so it can reach down to an altitude of 389.32 km.\n",
"\n",
"-----------------------------------------------------------\n",
"Moon sling throws a 4.817 ton payload to the capture slings.\n",
"-----------------------------------------------------------\n",
"\n",
"Moon sling 1 length (km) : 66.770\n",
"Moon sling 2 length (km) : 16.693\n",
"Moon sling counterbalance ratio (specified) : 4.0000\n",
"\n",
"Moon sling 1 tip diameter (mm) : 3.6008\n",
"Moon sling 1 hub diameter (mm) : 3.7629\n",
"Moon sling 2 hub diameter (mm) : 3.7629\n",
"Moon sling 2 tip diameter (mm) : 3.7526\n",
"\n",
"Moon sling 1 taper ratio : 1.0450\n",
"Moon sling 2 taper ratio : 1.0028\n",
"\n",
"Center of mass for empty moon sling (m) : -1.5672e-12\n",
"Center of mass for full moon sling (m) : 0.00000\n",
"\n",
"Moon sling 1 tip speed (m/s) : 809.33\n",
"Moon sling 1 tip accel (g's) : 1.0000\n",
"Moon sling 2 tip speed (m/s) : 202.33\n",
"Moon sling 2 tip accel (g's) : 0.25000\n",
"Moon sling rotation period (minutes) : 8.6394\n",
"Moon sling full moment of inertia (kg*m^2) : 3.5790e13\n",
"Moon sling empty moment of inertia (kg*m^2) : 8.9482e12\n",
"\n",
"Moon sling tether 1 mass (tons) : 1.1252\n",
"Moon sling tether 2 mass (tons) : 0.28906\n",
"Moon sling grapple 1 mass (tons) : 1.2041\n",
"Moon sling grapple 2 mass (tons) : 1.5386\n",
"Moon sling ballast 2 mass (tons) : 5.3512\n",
"Moon sling solar panel mass via Juno (tons) : 0.19524\n",
"Moon sling radiator mass = solar panel mass (tons) : 0.19524\n",
"Moon sling motor mass = solar panel mass (tons) : 0.19524\n",
"Moon sling hub mass = solar panel mass (tons) : 0.19524\n",
"\n",
"Moon sling manufactured mass (tons) : 4.9380\n",
"Moon sling system mass, including ballast (tons) : 10.289\n",
"\n",
"-----------------------------------------------------------\n",
"Capture sling A (with C) throws a 1.455 ton payload to v_inf = 2640.0 m/s.\n",
"-----------------------------------------------------------\n",
"\n",
"Capture sling A1 length (km) : 99.186\n",
"Capture sling A2 length (km) : 99.949\n",
"Capture sling A counterbalance ratio (derived) : 0.99236\n",
"\n",
"Capture sling A1 tip diameter (mm) : 1.6436\n",
"Capture sling A1 hub diameter (mm) : 1.7548\n",
"Capture sling A2 hub diameter (mm) : 1.7548\n",
"Capture sling A2 tip diameter (mm) : 1.6420\n",
"\n",
"Capture sling A1 taper ratio : 1.0676\n",
"Capture sling A2 taper ratio : 1.0687\n",
"\n",
"Center of mass for empty capture sling A (m) : 6.0872e-12\n",
"Center of mass for full capture sling A (m) : 4.6330e-12\n",
"\n",
"Capture sling A1 tip speed (m/s) : 986.42\n",
"Capture sling A1 tip accel (g's) : 1.0000\n",
"Capture sling A2 tip speed (m/s) : 994.01\n",
"!!!WARNING!!! Capture sling A2 tip accel (g's) : 1.0077\n",
"Capture sling A rotation period (minutes) : 10.530\n",
"Capture sling A full moment of inertia (kg*m^2) : 2.7047e13\n",
"Capture sling A empty moment of inertia (kg*m^2) : 7.2960e12\n",
"\n",
"Capture sling A tether 1 mass (tons) : 0.35850\n",
"Capture sling A tether 1 gradient reinforcement (tons) : 0.010967\n",
"Capture sling A tether 2 mass (tons) : 0.36102\n",
"Capture sling A tether 2 gradient reinforcement (tons) : 0.011044\n",
"Capture sling A grapple 1 mass (tons) : 0.25089\n",
"Capture sling A ballast 1 mass (tons) : 0.0035661\n",
"Capture sling A grapple 2 mass (tons) : 0.25000\n",
"Capture sling A solar panel mass via Juno (tons) : 0.099326\n",
"Capture sling A radiator mass = solar panel mass (tons) : 0.099326\n",
"Capture sling A motor mass = solar panel mass (tons) : 0.099326\n",
"Capture sling A hub mass = solar panel mass (tons) : 0.099326\n",
"\n",
"Capture sling A manufactured mass (tons) : 1.6177\n",
"Capture sling A system mass, including ballast (tons) : 1.6213\n",
"\n",
"-----------------------------------------------------------\n",
"Capture sling B throws a 1.000 ton payload to v_inf = 2640.0 m/s.\n",
"-----------------------------------------------------------\n",
"\n",
"Capture sling B1 length (km) : 124.00\n",
"!!!WARNING!!! Capture sling B2 length (km) : 135.15\n",
"Capture sling B counterbalance ratio (derived) : 0.91753\n",
"\n",
"Capture sling B1 tip diameter (mm) : 1.6810\n",
"Capture sling B1 hub diameter (mm) : 1.8243\n",
"Capture sling B2 hub diameter (mm) : 1.8243\n",
"Capture sling B2 tip diameter (mm) : 1.6554\n",
"\n",
"Capture sling B1 taper ratio : 1.0852\n",
"Capture sling B2 taper ratio : 1.1020\n",
"\n",
"Center of mass for empty capture sling B (m) : -4.7794e-12\n",
"Center of mass for full capture sling B (m) : -8.5727e-12\n",
"\n",
"Capture sling B1 tip speed (m/s) : 1102.9\n",
"Capture sling B1 tip accel (g's) : 1.0000\n",
"!!!WARNING!!! Capture sling B2 tip speed (m/s) : 1202.1\n",
"!!!WARNING!!! Capture sling B2 tip accel (g's) : 1.0899\n",
"Capture sling B rotation period (minutes) : 11.774\n",
"Capture sling B full moment of inertia (kg*m^2) : 4.6842e13\n",
"Capture sling B empty moment of inertia (kg*m^2) : 1.4707e13\n",
"\n",
"Capture sling B tether 1 mass (tons) : 0.47936\n",
"Capture sling B tether 1 gradient reinforcement (tons) : 0.014665\n",
"Capture sling B tether 2 mass (tons) : 0.51737\n",
"Capture sling B tether 2 gradient reinforcement (tons) : 0.015828\n",
"Capture sling B grapple 1 mass (tons) : 0.26244\n",
"Capture sling B ballast 1 mass (tons) : 0.049747\n",
"Capture sling B grapple 2 mass (tons) : 0.25000\n",
"Capture sling B solar panel mass via Juno (tons) : 0.13759\n",
"Capture sling B radiator mass = solar panel mass (tons) : 0.13759\n",
"Capture sling B motor mass = solar panel mass (tons) : 0.13759\n",
"Capture sling B hub mass = solar panel mass (tons) : 0.13759\n",
"\n",
"Capture sling B manufactured mass (tons) : 2.0595\n",
"Capture sling B system mass, including ballast (tons) : 2.1093\n",
"\n",
"-----------------------------------------------------------\n",
"Capture sling C (on A) throws a 0.4551 ton payload to v_inf = 2640.0 m/s.\n",
"-----------------------------------------------------------\n",
"\n",
"Capture sling C1 length (km) : 99.186\n",
"Capture sling C2 length (km) : 99.949\n",
"Capture sling C counterbalance ratio (derived) : 0.99236\n",
"\n",
"Capture sling C1 tip diameter (mm) : 1.2177\n",
"Capture sling C1 hub diameter (mm) : 1.3001\n",
"Capture sling C2 hub diameter (mm) : 1.3001\n",
"Capture sling C2 tip diameter (mm) : 1.2165\n",
"\n",
"Capture sling C1 taper ratio : 1.0676\n",
"Capture sling C2 taper ratio : 1.0687\n",
"\n",
"Center of mass for empty capture sling C (m) : 4.3380e-12\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Center of mass for full capture sling C (m) : 8.4406e-12\n",
"\n",
"Capture sling C1 tip speed (m/s) : 986.42\n",
"Capture sling C1 tip accel (g's) : 1.0000\n",
"Capture sling C2 tip speed (m/s) : 994.01\n",
"!!!WARNING!!! Capture sling C2 tip accel (g's) : 1.0077\n",
"Capture sling C rotation period (minutes) : 10.530\n",
"Capture sling C full moment of inertia (kg*m^2) : 1.4846e13\n",
"Capture sling C empty moment of inertia (kg*m^2) : 5.8578e12\n",
"\n",
"Full capture sling rot. ang. mom. (kg*m^2/s) : 0.00027046\n",
"Empty capture sling rot. ang. mom. (kg*m^2/s) : 0.00020942\n",
"\n",
"Capture sling C tether 1 mass (tons) : 0.19678\n",
"Capture sling C tether 1 gradient reinforcement (tons) : 0.0060198\n",
"Capture sling C tether 2 mass (tons) : 0.19816\n",
"Capture sling C tether 2 gradient reinforcement (tons) : 0.0060622\n",
"Capture sling C grapple 1 mass (tons) : 0.13771\n",
"Capture sling C ballast 1 mass (tons) : 0.095775\n",
"Capture sling C grapple 2 mass (tons) : 0.13792\n",
"Capture sling C ballast 2 mass (tons) : 0.092402\n",
"Capture sling C solar panel mass via Juno (tons) : 0.054519\n",
"Capture sling C radiator mass = solar panel mass (tons) : 0.054519\n",
"Capture sling C motor mass = solar panel mass (tons) : 0.054519\n",
"Capture sling C hub mass = solar panel mass (tons) : 0.054519\n",
"\n",
"Capture sling C manufactured mass (tons) : 0.88865\n",
"Capture sling C system mass, including ballast (tons) : 1.0768\n",
"\n",
"-----------------------------------------------------------\n",
"Total tether mass (tons) : 3.5901\n",
"Total grapple mass (tons) : 4.0317\n",
"Total solar panel mass (tons) : 0.48668\n",
"Total radiator mass = solar panel mass (tons) : 0.48668\n",
"Total motor mass = solar panel mass (tons) : 0.48668\n",
"Total hub mass = solar panel mass (tons) : 0.48668\n",
"Total ballast mass (tons) : 5.5927\n",
"-----------------------------------------------------------\n",
"Total manufactured mass (tons) : 9.5685\n",
"Total manufactured mass / total payload mass : 3.2880\n",
"Total system mass, including ballast (tons) : 15.161\n",
"\n",
"Moon sling needs to throw 0.2415 tons of ballast to the capture sling.\n",
"This requires 1 throws, so the setup takes 18.94 days\n",
"\n",
"After setup, the moon sling throws to the capture sling every 18.94 days and the capture sling can throw every 18.94 days.\n",
"\n",
"-----------------------------------------------------------\n",
"\n",
"Rocket A sends a 1.910 ton payload through a delta-v of 1846. m/s. Its total mass (structure plus propellant, no payload) is 1.563 tons. Its structural mass is 0.2039 tons, which is 15.00% of its propellant mass of 1.359 tons. Its propellant mass fraction is 0.3914 and its payload fraction is 0.5499.\n",
"\n",
"Rocket B sends a 1.000 ton payload through a delta-v of 1846. m/s. Its total mass (structure plus propellant, no payload) is 0.8185 tons. Its structural mass is 0.1068 tons, which is 15.00% of its propellant mass of 0.7117 tons. Its propellant mass fraction is 0.3914 and its payload fraction is 0.5499.\n",
"\n",
"Total rocket mass is 2.382 tons, or 0.8185 times the combined payload mass of 2.910 tons.\n",
"\n",
"total manufactured mass / total rocket mass : 4.0173\n",
"-----------------------------------------------------------\n",
"\n",
"The moon sling cycle starts at the moment it throws a payload. It takes 2.5002 days to spin down and then 1.0000 days to attach a new payload. The moon sling then waits (retracted and shielded) for 5.4438 days. It takes 10.000 days to spin up, which is the end of the cycle. The moon sling cycle takes 18.944 days, which is 3.0000 resonance revisit times.\n",
"\n",
"The capture sling cycle starts at the moment the moon sling throws its payload to the capture sling. Each capture throw requires one moon throw. The capture sling takes 3.0000 days to rendezvous with and attach the payload. The capture sling then waits (retracted and shielded by the counterbalance masses) for 0.44843 days. It takes 10.000 days to spin up and throw at periapsis, then 3.1398 days to spin down. The capture sling then waits (retracted but largely unshielded) for 2.3558 days until the next revisit time after the moon sling finishes spinning up, which is the end of the cycle. The capture sling cycle takes 18.944 days, which is 3.0000 resonance revisit times.\n",
"\n",
"-----------------------------------------------------------\n",
"\n",
"Moon sling spinup time (days) : 10.000\n",
"Moon sling spindown time (days) : 2.5002\n",
"Moon sling average power (kW) : 3.0430 , Alt. calc: 3.0430\n",
"Moon sling average torque (N*m) : 502100.\n",
"Moon sling const accel spinup revolutions : 833.39\n",
"Moon sling const accel spindown revolutions : 208.36\n",
"\n",
"Capture sling A spinup time (days) : 10.000\n",
"Capture sling A spindown time (days) : 3.1398\n",
"Capture sling A average power (kW) : 1.5481 , Alt. calc: 1.5481\n",
"Capture sling A average torque (N*m) : 311330.\n",
"Capture sling A const accel spinup revolutions : 683.78\n",
"Capture sling A const accel spindown revolutions : 214.69\n",
"\n",
"Capture sling B spinup time (days) : 10.000\n",
"Capture sling B spindown time (days) : 3.1398\n",
"Capture sling B average power (kW) : 2.1446 , Alt. calc: 2.1446\n",
"Capture sling B average torque (N*m) : 482220.\n",
"Capture sling B const accel spinup revolutions : 611.54\n",
"Capture sling B const accel spindown revolutions : 192.01\n",
"\n",
"Capture sling C spinup time (days) : 10.000\n",
"Capture sling C spindown time (days) : 3.1398\n",
"Capture sling C average power (kW) : 0.84974 , Alt. calc: 0.84974\n",
"Capture sling C average torque (N*m) : 170890.\n",
"Capture sling C const accel spinup revolutions : 683.78\n",
"Capture sling C const accel spindown revolutions : 214.69\n",
"\n",
"Total required average sling power (kW) : 7.5854\n",
"-----------------------------------------------------------\n",
" \n",
"Moon sling throws payload mass (tons) : 4.82 , which is 20.0 % of the total.\n",
"Moon sling throws balance mass (tons) : 19.3 , which is 80.0 % of the total.\n",
"\n",
"1 moon payload becomes the capture sling payloads and counterbalances:\n",
"Capture sling A throws payload mass (tons) : 1.46 , which is 6.04 % of the total.\n",
"Capture sling A throws balance mass (tons) : 1.44 , which is 6.00 % of the total.\n",
"Capture sling B throws payload mass (tons) : 1.00 , which is 4.15 % of the total.\n",
"Capture sling B throws balance mass (tons) : 0.918 , which is 3.81 % of the total.\n",
"Capture sling C throws payload mass (tons) : 0.455 , which is 1.89 % of the total.\n",
"Capture sling C throws balance mass (tons) : 0.452 , which is 1.88 % of the total.\n",
"\n",
"Payloads and balances thrown from moon sling rotate once every 8.64 minutes.\n",
"Payloads and balances thrown from capture sling A rotate once every 10.5 minutes.\n",
"Payloads and balances thrown from capture sling B rotate once every 11.8 minutes.\n"
]
}
],
"source": [
"summary_digits = 5 #Many print statements in this notebook will print \"summary_digits\" significant figures.\n",
"if cbrs[1] < tolerance:\n",
" desc_width = 5 #This is used at the very bottom of this cell.\n",
" desc = 'Summary for a '+str((payload_masses[0][0]/1000.0).n(digits=3))+' ton payload thrown directly from '\n",
" lj = 50 #Left justify descriptions by this number of characters.\n",
"else:\n",
" desc_width = 15 #This is used at the very bottom of this cell.\n",
" desc = 'Summary for a '+str(((payload_masses[C_is_on][0]+payload_masses[3][0])/1000.0).n(digits=3))+' ton payload and a '+str((payload_masses[C_is_not_on][0]/1000.0).n(digits=3))+' ton payload thrown from '\n",
" lj = 57 #Left justify descriptions by this number of characters.\n",
"desc += moon_name+' around '+pname+' by a '+material+' sling with safety factor '+str(safety.n(digits=3))\n",
"if abs(best_case_offset) > tolerance: best_str = 'worst case '\n",
"else: best_str = 'best case '\n",
"if cbrs[1] < tolerance: desc +=' on a '+best_str+mnames[mis[0]]+' mission with v_inf = '+str(v_infs[mis[0]].n(digits=5)) +' m/s'\n",
"else: desc +=' on a '+mnames[mis[C_is_on]] +' mission with v_inf = '+str(v_infs[mis[C_is_on]].n(digits=5))+' m/s'\n",
"if cbrs[1] < tolerance or mis[1] == mis[2]: desc += '.\\n'\n",
"else: desc +=' and a '+mnames[mis[C_is_not_on]]+' mission with v_inf = '+str(v_infs[mis[C_is_not_on]].n(digits=5))+' m/s.\\n'\n",
"print desc\n",
"\n",
"if cbrs[1] > tolerance: print traj_string+'\\n'\n",
"\n",
"separator = '-' * (lj+2)\n",
"print '1 = payload arm of sling, 2 = counterbalance arm of sling. Center of mass > 0 if it\\'s on the payload arm.'\n",
"if cbrs[1] > tolerance: print 'A/B = capture orbit sling which releases its payload farthest/nearest to',pname\n",
"\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" if cbrs[j] > dmr: print '\\n!!! DANGER !!!',cap1st(descs[j]),'mass ratio',cbrs[j],'is higher than the safe limit of',dmr,'!!!'\n",
" if cbrs[j] < 1/dmr: print '\\n!!! DANGER !!!',cap1st(descs[j]),'mass ratio',cbrs[j], 'is lower than the safe limit of',1/dmr,'!!!'\n",
"\n",
"if cbrs[1] > tolerance:\n",
" print '\\nCapture orbit is in a',str(resn_s)+':'+str(resn_p),'resonance with',moon_name+'.'\n",
" print 'Capture orbit apoapsis (km)'.ljust(lj),':',(apoapsis_capture/1000.0).n(digits=summary_digits)\n",
" print 'Capture orbit periapsis (km)'.ljust(lj),':',(periapsis_capture/1000.0).n(digits=summary_digits)\n",
" print 'Capture orbit periapsis altitude (km)'.ljust(lj),':',((periapsis_capture-r_pl)/1000.0).n(digits=summary_digits)\n",
" desc = 'Capture orbit period ('+uname+')' ; print desc.ljust(lj),':',(period_capture/units).n(digits=summary_digits)\n",
" desc = 'Capture orbit revisit time ('+uname+')' ; print desc.ljust(lj),':',(period_capture*resn_s/units).n(digits=summary_digits)\n",
" print longest_arm_string #This was written in the animation cell.\n",
"\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" if j>0:\n",
" somegrad = Marswhip_gradient_factor #Only apply the gradient reinforcement to capture orbit slings.\n",
" somespace = '' #Only put a space after the moon sling description.\n",
" else:\n",
" somegrad = 1.0\n",
" somespace = ' ' #Only put a space after the moon sling description.\n",
" print '\\n',separator\n",
" if j==0 and cbrs[1] > tolerance:\n",
" print cap1st(descs[j]),'throws a',(payload_masses[j][0]/1000.0).n(digits=4),'ton payload to the capture slings.'\n",
" else:\n",
" if j==0: desc = ''\n",
" elif j==3: desc = ' (on '+sdescs[C_is_on]+')'\n",
" elif j==C_is_on: desc = ' (with '+sdescs[3]+')'\n",
" else: desc = ''\n",
" print cap1st(descs[j])+desc,'throws a',(payload_masses[j][0]/1000.0).n(digits=4),'ton payload to v_inf =',(v_infs[mis[j]]).n(digits=5),'m/s.'\n",
" print separator\n",
" wa = ''\n",
" if radii[j] > r_max+tolerance: wa = warnstring\n",
" desc = '\\n'+wa+cap1st(descs[j])+somespace+'1 length (km)' ; print desc.ljust(lj+1),':',(radii[j]/1000.0).n(digits=summary_digits)\n",
" wa = ''\n",
" if radii[j]/cbrs[j] > r_max+tolerance: wa = warnstring\n",
" desc = wa+cap1st(descs[j])+somespace+'2 length (km)' ; print desc.ljust(lj),':',(radii[j]/cbrs[j]/1000.0).n(digits=summary_digits)\n",
" if orig_cbrs[j] < -tolerance: cbr_desc = '(derived)'\n",
" else: cbr_desc = '(specified)'\n",
" desc = cap1st(descs[j])+' counterbalance ratio '+cbr_desc ; print desc.ljust(lj),':',cbrs[j].n(digits=summary_digits)\n",
"\n",
" desc = '\\n'+cap1st(descs[j])+somespace+'1 tip diameter (mm)' ; print desc.ljust(lj+1),':',(tip_diams[j][0]*1000).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+somespace+'1 hub diameter (mm)' ; print desc.ljust(lj),':',(hub_diams[j][0]*1000).n(digits=summary_digits)\n",
" if abs(hub_diams[j][0] - hub_diams[j][1]) > tolerance: print '!!!WARNING!!! Equivalent sling diameters aren\\'t equal at the',descs[j],'hub!'\n",
" desc = cap1st(descs[j])+somespace+'2 hub diameter (mm)' ; print desc.ljust(lj),':',(hub_diams[j][1]*1000).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+somespace+'2 tip diameter (mm)' ; print desc.ljust(lj),':',(tip_diams[j][1]*1000).n(digits=summary_digits)\n",
"\n",
" desc = '\\n'+cap1st(descs[j])+somespace+'1 taper ratio' ; print desc.ljust(lj+1),':',(hub_diams[j][0]/tip_diams[j][0]).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+somespace+'2 taper ratio' ; print desc.ljust(lj),':',(hub_diams[j][1]/tip_diams[j][1]).n(digits=summary_digits)\n",
" \n",
" wa = ''\n",
" if abs(coms_empty[j]) > tolerance: wa = warnstring\n",
" desc = '\\n'+wa+'Center of mass for empty '+descs[j]+' (m)' ; print desc.ljust(lj+1),':',(coms_empty[j]).n(digits=summary_digits)\n",
" wa = ''\n",
" if abs(coms_full[j]) > tolerance: wa = warnstring\n",
" desc = wa+'Center of mass for full '+descs[j]+' (m)' ; print desc.ljust(lj),':',(coms_full[j]).n(digits=summary_digits)\n",
"\n",
" wa = ''\n",
" if omegas[j]*radii[j] > v_tip_max+tolerance: wa = warnstring\n",
" desc = '\\n'+wa+cap1st(descs[j])+somespace+'1 tip speed (m/s)' ; print desc.ljust(lj+1),':',(omegas[j]*radii[j]).n(digits=summary_digits)\n",
" wa = ''\n",
" if omegas[j]^2*radii[j]/g > tip_accel_max/g+tolerance: wa = warnstring\n",
" desc = wa+cap1st(descs[j])+somespace+'1 tip accel (g\\'s)' ; print desc.ljust(lj),':',(omegas[j]^2*radii[j]/g).n(digits=summary_digits)\n",
" wa = ''\n",
" if omegas[j]*radii[j]/cbrs[j] > v_tip_max+tolerance: wa = warnstring\n",
" desc = wa+cap1st(descs[j])+somespace+'2 tip speed (m/s)' ; print desc.ljust(lj),':',(omegas[j]*radii[j]/cbrs[j]).n(digits=summary_digits)\n",
" wa = ''\n",
" if omegas[j]^2*radii[j]/cbrs[j]/g > tip_accel_max/g+tolerance: wa = warnstring\n",
" desc = wa+cap1st(descs[j])+somespace+'2 tip accel (g\\'s)' ; print desc.ljust(lj),':',(omegas[j]^2*radii[j]/cbrs[j]/g).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' rotation period (minutes)' ; print desc.ljust(lj),':',(2*pi/omegas[j]/60).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' full moment of inertia (kg*m^2)' ; print desc.ljust(lj),':',(moments_full[j][0]+moments_full[j][1]).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' empty moment of inertia (kg*m^2)' ; print desc.ljust(lj),':',(moments_empty[j][0]+moments_empty[j][1]).n(digits=summary_digits)\n",
" if j==3: #See if the moment ballast capture sling C does its job.\n",
" full_ang_mom = (sum(moments_full[3])+sum(moments_full[C_is_on]))*omegas[3] - sum(moments_full[C_is_not_on])*omegas[C_is_not_on]\n",
" wa = ''\n",
" if abs(full_ang_mom) > 0.1: wa = warnstring #\"tolerance\" is too small here.\n",
" desc = '\\n'+wa+'Full capture sling rot. ang. mom. (kg*m^2/s)' ; print desc.ljust(lj+1),':',(full_ang_mom).n(digits=summary_digits)\n",
" empty_ang_mom = (sum(moments_empty[3])+sum(moments_empty[C_is_on]))*omegas[3] - sum(moments_empty[C_is_not_on])*omegas[C_is_not_on]\n",
" wa = ''\n",
" if abs(empty_ang_mom) > 0.1: wa = warnstring #\"tolerance\" is too small here.\n",
" desc = wa+'Empty capture sling rot. ang. mom. (kg*m^2/s)' ; print desc.ljust(lj),':',(empty_ang_mom).n(digits=summary_digits)\n",
" desc = '\\n'+cap1st(descs[j])+' tether 1 mass (tons)' ; print desc.ljust(lj+1),':',(tether_masses[j][0]/1000.0).n(digits=summary_digits)\n",
" if j>0: desc = cap1st(descs[j])+' tether 1 gradient reinforcement (tons)' ; print desc.ljust(lj),':',(tether_masses[j][0]*(somegrad-1.0)/1000.0).n(digits=summary_digits)\n",
" if abs(v_cs[j][0]-v_c) > tolerance: desc = cap1st(descs[j])+' tether 1 safety factor multiplied by' ; print desc.ljust(lj),':',(v_c/v_cs[j][0])^2,', Alt. calc : ',1/cbrs[j]^2\n",
" desc = cap1st(descs[j])+' tether 2 mass (tons)' ; print desc.ljust(lj),':',(tether_masses[j][1]/1000.0).n(digits=summary_digits)\n",
" if j>0: desc = cap1st(descs[j])+' tether 2 gradient reinforcement (tons)' ; print desc.ljust(lj),':',(tether_masses[j][1]*(somegrad-1.0)/1000.0).n(digits=summary_digits)\n",
" if abs(v_cs[j][1]-v_c) > tolerance: desc = cap1st(descs[j])+' tether 2 safety factor multiplied by' ; print desc.ljust(lj),':',(v_c/v_cs[j][1])^2,', Alt. calc : ',1/cbrs[j]^2\n",
" desc = cap1st(descs[j])+' grapple 1 mass (tons)' ; print desc.ljust(lj),':',(grapple_masses[j][0]/1000.0).n(digits=summary_digits)\n",
" if ballast_masses[j][0] > tolerance: desc = cap1st(descs[j])+' ballast 1 mass (tons)' ; print desc.ljust(lj),':',(ballast_masses[j][0]/1000.0).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' grapple 2 mass (tons)' ; print desc.ljust(lj),':',(grapple_masses[j][1]/1000.0).n(digits=summary_digits)\n",
" if ballast_masses[j][1] > tolerance: desc = cap1st(descs[j])+' ballast 2 mass (tons)' ; print desc.ljust(lj),':',(ballast_masses[j][1]/1000.0).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' solar panel mass via Juno (tons)' ; print desc.ljust(lj),':',(junopanel_masses[j]/1000.0).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' radiator mass '+estimate_misc_str+'(tons)' ; print desc.ljust(lj),':',(radiator_masses[j]/1000.0).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' motor mass '+estimate_misc_str+'(tons)' ; print desc.ljust(lj),':',(motor_masses[j]/1000.0).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' hub mass '+estimate_misc_str+'(tons)' ; print desc.ljust(lj),':',(hub_masses[j]/1000.0).n(digits=summary_digits)\n",
" mfg_mass = sum(tether_masses[j]) + sum(grapple_masses[j]) + junopanel_masses[j] + radiator_masses[j] + motor_masses[j] + hub_masses[j]\n",
" desc = '\\n'+cap1st(descs[j])+' manufactured mass (tons)' ; print desc.ljust(lj+1),':',(mfg_mass/1000.0).n(digits=summary_digits)\n",
" if ballast_choice == 1: desc = cap1st(descs[j])+' system mass, including ballast (tons)' ; print desc.ljust(lj),':',((mfg_mass+sum(ballast_masses[j]))/1000.0).n(digits=summary_digits)\n",
"\n",
"total_tether_mass = sum(tether_masses[0])\n",
"total_grapple_mass = sum(grapple_masses[0])\n",
"total_panel_mass = junopanel_masses[0]\n",
"total_radiator_mass = radiator_masses[0]\n",
"total_motor_mass = motor_masses[0]\n",
"total_hub_mass = hub_masses[0]\n",
"total_ballast_mass = sum(ballast_masses[0])\n",
"total_payload_mass = payload_masses[0][0]\n",
"if cbrs[1] > tolerance:\n",
" total_tether_mass += (sum(tether_masses[1]) + sum(tether_masses[2]) + sum(tether_masses[3]))*Marswhip_gradient_factor\n",
" total_grapple_mass += sum([sum(grapple_masses[j]) for j in range(1,num_slings)])\n",
" total_panel_mass += sum(junopanel_masses[1:])\n",
" total_radiator_mass += sum(radiator_masses[1:])\n",
" total_motor_mass += sum(motor_masses[1:])\n",
" total_hub_mass += sum(hub_masses[1:])\n",
" total_ballast_mass += sum([sum(ballast_masses[j]) for j in range(1,num_slings)])\n",
" total_payload_mass = sum([x[0] for x in payload_masses[1:]]) #No \"+=\" because that would be double-counting.\n",
" total_capture_ballast_mass = sum([sum(ballast_masses[j]) for j in range(1,num_slings)])\n",
"print '\\n',separator\n",
"print 'Total tether mass (tons)'.ljust(lj),':',(total_tether_mass/1000.0).n(digits=summary_digits)\n",
"print 'Total grapple mass (tons)'.ljust(lj),':',(total_grapple_mass/1000.0).n(digits=summary_digits)\n",
"print 'Total solar panel mass (tons)'.ljust(lj),':',(total_panel_mass/1000.0).n(digits=summary_digits)\n",
"desc= 'Total radiator mass '+estimate_misc_str+'(tons)' ; print desc.ljust(lj),':',(total_radiator_mass/1000.0).n(digits=summary_digits)\n",
"desc= 'Total motor mass '+estimate_misc_str+'(tons)' ; print desc.ljust(lj),':',(total_motor_mass/1000.0).n(digits=summary_digits)\n",
"desc= 'Total hub mass '+estimate_misc_str+'(tons)' ; print desc.ljust(lj),':',(total_hub_mass/1000.0).n(digits=summary_digits)\n",
"if ballast_choice == 1: print 'Total ballast mass (tons)'.ljust(lj),':',(total_ballast_mass/1000.0).n(digits=summary_digits)\n",
"print separator\n",
"tot_mfg_mass = total_tether_mass + total_grapple_mass + total_panel_mass + total_radiator_mass + total_motor_mass + total_hub_mass\n",
"print 'Total manufactured mass (tons)'.ljust(lj),':',(tot_mfg_mass/1000.0).n(digits=summary_digits)\n",
"print 'Total manufactured mass / total payload mass'.ljust(lj),':',(tot_mfg_mass/total_payload_mass).n(digits=summary_digits)\n",
"if ballast_choice == 1:\n",
" print 'Total system mass, including ballast (tons)'.ljust(lj),':',((tot_mfg_mass + total_ballast_mass)/1000.0).n(digits=summary_digits)\n",
" if cbrs[1] > tolerance:\n",
" print '\\n',cap1st(descs[0]),'needs to throw',(total_capture_ballast_mass/1000.0).n(digits=4),'tons of ballast to the capture sling.'\n",
" ballast_throws = ((total_capture_ballast_mass/payload_masses[0][0]).n(digits=summary_digits)).ceil()\n",
" print 'This requires',ballast_throws,'throws, so the setup takes',(ms_cycle_time*ballast_throws/units).n(digits=4),uname\n",
" print '\\nAfter setup, the moon sling throws to the capture sling every',(ms_cycle_time/units).n(digits=4),uname,'and the capture sling can throw every',(cs_cycle_time/units).n(digits=4),uname+'.'\n",
"\n",
"print '\\n',separator\n",
"\n",
"print rocket_desc\n",
"\n",
"#From Jokic and Longuski 2004 table 3, for single stage rockets:\n",
"if cbrs[1] < tolerance: print 'JL04 mu_single = moon tether1/propellant'.ljust(lj),':',(tether_masses[0][0]/sum(rocket_propellants)).n(digits=summary_digits)\n",
"\n",
"print 'total manufactured mass / total rocket mass'.ljust(lj),':',(tot_mfg_mass/(sum(rocket_structures)+sum(rocket_propellants))).n(digits=summary_digits)\n",
"\n",
"print separator\n",
"\n",
"print '\\n'+ms_cycle_description\n",
"\n",
"if cbrs[1] > tolerance: print '\\n'+cs_cycle_description\n",
"\n",
"print '\\n',separator\n",
"\n",
"for j in range(num_slings):\n",
" if cbrs[j] > tolerance:\n",
" desc = '\\n'+cap1st(descs[j])+' spinup time ('+uname+')' ; print desc.ljust(lj+1),':',(spinup_times[j]/units).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' spindown time ('+uname+')' ; print desc.ljust(lj),':',(spindown_times[j]/units).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' average power (kW)' ; print desc.ljust(lj),':',(avg_powers[j]/1000).n(digits=summary_digits),', Alt. calc:',(avg_powers_alt[j]/1000).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' average torque (N*m)' ; print desc.ljust(lj),':',(avg_torques[j]).n(digits=summary_digits)\n",
" desc = cap1st(descs[j])+' const accel spinup revolutions' ; print desc.ljust(lj),':',(0.5*(omegas[j]/spinup_times[j])*spinup_times[j]^2/(2*pi)).n(digits=summary_digits) #theta = 0.5*a*t^2, where a = angular acceleration. Revolutions = theta/(2*pi)\n",
" desc = cap1st(descs[j])+' const accel spindown revolutions' ; print desc.ljust(lj),':',((omegas[j]*spindown_times[j]-0.5*(omegas[j]/spindown_times[j])*spindown_times[j]^2)/(2*pi)).n(digits=summary_digits) #theta = omega0*t - 0.5*a*t^2, where a = angular acceleration. Revolutions = theta/(2*pi)\n",
"\n",
"if cbrs[1] > tolerance: print '\\nTotal required average sling power (kW)'.ljust(lj+1),':',(sum(avg_powers)/1000).n(digits=summary_digits)\n",
"print separator\n",
"\n",
"total_material = sum(payload_masses[0])*mt4ct #Don't include capture sling masses- that'd be double-counting.\n",
"print ' '\n",
"if abs(mt4ct-1) > tolerance:\n",
" print 'Moon sling throws',mt4ct.ceil(),'times for each capture sling throw. Each full-sized moon throw consists of:'\n",
" print 'Moon sling throws payload mass (tons)'.ljust(lj),':',(payload_masses[0][0]/1000.0).n(digits=3),', which is',(100*payload_masses[0][0]/total_material).n(digits=3),'% of the total.'\n",
" print 'Moon sling throws balance mass (tons)'.ljust(lj),':',(payload_masses[0][1]/1000.0).n(digits=3),', which is',(100*payload_masses[0][1]/total_material).n(digits=3),'% of the total.'\n",
" print '\\nAfter',mt4ct.ceil(),'throws, the moon sling has thrown a total of:'\n",
"print 'Moon sling throws payload mass (tons)'.ljust(lj),':',(payload_masses[0][0]*mt4ct/1000.0).n(digits=3),', which is',(100*payload_masses[0][0]*mt4ct/total_material).n(digits=3),'% of the total.'\n",
"print 'Moon sling throws balance mass (tons)'.ljust(lj),':',(payload_masses[0][1]*mt4ct/1000.0).n(digits=3),', which is',(100*payload_masses[0][1]*mt4ct/total_material).n(digits=3),'% of the total.'\n",
"\n",
"if cbrs[1] > tolerance:\n",
" if abs(mt4ct-1) < tolerance: print '\\n1 moon payload becomes the capture sling payloads and counterbalances:'\n",
" else: print '\\n',mt4ct.n(digits=5),'moon payloads become the capture sling payloads and counterbalances:'\n",
" for j in range(1,num_slings):\n",
" if cbrs[j] > tolerance:\n",
" desc = cap1st(descs[j])+' throws payload mass (tons)' ; print desc.ljust(lj),':',str((payload_masses[j][0]/1000.0).n(digits=3)).rjust(4),', which is',(100*payload_masses[j][0]/total_material).n(digits=3),'% of the total.'\n",
" desc = cap1st(descs[j])+' throws balance mass (tons)' ; print desc.ljust(lj),':',str((payload_masses[j][1]/1000.0).n(digits=3)).rjust(4),', which is',(100*payload_masses[j][1]/total_material).n(digits=3),'% of the total.'\n",
"\n",
"print ''\n",
"for j in range(num_slings-1): #Skip sling C because it's redundant.\n",
" if cbrs[j] > tolerance: desc = 'Payloads and balances thrown from '+descs[j].ljust(desc_width)+' rotate once every '+str((2*pi/omegas[j]/60).n(digits=3))+' minutes.' ; print desc"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where Deimos stays in the x-y plane and the capture slings are inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"cosine of capture orbit's true anomaly at Phobos = -0.363741529069014\n",
"cosine of capture orbit's true anomaly at Deimos = -0.993853976119485\n"
]
},
{
"data": {
"image/png": 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91npWL7jAozpRKBBYAQDA0QZImixplKSO0r6F0v5u0qpa0r8ypS5dpA8+sFYAINAIrAAA4GijJN0i7e4sjRghDR0qXZwmTcyW1syUqrTyukAUJgRWAABwFLdD+mSt1DVeSk+XevSQHm0vqZ1UpbTX1aGwYZUAAABw0K+/So8+Ks13UpkPpW4dpTVrpNdekyp9JKm0pDpeV4nChhFWAACg7dulYcOkl1+2par+3kG6832p3lxZSF0iabakZyWV8rJSFEaMsAIAUIht3iz17SvFx0svvij16SOtXy/d9bYUsVDS+ZKmSMqSNFHSPV5Wi8KKEVYAAAqhjRttaarXXpOKF5fuuUf697+lMmUO+ab6kt7xqkLgLwRWAAAKkQ0bpMGDpTFjpJIlpQcflO68Uzr9dK8rA46PwAoAQCGwZo305JPSG29YOH38cen22yU2ZEMoILACABDGVq2SBg2SUlKks86yNoBbb7XRVSBUEFgBID+ysmw69ebN0i+/2Pu0NJtWnZmpYnv26BFJxZ55RipVSoqKkmJipAoVpNhYe1+2rBQZ6fU9QZhbvlwaOFCaNEmqWFF64QWpVy+pRAmvKwPyL8I557wuAgB8xTlp3TopNdXeVq36K6Bu3WqhNUdEhA1VFS0qFS2q7MhIbd68WRXKllWRzEzpwAFpzx47Zo7ISKlcub8CbI0aUv36UoMGUpUqdkzgJKWmSgMGSO+/bzP/H3jAFv0vXtzryoCTR2AFgPXrpcWL/wqoS5ZIv/1mX6tcWapVy4aocgLmoaOl5cpZWP1Tenq6YmJilJaWpuic5sADByzoHjoqm/PvTZukFStsyrYknXGGVK+eBdj69aWGDS3EArn44gsLqjNnStWq2WSqbt0OuzyBkEVgBVD4ZGbas/u0adLUqdL339vnK1f+KyjmvJ11Vr4OfczAmhfbtv0VmHPefv7Zvla9upSUZG9NmtBOgMPMm2dB9dNPbbD+4Yelzp2tGwUIFwRWAIXDrl3SrFkWUGfMkHbutNHRtm3t7R//yHc4PZaTDqzHsm2b9Pnn0vTpFq63bbNFMtu0sfDaooVUmk3dCyPnLKAOGCDNny+df77Uv7/UoYNUhC2BEIYIrADCV86z+vDhFlL377eX93NGKxs2LPBn9wINrIfKzpa++uqvUeFvvpGKFbOwffvt0uWX0/taCDhnL/kPGCAtWmQvAvTvL7VrR1BFeCOwAgg/v/8uvf66NGKE9MMPUs2aNj26fXupatWA3nTAAuuR1q2zWTUjR0rffmttA3362OwaVoAPO9nZ9nfKwIHWLdKkiQXVVq34OwWFA4EVQPhYssRC6vjxNtHp2mtt9LFZs6A9qwctsOZwTvrsMxtFfvddm2HTrZvd77p1A3/7CKisLDutAwfa3LxLLrGgyoA6ChteQAAQ+hYskC6+2F4f/fBDmx7988/ShAn2+XB+Zo+IsPs4YYLd5wcftMegXj37/IIFXleIk5CZKb35pnWwdOkilS9vvapz50rNm4f3JQ0cC4EVQOhascKa95o1k3bvliZPtiWqHn7YnuELm/Ll7b6vX2+Pxe7d9ti0a2ePFXzvwAFp9Gib7X/DDVJCgi1oMWuWnUqgsCKwAgg9GzZI3btLderYov4pKdLXX0vXXOObtXySk5OVlJSklJSU4N94VJQ9Fl9/bY/NqlX2WHXvbo8dfCcjQ/q//7P1U3v1kmrXtl7V6dOlCy/0ujrAe/SwAggd27dbM9+IEba80yOPSL17+2pl9KD3sObF/v3SqFHS44/bhgh9+kgPPWRbxMJTe/favLkhQ2wfiU6dbJC8dm2vKwP8hcAKwP+csx7Nf/7Tmvvuv1+66y7bEtVnfBlYc+zZYxvKDxliIf+ll6xBkobIoNuzx0ZUhw615XWvu87aj2vU8LoywJ9oCQDgb1u32mz/666TrrhCWrPGntl9GFZ9r2RJe+zWrLGZO127Sh072mOMoEhPl556SoqPl/7zH+mqq2yjtTfeIKwCJ0JgBeBPzln/5Xnn2Uz3iROlt9/mZeyCULasPZYTJ9qSWDVr2gg2L7gFzG+/WUdGfLz02GP2d8Lq1dapcc45XlcH+B+BFYD/HDmqunKlPcOjYHXsaI8to60Bs2OH9aTGx0uDB0vXXy+tXWtt2PHxXlcHhA56WAH4y6xZFlSLFLHF8EMsqPq6h/VEJk2yzQays21k+8orva4opG3dKj37rF3Cztk8t379Cudqa0BBYIQVgD84Jz3/vNS6tdSwIaOqwZYz2tqwoe33+cILtAichE2bpH//W6pSxSZV3XmnrSQ2dChhFTgVBFYA3svIsMUn775b6tvXFp+kVzX4ypa1x/6eeyx19e5t5wa5+vFHG6CuWlUaO1a67z4Lqk8+yaUMFAR/rLANoPDaskXq0EFaskQaN86294F3IiOlZ56xhUBvuUX67jvbzJ7hwWNau9Zm/Y8dK8XESI8+Kt1xh/0bQMFhhBWAd1JT7SXoDRtso3TCqn907y7Nm2fbvDZsaH9Q4KDvv7eHqHp1G5R+6im7jB98kLAKBAKBFYA3ZsywzdErVJAWL5YaNfK6IhypcWM7N+XLS02b2jkr5L75RkpOtjVTP/1UGjbMMn2/flKpUl5XB4QvAiuA4Js82fa6b9nSRvEqVvS6IhxPxYo2+t2ypZ2zKVO8rsgTS5faSmu1a0tffCG9/LK1A9x5p1SihNfVAeGPwAoguFJSpM6d7dn/nXd4tg8FJUrYuerQwTa7nzDB64qC5quvpHbtpHr1pGXLpJEjbcH/Pn2k007zujqg8GDSFYDgSUmxldNvuMG2+ImM9Loi5FXRotL48VLx4lK3bva55GRvawqgBQukAQNsWeDq1W0+YNeuUhTPmoAn+K8HIDgmT7agesMN0ujRtjEAQktkpDRmjP37+ustvF5zjbc1FSDnpDlzLKjOnSvVqmWDyR078rcV4DWeMQAE3vTpNhrXqZONrBJWQ1eRInYOO3aUunQJi4lYzkkffmjzypo3l9LS7O+r//3P7iJhFfAezxoAAis11XpW27Sx11V59g99UVHSG2/YOe3UKWSXvHJOmjrVFkNo3VrKyrK/rVJTbeCYv6sA/+C/I4DA2bJFuvpqe231rbesDxLhoWhRO6c1a0rt20tbt3pdUZ5lZ0uTJkl161rpp51mvapffGEZPCLC6woBHInACiAwMjJsVnlWli2FxGoA4adECem996TMTDvXPt/GNSvL5v2df74NDJcpY72q8+dLV15JUAX8jMAKoOA5J912m71UPGUK66yGs4oVLbSmptpaT855XdFRMjNt69TzzpOuu06qXFlauNAW/r/kEq+rA5AXBFYABe+FF6TXX5dee80aBBHeGjeWXn3VVhD473+9ruag/fvtEkxMlG68UTr3XFtXdeZM6R//8Lo6APnBslYACtasWVLfvtK999oSVigcuneXVqyQ7rnHhjKvvNKzUvbts5XTBg+WNm60PSqmTJHq1PGsJACnKMI5H75+AyA0bd1qk3AaNZKmTSuUKwKkp6crJiZGaWlpio6O9rqc4MrKktq2lRYvllaulMqVC+rN//GHDfQ+/bRdil26SA89ZJckgNBGSwCAguGc9TAWKWINg4UwrB4qOTlZSUlJSklJ8bqU4ImMtKXLihSRbr89aP2su3ZZSK1SRerXT2rRQlq16q9FDACEPkZYARSMlBSb0TJpkr0GW0gV6hHWHJMm2TT8lJSAbt+alia9+KL03HMWWm+8UfrPf6SqVQN2kwA8QmAFcOq2brW+xSuukN5+2+tqPEVg/VPnztLs2QFpDfj1V+n5521+1759Uu/e0n33SWefXaA3A8BHaAkAcGpyWgEiI6WXXvK6GvjFyy8XeGvAtm02ghoXJw0dKt10k7RunV12hFUgvBFYAZyat9+2KdgjRkhly3pdDfyibFlp+HBp8mTpnXdO6VCbN9viA/HxloPvuEPasEEaNkyKjS2QagH4HC0BAE7e9u1SjRpS8+aFvhUgBy0BR8hpDVi1Kt9/0Pz8szRkiDRypG2feued0l132Q5VAAoXRlgBnLyBA6UDB2gFwPG9/LJdI4MG5flH1q+Xbr1VSkiweVsPPWQjqk88QVgFCisCK4CTs2GDtQHcfz+tADi+smVtRtSIEXbNnMDq1VLPnlK1atZlMmCA/Uj//tLppwelWgA+RUsAgJPTvbv08cfSmjVSyZJeV+MbtAQcw549NlzasqWt0XuEb7+1AdgJE2xBgXvvlW65hcsKwF8YYQWQf8uXS2++KT3yCKkCuStZ0q6VN96w7Vv/9L//2XKttWpJn31my1StWyfdfTeXFYDDMcIKIP/atbNJNKtWSUWLel2NrzDCehwHDtgEvfPO09ePTNWAAdLUqbY71QMPSD16SMWKeV0kAL+K8roAACFmwQJp+nSbDUNYRV4VLaofbhigxMeu07+nLdC2ak31+uu2ORqXEYDcMMIKIH8uvljavVv6+mtbGB6HYYT1aPPm2Qz/ObOztfK0+ioTH60y38xTZKTXlQEIFTzbAMi7JUus2bB/f8IqTsg5m5N38cXSpZdKO3dK70wsoupv9NdZ381X5PKlXpcIIITwjAMg70aMkCpVsh5W4Bick2bMkJo0kVq0kPbuld5/X1q6VOrYUSpydZJUsaJdSwCQRwRWAHnz++/S+PG2onsU7e84XHa2rZ3aoIHUtq0UGSnNnCl99ZWUlCRFRPz5jVFRdg2NH2/XFADkAYEVQN6MHWszvXv39roSeGD9emnUKGniROmPP/76fFaW7cpbp47UoYMUHS19+qnNzWvV6pCgeqjevaX9+6Vx44JWP4DQxqQrALlzTjr3XKluXVvdHccVbpOunLPNzIYOtX9L0hlnSO+8I23ZYgv+f/edvfzfv7/UtGkeD9yliy3EumrVcVItAPyFEVYAufv0U+mHH6Tbb/e6EgTZxInSM89YMN2928Jp+fK2adUNN9gGVosWSR99lI+wKtm19P330uzZAasdQPhghBVA7jp0sMC6YgWjYbkItxHWq66ynVVnzZLGjJEGD5Z+/NG+NmCA9PDDJ3lg52yLq3PPld59t8DqBRCeGGEFcGK7dtm07169CKuF0M6dFlgTEmxQ9MILpdRUW+w/JuYUDhwRYdfU9Ok2dAsAJ0BgBXBis2bZBJn27b2uBEG0e7f1ra5caQG1cWPp22+thfnLL23+3eWXn+KNtG9v19asWQVSM4DwRWAFcGJTp9pLt1Wrel0JgiA9XXrySSk+XnrgAcuUlSpZq+nTT0tt2thIa69eUs2ap3hjCQl2kKlTC6J0AGGMwArg+DIzrR0gKcnrSkJOcnKykpKSlJKS4nUpefLbb9Ljj0txcfa+c2dpzRpbLnXxYluJaulS6xB57TXp1VcL6IaTkqwtICurgA4IIBwx6QrA8X32me2tuWiRvSaMXIXapKsdO6TnnpNefNFe5r/lFum++2wzqqBYtMi2xfrss3wuMwCgMGG7GgDHN22aVK6c1LCh15WggG3ZIj377F87pPbpI/Xta0tWBVWjRtJZZ9m1RmAFcBy0BAA4vqlTbZ/NIvyqCBebNkl33SVVqSK98or9e8MGW2s16GFVsmurbVv6WAGcEM9CAI5t/Xpb2L1tW68rQQH48UcbRa1a1XZE/c9/7HODBklnnulxce3a2Y4EGzZ4XAgAv6IlAMCxLV5s7//xD2/rwClZs0Z66ikLqaefLj32mHTHHZKv2mubNLH3ixfb8gQAcARGWAEcW2qqVLmy9Rci5Hz3nW2dWr26LfQweLANYD7wgM/CqmR90pUq2TUHAMfACCuAY0tNlerX97oK5NOKFdLAgdLEiVJsrPT887YkVYkSXleWi/r1CawAjosRVgBHc47AGmKWLJE6dJDOP992oho+XFq7VvrXv0IgrEp/BVZWWgRwDARWAEdbt076/XcCawj48kubF1e/vrR8uTRqlLR6tXTbbVLx4l5Xlw/169vuBevXe10JAB8isAI4Ws5LswRW3/rsM6lFC+nCC20k9Y03rG/1ppukokW9ru4k5FxrtAUAOAYCK4CjMeHKl5yTPv1UuvRS24Bsyxbp7belb76Rrr9eigrlWQlMvAJwAgRWAEdbtUqqVcvrKvD+1LHZAAAgAElEQVQn56SZM6WLLpKuuELatUuaMkVatkzq3FmKjPS6wgJSu7ZdewBwBAIrgKNt3hzEzeRxPM7ZBlCNGklXXWUfz5ghff21dPXVYbgBWWysXXsAcIRw+3UHoCD88ouFB3giO1uaNEmqW1dq395m+X/8sfT55xZcIyK8rjBAYmPt2gOAIxBYARwuK0vaulWqUMHrSgqdrCzprbfslfFOnWzL1LlzpfnzrRUgbINqjgoVrDE3O9vrSgD4DIEVwOG2b7fkRGANmgMHpNdfl2rUkLp1k+LibDT1k0+kSy7xurogqlDBrr3t272uBIDPEFgBHC6nh5CWgIDbv1969VXbPrVnT+m886TFi6UPPpCaNPG6Og/kXHP0sQI4AoE1hKSkpHhdAgqYL89pTg8hI6wBs2+f9PLLUkKCLfDfoIHN+H/vPft3oZVzzfm0j9WX/19xSjinoYPAGkL4jxV+fHlON2+2Zsly5byuJOz88Yf03HNS1arSnXfaWqrffCO9845Up47X1flA+fL23qcjrL78/4pTwjkNHYFbZvoPSd8F7OiFU5qkJV4XgQLlx3O6Mk0qUVJaEYrbJfnA7j/fL5NUyv65a480fKL07JvSr+nSDVdJD94kVTtb0j757xrwTFHpb6XsGvTjY+LH/684NZzTgneupL8F4LjuJLz11lu5f1Oqc075f3tLb53Uz/nh+IGuvZ3ahWztofy4B/L4gTynJ1/7YOd0Rlg/7oE8dprSnCSXpjT3m5x7Qs79Xc4VlXO3yLl1Pq/f+/N6unMa4sva+R0cfrX783ew98c+peOnulzlKUce4aRGWFNSUtS1a9cTf9O5kk5ih72Uu1PU9blcjn0KAnn8QNeuuyU9F5hD87h7dPwAnlPpJGsflSlNKCp9HIBj50PIXjO7JV0iDbxBemWylHFAuvka6b7uUuXyBXMTIfvY5OX4VxSVrsuUbgrAsU8Vv4ODfuyAH9+Pv4N9cOxTOv65eTh2XnLkEfIUWJ1z2rVr18GPMzMzlZ6envsPnpOvWuzYJTOVfk4ejn2SAnl8avfm+NResMcvFrNHxYpHancuP+fH2r0+9vbt0tDX7LgvTkxXr17Wq5rTmllQtxiKj01ej1+qeKT2R+/W/pOoweva/XrsQB+f2r05vm9rz1Suv+yOzJGlS5dWRC4LTUc451xut52enq6YmJg81QkgtD0i6RZJlbwuBIXSJkmvSHrC60IABE1aWpqio6NP+D15CqxHjrACCF/FnnlGxV55RbvXrPG6FN/7+Wfp+eelceOk006T+vSRunVL1/nnV9bPP/+c6y9gHK1UQoL29+mj/f36eV0KgCDJywhrnloCIiIi+MULFBalSkmZmfyfP4F166TBg213qtKlpf79pX/9S4qJkXJe5YqOjuYxPBmZmTqtVCmdxmMH4BCBW9YKQGiKirK9QnGUH36QnnxSevNNqUwZaeBAG1UtXdrrysJIZqZdgwBwCH4rADhcTIy0Z4+F1qKsxSpJK1dKgwZJb79t+ykMHSrdcov0t0CsNViYHTgg7d5t1yAAHIKdrgAcrkIFyTlp61avK/HcsmVSx45SrVrSggXSiy9aO8C//01YDYgtW+w92wIDOAKBFcDhYmPtvU+3xwyGxYulpCSpbl1p6VLptdekNWuk22+3yVUIkJxrLucaBIA/EVh9YPLkyWrZsqXOPPNMRUREaNmyZfk+xo033qiIiIij3mrWrBmAipEXxzonF154Yb6Pk5GRoYceekhxcXEqXry4EhISNHr06ABU/Kec0a1ffgncbfjUwoVSq1ZSo0bS999LY8fa+969pWLFvK6uEMi55oIwwvrYY48d9f+zfPn87ezgnNPQoUOVmJio4sWLq3LlynryyScDVDHyav78+WrXrp1iY2MVERGh9957L18/f6xrIyIiQiVLlgxQxcgLelh9YM+ePbrooovUqVMn3XzzzSd1jBdeeEGDBw8++HFmZqbq1KmjTp06FVSZOAmtWrXSmDFjDn5c7CRST+fOnbV161aNGjVK55xzjrZt26bMzMyCLPNwZctKkZGFZoTVOWnePOmJJ6Q5c6SaNaWUFKlTJ3sYEESbN9uDXrZsUG6uZs2a+uSTTw5+HJnPE37XXXdp1qxZGjp0qGrXrq20tDTt2LGjoMtEPu3Zs0d16tRRz549de211+b75/v166fbbrvtsM81b95cDRs2LKgScTLyvZkrAmb9+vVOklu6dOlRX/v999/dzTff7MqWLetKly7tLrvsMrds2bLjHmvKlCkuIiLCbdiwIZAl4wR69Ojh2rdvf8Lvye28zpw508XExLidO3cGutzDxcY698gjwb3NIMvOdu6jj5xr2tQ5ybkLLnBu0iTnsrJO7bhpaWlOkktLSyuYQguT/v2dq1gxKDf16KOPujp16hz36xkZGe7ee+91sbGx7m9/+5tr1KiRmzNnzsGvf/vtty4qKsp99913QagWJ0uSmzJlymGfy+3cHmnZsmVOkps/f36Aq8WJ0BIQApxzatOmjbZs2aIPPvhAqampqlevnpo3b65ff/31mD8zatQoXXHFFYqLiwtytTjU3LlzddZZZykxMVE333yztm3bdvBreTmvU6dOVYMGDfT000+rYsWKSkxMVL9+/bR3797AFh4bG7YtAc5J06dLTZpILVtKGRnS1KnSkiXStddKRfit6J1ffglq/+rq1asVGxurKlWqKDk5WevWrTv4tZ49e2rhwoWaMGGCli9frk6dOqlVq1ZavXq1JGnatGmqWrWqpk+fripVqig+Pl69e/c+7u9k+Edu5/ZII0eOVGJiopo1axbkSnEYrxMz/nK8EdZPP/3URUdHu3379h32+YSEBPfKK68cdZxffvnFRUZGurfffjug9eLEJkyY4KZPn+5WrFjhpk6d6urUqeNq1qx58Dzm5by2bNnSFS9e3LVp08Z9+eWXbsaMGS4uLs717NkzsMW3a+dc69aBvY0gy8py7t13natb10ZUL7rIuQ8/tJHWgsQI6ylo3dq5pKSg3NQHH3zgJk2a5JYvX+4+/vhjd8kll7hy5cq5HTt2uDVr1riIiAi3adOmw36mefPm7oEHHnDOOXfrrbe64sWLu8aNG7v58+e7OXPmuAsuuMBddtllQakfeaMjRljzcm4PtW/fPnfGGWe4IUOGBLxWnBg9rEE2fvx43XrrrQc/njlzZq5/taWmpmr37t0qU6bMYZ/fu3ev1q5de9T3v/766zr99NN19dVXF0zRyNWxzmuXLl0OflyrVi01aNBAcXFxmjFjhjp06JCn85qdna2IiAiNHz9eMX+uTTls2DB17NhRL7/8skqUKBGYO1SjhjVyhoGsLGnSJFvk/5tvpEsvlWbPtve57ASIYFuxQurWLSg31bp164P/rl27tpo0aaKEhASNHTtWlStXlnNOiYmJh/1MRkbGwf+v2dnZysjI0Lhx4w5+36hRo1S/fn19//33ql69elDuB/JnyZIluZ7bQ02ePFm7du1S9+7dg1UijoPAGmRJSUlq3LjxwY8rVqyY689kZ2erQoUKmjt37lFfO/300w/72Dmn0aNH64YbbjipCT44OXk5rxUqVFBcXNzBl53ycl4rVKigihUrHgyrklSjRg0557Rx40ZVq1atgO/Jn+rXl55+Wtq2TTrrrMDcRoBlZlrmHjTIZvq3aCGNGCE1bep1ZTimrVuljRvt2vNAyZIlVbt2ba1evVoVK1ZUZGSkUlNTj5qIVapUKUn2fzMqKuqw4FOjRg1J0k8//URg9ans7Oxcz+2hRo4cqbZt2+Z7BQkUPAJrkJUuXVql87mPY7169bRlyxZFRUUpPj7+hN87b948rVmzRr169TqFKpFfeTmvO3fu1M8//6wKfy7Zk5fzetFFF2nixInavXv3wV+mP/zwg4oUKaJKlSoV6H04TE5oSE2VDhmJCgX790tvvCE99ZS0dq3Utq00bpwtVQUfS0219x4F1oyMDK1atUrNmjVT3bp1lZWVpW3bth33FbCLLrpImZmZWrt2rRISEiTZ/01JzB3wsbyc2xzr16/XnDlzNHXq1CBVhxPytiMBzjm3c+dOt3TpUjdjxgwnyU2YMMEtXbrUbd682TnnXHZ2tmvatKmrU6eO+/DDD9369evdwoUL3UMPPeQWL1582LGuv/5617hxYy/uBg6xa9cu17dvX/f555+79evXuzlz5rgmTZq4ihUruvT0dOdc3s7rrl27XKVKlVzHjh3dypUr3bx581y1atVc7969A3sHsrOdO/105wYMCOztFKB9+5wbPty5s8+2HtUOHZxbsiT4ddDDepKeeMK5M84o+Kbi4+jbt6+bO3euW7dunVu0aJFr27atK1269MGVVbp16+bi4+Pdu+++69atW+e++uorN3jwYDdjxgznnHNZWVmuXr167uKLL3ZLlixxX3/9tWvcuLG78sorg1I/jm/Xrl1u6dKlbunSpU6SGzZsmFu6dKn78ccfnXO5n9scDz/8sIuNjXWZmZle3A0cgcDqA2PGjHGSjnp79NFHD35Penq6+9e//uViY2Nd0aJFXeXKlV23bt3cTz/9dPB7fv/9d1eiRAn36quvenAvcKg//vjDtWjRwpUtW9YVLVrUnX322a5Hjx6HnS/n8nZeV61a5a644gpXokQJV6lSJXfPPfe4P/74I/B3onlz566+OvC3c4r27HHu+edtJa6ICOeSk51bscK7egisJ6l9e+euuCJoN9elSxdXoUIFV7RoURcbG+s6dOjgVq5cefDr+/fvd4888oiLj493RYsWdeXLl3fXXHONW758+cHv2bRpk+vQoYMrVaqUK1eunLvxxhuDvwQdjjJnzpxjPqf26NHDOZe3c5uVleUqVarkHnzwQY/uBY4U4ZxzXozsAvC5+++3JtCffvK6kmPavdt6UocOlXbutLk6Dz4oed06mJ6erpiYGKWlpSk6OtrbYkJJ5cp2Eg/ZAAUAcrDiIIBjq19f+vlnm3jlI2lpNpEqPt4CalLSX9uoeh1WcZI8nnAFwP8IrACOLWcbws8/97aOP/32m/TYYxZUn3hC6tLFJlW99pr055wXhKovvrD3bH0J4DgIrACOrUoVG7KcPt3TMrZvt5HUuDhpyBCpRw9p3Trp5Zels8/2tDQUlGnTpHPPtb9GAOAYWNYKwPElJdmaUNnZQd+zdMsW608dMcIW+L/9dqlvX6lcuaCWgUDLzrY/im680etKAPgYI6wAjq9dO+svXLw4aDe5caN05502wPvaa9Ldd0sbNtg+BoTVMPTVV9Yn3a6d15UA8DECK4Dja9JEKlNGCsLC2Rs2SLfdZv2ob74pPfCA9OOPtqXqmWcG/ObhlalT7Rpr0sTrSgD4GIEVwPFFRUlt2gQ0sK5ZI910k1StmvTuu9Ljj1t4feQR6Yidh0NKcnKykpKSlJKS4nUp/jZ1qm1HdsQ2mQBwKNZhBXBi774rdexoU/KrVi2ww65aZctTpaRIZ50l3XuvdOutUsmSBXYTnmAd1nxYu1Y65xy7xjp08LoaAD7GCCuAE2vRQipWTHr//QI53PLlUufOUs2a0rx50gsv2Kz/e+4J/bCKfHr/fbu2WrTwuhIAPkdgBXBipUtbW8CoUdIpvCCTmipdc41Up47N4RoxwtoB/vlPqUSJAqwXocE5u6batpVKlfK6GgA+R2AFkLvbb5dWrpQ++yzfP7pokeXdBg2kb76RRo+WfvjBXv4vXjwAtSI0zJ8vffutXVsAkAsCK4DcNW9umwgMH57nH5k/X7rySpv8vX69zfxftUrq2VMqWjSAtSI0DB9u19Tll3tdCYAQQGAFkLuICKlPH5scs2XLcb/NOenTT6VLL5UuucSW13znHRtZ7dbNFh0AtHmzNHmyja5GRHhdDYAQQGAFkDc9etjQ6MiRR33JOWnmTOmii6QrrpB27ZKmTJGWLpU6dQr6Jlnwu5EjbbJV9+5eVwIgRPA0AiBvTj/dhklfeUXKzJRkQfX996VGjaSrrrKPZ8yQvv5auvpqgiqOITPTrqFu3UJ7oV0AQcXTCYC869NH2rhR2e9P08SJ0gUXWDD929+kjz+WPv/cgiuv8uK4pk6VNm1ishWAfGHjAAB5lpkp/VrrYu3YsFu1Mr5W8yuKqH9/6eKLva7MP9g44ASys6X69aXoaFuEFwDyiCkQAHJ14IDN8n/ySancmie1QM303ePvKPGRZK9LQyh5+21p2TJpwQKvKwEQYmgJAHBcGRnWbpiYKN10k1SrlvTC102ltm2VOO5hS7JAXuzfLz38sNSunc3OA4B8ILACOMrevdJLL9k273362KSq//3PZv7Xry8bal237pgrBgDHNHKkLcg7aJDXlQAIQfSwAjhozx4bUX3mGVtDtWtX6aGHpBo1jvHN3bvbTKs1a6SSJYNeq1/Rw3oMe/ZICQlSy5bS2LFeVwMgBDHCCkC7dkmDB0vx8dL990utW0vffWd9q8cMq5L0xBPSzp3SCy8Es1SEouefl377TXr8ca8rARCiCKxAIfb775Y74+KkRx6Rrr1WWr1aGj1aqlYtlx+Oj7d+gSFDpO3bg1EuQtH27dLTT9u1Eh/vdTUAQhQtAUAhtHOn9Nxz0osv2lyYm2+W7rtPqlQpnwfavt2GYJs3txngoCXgSJ07S7NnS6tWSWXLel0NgBDFCCtQiGzdasE0Ls4C68032zyY//73JMKqZAHkpZekd96RJk0q8HoR4iZOtLeXXyasAjgljLAChcAvv9irsq++KkVFSf/8p3T33QWUIZyzXoIFC6SVKwt9MGGE9U/bt0s1a0rNmtkfM2x/BuAUMMIKhLGffpLuuEOqUkV6/XUbXd2wwValKrBcGREhjRghZWVZEgYku/Cys6XhwwmrAE4ZgRUIQ+vW2cv955xjraWPPCL9+KP02GPS3/8egBssV47WAPwlpxXgpZfs2gCAU0RLABBGvv/eRk/Hj5fKlJH69bPJ2aVKBeHGaQ2QREsArQAAAoERViAMfPONLfJfo4b0ySfSs8/aZKp77w1SWJX+ag3IzrZNBbKygnTD/pScnKykpCSlpKR4XUrwZGXZuacVAEABY4QVCGHLlkkDBkiTJ0tnny395z9Sz57Saad5WNSsWbbzQN++NtOrkCnUI6z33isNGyZ9+KF05ZVeVwMgjDDCCoSgxYulpCSpbl0LrSNH2oL/ffp4HFYlqUULG+J95hnpjTc8LgZBM26cNHSoBVbCKoACRmAFQsjChVKrVlKjRtavOm6cve/VSypWzOvqDnHXXTbUe/PN0pdfel0NAm3RIjvXN90k3Xmn19UACEO0BAA+55w0d6699D9njs1nefhhqVMnKTLS6+pOICNDuuwyW0dr8WKpYkWvKwqKQtcSsGmT1KCBVLWq7WhVvLjXFQEIQ4ywAj7lnPTRRzbZ+vLLpd9+k959V1q+XEpO9nlYlSy4TJ5shV5zjbR3r9cVoaDt3StdfbXtRjF5MmEVQMAQWAGfcU6aNk268EJ7+f/AAft4yRKpQwepSCj9ry1fXnr/fVvG4Lrr7M4gPBw4YOd05Uo7x6y3CiCAQumpDwhr2dk2glqvnk2oKlbMRlgXLZLatg3hFYLq1bNF5GfMkG64odAvdxUWMjOl66+3czpxop1jAAggAivgsawsacIE6fzzpY4dpTPOsF7V+fNtwn3IBtVDtWljd3LSJJuYk53tdUU4WdnZNsvv3XdtG7U2bbyuCEAhQGAFPJKZabP8a9a0Rf8rVbJNombPli69NEyC6qE6dJDefNPebrqJkdZQlJlpqz/knMdrrvG6IgCFRJTXBQCFzf79FlSfekpat05q184+btTI68qCIDnZmnRvuEHat8/WaS1a1OuqkBcHDlgbwLvv2t6/ycleVwSgECGwAkGyb580erQ0ZIj000/Stdfac/8FF3hdWZB17WqzyZOTbemrt96SSpTwuiqcyN69NsEqp2eVkVUAQUZLABBgf/whPf+8lJAg/etf0kUX2aT5SZMKYVjN0aGDNGWKzSq7+GJbyxP+tGmTnaOPPrJzRlgF4AECKxAgu3dLTz8tVaki9etnu1WuWmUDijVrel2dD7RpI332mbRli9SwITti+dGXX9qmAFu2WIM1E6wAeITAChSwtDRp4EApLs52pGrfXvrhB+n116XERK+r85n69W0XrCpVpEsusWZe+MO4cXZOqlaVvv6apasAeIrAChSQX3+VHn3UgurAgdaquWaN9Oqr9pyP4yhf3pZGuO46qUcP6d57WUHAS1lZ9pJAjx5St252btgUAIDHmHQFnKLt26Vhw6SXXrLn+ltvtcwVG+t1ZSGkeHFp1CipTh3pnnusyXfcOKlsWa8rK1y2b5e6d5dmzZJeeMGarsNufTUAoSjCOee8LgIIRZs3S0OHSv/3f/acfscdUt++0llneV1ZiJs1y0ZbixSRhg+33RRCSHp6umJiYpSWlqbo6Givy8m7iROl22+3ZcdSUqzpGgB8gpYAIJ9+/tkGnqpUkUaOtAHBH3+05aoIqwWgRQvbn75pU6lTJ6lLFxv5Q2Bs3y517mxvF19sjz1hFYDPEFiBPFq/3l7uT0iwddMfesiC6oABUpkyXlcXZsqVs0VqU1KkTz+1ZRUmTfK6qvAzcaJ03nnWp5qzdS79qgB8iMAK5GL1atuNslo1W4ZywAALqv37S6ef7nV1YSwiwjYXOHS0tXNnRlsLwrFGVbt0oV8VgG8RWIHjWLXKdqI891zpww+lZ56xUdb775dKl/a6ukLk0NHW2bOlc86RBg2S9uzxurLQs2ePPXbnnMOoKoCQQmAFjrB8uQ081awpzZtnk6XXrZPuvlsqWdLr6gqpnNHWVatsuPuJJ6w3Y/hw2+MeJ7Z/vz1WCQn22N10kz2WjKoCCBEEVuBPqanS1VfbykqLF9vs/zVrpH/+k63ufaNsWdvn9vvvpZYt7eTUqGGjr9nZXlfnP9nZ9tjUqGGPVcuW9tg99xxLhgEIKQRWFHpffCFddZXtQPntt9KYMbYz1S232PKg8KH4eGnsWOl//7NJQ9ddZydw8mQpM9Pr6iRJycnJSkpKUkpKSvBvPDPTHov69e2xqVnTXjoYO9YeOwAIMazDikJr3jybQPXppzYA9fDD1goQxXYaoWfBAlu2Yf58qWJFW86hd2+pQoWgl+LpOqybN9taa6+8Im3aZBOqBg2ySWsAEMIYYUWh4pz0ySe2Rfqll9pk6YkTbWOl664jrIaspk3tL5AlS2y4fPBg6eyzrUdz3jw78eHKObuPXbrYfR482B6DJUvs84RVAGGAEVYUCs5JM2faiOqiRfZKaf/+Urt2tqESwszvv9vL38OHW3/HeedJvXpZk3LVqgG96aCNsK5dK73/vm1p++23UvXqtlNV9+6stwYg7BBYEdays6WpU6WBA21SVZMmFlRbtWJydKHgnC3fNHy4NH26zZavWVNKSrK3Ro0K/C+WgAXW7Gzpyy+ladPsol65UipWTGrb1vYFvuwyLmoAYYvAirCUlWVLdw4cKK1YYS0A/ftLl1/Oc3qhtWuX9PHHFvamT5d27rS9dNu2taH2f/yjQPbWLdDAunWrzQqcNs1q3rbNtlVr29YC95VXsigwgEKBwIqwkplpa6EPGiR99509n/fvLzVr5nVl8JWsrL+C4NSpdrFIUqVK1i9y6Fs+F9U/6cC6dau9DHDo28aN9rVzz/1rVPjCC6XIyHzVBAChjsCKsHDggPTGG9JTT9naqW3a2Kz/Cy/0ujKEhA0bbPHdQ8Pib7/Z1ypVkmrXlmJj7a1CBXvL+Xf58lLRogcPdczAeuCAtGWLzeL/5Rd7n/PvX36xlwFywukZZxwemBs2ZCkqAIUegRUhLSPD1k0dPFj68UfpmmssqNar53VlCGnO2T68OeF11aq/AuaWLTZCe6hSpWyJiaJFlR0ZqS1btqj8mWeqSGamDfvv3n3490dGWtDNCb01avwVUKtUoW8FAI5AYEVI2rvXlpscMsQyRKdOFlRr1/a6MoS9rCxpx47DR0vT0iyYZmZq3+7deuqpp/RA//46LSfIxsQcPipbtizLUwBAPhBYEVL27LEtU595xtZQve466cEHbYAK8ANPNw4AgDDFMukICenp0ssvS8OG2RKb3btLDzwgnXOO15UBAIBAI7DC1377Tfrvf6UXXrDR1Ztuku6/nzkoAAAUJgRW+NKOHdJzz0kvvWRrvd9yi3TvvTZhGwAAFC4EVvjK1q3S0KHSiBE2Ufv226W+fW1CNQAAKJwIrPCFTZukp5+WXn3VlrS86y7p7rulM8/0ujIAAOA1Ais89eOPtjTVqFHS3/4m/ec/0p132trpAAAAEoEVHlm71nalGjvWlqh89FHpn/+UWAUIAAAcicCKoPr+e2nQIOmtt+zl/qeekm67zTYKAgAAOBYCK4Lim2+kgQOld96xjX6GDZNuvlkqUcLrygAAgN+xNyACaulS6dprbcvUL76wxf/XrrU+VcIqAADICwIrAuKrr6R27aR69aRly6SRI6XVq6U+faTTTvO6OgAAEEoIrChQCxZILVtKjRtbQB03zvpWe/WSihXzujoAABCKCKw4Zc5Js2dLl10mNWsm/fKLNGGCtHKldMMNUhSd0iiEkpOTlZSUpJSUFK9LAYCQF+Gcc14XgdDknPTRR9KAAdLnn0t160r9+0vt20tF+FMIhVR6erpiYmKUlpamaNZpA4ACQaxAvjknTZ1qL/u3bi1lZUnTp0upqdI11xBWAQBAwSJaIM+ys6VJk2wktX17mzw1a5bN/m/TRoqI8LpCAAAQjgisyFVWlpSSIp1/vtSpk1SmjDR3rjR/vnTllQRVAAAQWEyHQa6uvFKaM0dq1Up69VXpH//wuiIAAFCYMOkKuZo+XSpXTmrY0OtKAP9j0hUAFDxGWJGrtm29rgAAABRm9LACAADA1wisAGS+Xi8AAAvISURBVAAA8DUCKwAAAHyNwAoAAABfI7ACAADA1wisAAAA8DUCKwAAAHyNwAoAAABfI7ACAADA1wisAAAA8DUCK4CwdOONNyoiIuKwtwsvvDDfx8nIyNBDDz2kuLg4FS9eXAkJCRo9enQAKgYAHE+U1wUAQKC0atVKY8aMOfhxsWLF8n2Mzp07a+vWrRo1apTOOeccbdu2TZmZmQVZJgAgFwRWAGGrePHiKl++/HG/npaWpnvvvVfvvfee9u3bpwYNGui5555TnTp1JEkffvih5s2bp3Xr1unvf/+7JCk+Pj4YpQMADkFLAICwNXfuXJ111llKTEzUzTffrG3bth38mnNObdq00ZYtW/TBBx8oNTVV9erVU/PmzfXrr79KkqZOnaoGDRro6aefVsWKFZWYmKh+/fpp7969Xt0lACiUGGEFEJZat26tTp06KS4uTuvXr1f//v11+eWXKzU1VcWLF9ecOXO0YsUKbdu2TcWLF5ckDR06VO+9954mTZqkW265RevWrdOCBQt02mmnacqUKdqxY4duv/12/frrr/SxAkAQEVgBhLzx48fr1ltvPfjxzJkz1aVLl4Mf16pVSw0aNFBcXJxmzJihDh06KDU1Vbt3/3879/MaR/3Hcfy9GgQhOxOs0C4R6qUpRUEaFqIU4sGCh+IaUGGCFLX2X/DiSW8WRFEQBKl4KVuoSvGwHjxY8eCpIPQg/iiGVtIctDrbiJ4638MX8qXUVvslcd7ZPh6wh6Uzm9dcwpNPt12PHTt2XPNZf/zxR5w/fz4iIq5evRqdTidOnDgRZVlGRMQbb7wRTz/9dLzzzjtx9913/wtPB4BgBba9wWAQCwsLG+9nZ2evu6bX68Xu3bvj+++/j4j/xmiv14szZ85cd+3MzMzGPbOzsxuxGhGxb9++aJomfvrpp9izZ88NN1VVFVNT1/6KXV5ejuXl5Vt6NgAEKzABut1udLvdm17zyy+/xMWLF6PX60VExPz8fKytrcXU1NQN/yHVgQMH4tSpU7G+vh7T09MREfHdd9/FHXfcEffdd99Nf97JkyejKIpbfxgArtNpmqZpewTAZlpfX49XXnklnnrqqej1erGyshIvv/xyXLhwIb755pvodrvRNE0sLi7GlStX4tixY7F3795YXV2N0WgUS0tL0e/3Y319Pfbt2xcPP/xwvPrqq/Hzzz/H0aNH49FHH4333nvvL3/2eDyOsiyjrmvBCrBJ/C8BwMS5884749y5c/Hkk0/G3NxcPPfcczE3NxdfffXVxklsp9OJ0WgUi4uLceTIkZibm4uqqmJlZSV27twZERHT09Px2WefxW+//Rb9fj+effbZeOKJJ+Ltt99u8/EAbjtOWAE2kRNWgM3nhBUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVoAtUFVVDAaDGA6HbU8B2PY6TdM0bY8AmBTj8TjKsoy6rqMoirbnAEwEJ6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEK8AWqKoqBoNBDIfDtqcAbHudpmmatkcATIrxeBxlWUZd11EURdtzACaCE1YAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQqwBaqqisFgEMPhsO0pANtep2mapu0RAJNiPB5HWZZR13UURdH2HICJ4IQVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAXYAlVVxWAwiOFw2PYUgG2v0zRN0/YIgEkxHo+jLMuo6zqKomh7DsBEcMIKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUm0scffxyPP/543HvvvdHpdOLrr7++5c94/vnno9PpXPd64IEHtmAxADciWIGJ9Pvvv8eBAwfitdde+78/46233opLly5tvC5evBj33HNPPPPMM5u4FIC/M9X2AICtcPjw4YiIWFlZueE1dV3HSy+9FKdPn44///wz+v1+vPnmm/HQQw9FRERZllGW5cb1p0+fjl9//TVeeOGFLd0OwLWcsAK3paZp4tChQ7G2thaj0SjOnj0b8/Pz8dhjj8Xly5f/8p7jx4/HwYMHY/fu3f/yWoDbm2AFbkuff/55nDt3Lk6dOhX9fj/27NkTr7/+eszMzMSHH3543fWXLl2KTz/9NI4ePdrCWoDbm2AFtr0TJ07E9PT0xuvLL7/823vOnj0b6+vrsWPHjmvu/fHHH+P8+fPXXf/BBx/EzMxMLC0tbcUjAHATvsMKbHuDwSAWFhY23s/Ozv7tPVevXo1erxdnzpy57s9mZmaued80Tbz//vtx+PDhuOuuu/7RpqqqYmrq2l+xy8vLsby8/I/uB+B/BCuw7XW73eh2u7d0z/z8fKytrcXU1FTcf//9N732iy++iB9++CFefPHFf/z5J0+ejKIobmkTAH9NsAIT6fLly3HhwoVYXV2NiIhvv/02IiJ27doVu3btioMHD8YjjzwSS0tLcezYsdi7d2+srq7GaDSKpaWl6Pf7G591/PjxWFhYiAcffLCVZwG43fkOKzCRPvnkk9i/f38cOnQoIv77V/T79++Pd999NyIiOp1OjEajWFxcjCNHjsTc3FxUVRUrKyuxc+fOjc+p6zo++uijWzpdBWBzdZqmadoeATApxuNxlGUZdV37SgDAJnHCCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVaALVBVVQwGgxgOh21PAdj2Ok3TNG2PAJgU4/E4yrKMuq6jKIq25wBMBCesAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVYAtUVRWDwSCGw2HbUwC2vU7TNE3bIwAmxXg8jrIso67rKIqi7TkAE8EJKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKsAWqqorBYBDD4bDtKQDbXqdpmqbtEQCTYjweR1mWUdd1FEXR9hyAieCEFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAmyBqqpiMBjEcDhsewrAttdpmqZpewTApBiPx1GWZdR1HUVRtD0HYCI4YQUAIDXBCgBAar4SALCJmqaJK1euRLfbjU6n0/YcgIkgWAEASM1XAgAASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFL7D98vsd4H4qk9AAAAAElFTkSuQmCC\n",
"text/plain": [
"Graphics object consisting of 5 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Old code- newer 3D plot above is more useful.\n",
"\n",
"#Examine the possibility of collisions by plotting the position of Phobos versus the capture orbit sling.\n",
"#Plot capture sling with its line of apsides pointing out of the screen, parallel to the intersection between\n",
"#the ecliptic plane and the plane that Phobos and Deimos orbit in (e.g. Mars's equatorial plane).\n",
"#Here, the argument of periapsis of the capture sling is 0 degrees.\n",
"print moon_frame_string\n",
"\n",
"#Calculate cosine of capture orbit's true anomaly at Phobos.\n",
"var('cos_ta') #Resets cos_ta so it's just a variable again.\n",
"eq4 = r == a*(1-ecc^2)/(1+ecc*cos_ta) # https://en.wikipedia.org/wiki/True_anomaly#Radius_from_true_anomaly\n",
"#soln4 = solve(eq4.subs(r=periapsis_phobos,a=a_capture),cos_ta) #capture orbit's true anomaly at Phobos\n",
"soln4 = solve(eq4.subs(r=periapsis_phobos-r_max,a=a_capture),cos_ta) #capture orbit's true anomaly 1 sling length below Phobos (just to be safer)\n",
"cos_ta = soln4[0].rhs()\n",
"sin_ta = sqrt(1-cos_ta^2)\n",
"print 'cosine of capture orbit\\'s true anomaly at Phobos =',cos_ta.n()\n",
"\n",
"gfx = Graphics() #Lots of graphics code on the web says \"g = Graphics()\" but that can't be used here because g is already 9.81 m/s^2.\n",
"frame_size=4.0\n",
"#r_max = 2000000 #Just to see what it looks like, redefine r_max, in meters.\n",
"angle=inc_phobos #Use the lower Phobos angle because those collisions are more likely.\n",
"gfx += circle((0,0), radius_mars, rgbcolor=(1,0,0)) #Mars is plotted as a red circle.\n",
"gfx += circle((periapsis_phobos*sin_ta*cos(angle),periapsis_phobos*sin_ta*sin(angle)), r_max, rgbcolor=(1,0,1)) #Max sling extension when sling COM is at Phobos's periapsis.\n",
"gfx += line([(-frame_size*radius_mars,r_max), (frame_size*radius_mars,r_max)], rgbcolor=(1,0,1)) #Max sling extension from Phobos or Deimos.\n",
"gfx += line([(-b_capture*cos(angle),-b_capture*sin(angle)), (b_capture*cos(angle),b_capture*sin(angle))]) #Plot limits of capture sling center of mass motion in ecliptic plane.\n",
"\n",
"#Calculate cosine of capture orbit's true anomaly at Deimos.\n",
"var('cos_ta') #Resets cos_ta so it's just a variable again.\n",
"eq4 = r == a*(1-ecc^2)/(1+ecc*cos_ta) # https://en.wikipedia.org/wiki/True_anomaly#Radius_from_true_anomaly\n",
"#soln4 = solve(eq4.subs(r=periapsis_deimos,a=a_capture),cos_ta) #capture orbit's true anomaly at Deimos\n",
"soln4 = solve(eq4.subs(r=periapsis_deimos,a=a_capture),cos_ta) #capture orbit's true anomaly at Deimos.\n",
"cos_ta = soln4[0].rhs()\n",
"sin_ta = sqrt(1-cos_ta^2)\n",
"print 'cosine of capture orbit\\'s true anomaly at Deimos =',cos_ta.n()\n",
"\n",
"#Max sling extension when sling COM is at Deimos's periapsis.\n",
"gfx += circle((periapsis_deimos*sin_ta*cos(angle),periapsis_deimos*sin_ta*sin(angle)), r_max, rgbcolor=(0,0,1))\n",
"\n",
"if textonly == 0: gfx.show(xmin=-frame_size*radius_mars, xmax=frame_size*radius_mars, ymin=-frame_size*radius_mars, ymax=frame_size*radius_mars, figsize=[7,7])\n",
"else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where Deimos stays in the x-y plane and the capture slings are inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"Graphics object consisting of 5 graphics primitives"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot capture sling with its line of apsides lying in the screen, perpendicular to the intersection between\n",
"#the ecliptic plane and the plane that Phobos and Deimos orbit in (e.g. Mars's equatorial plane).\n",
"#Here, the argument of periapsis of the capture sling is 90 degrees.\n",
"print moon_frame_string\n",
"gfx = Graphics()\n",
"angle=inc_phobos #Use the lower Phobos angle because those collisions are more likely here.\n",
"gfx += circle((0,0), radius_mars, rgbcolor=(1,0,0)) #Mars is plotted as a red circle.\n",
"gfx += circle((a_phobos,0), r_max, rgbcolor=(1,0,1)) #Plot sling around Phobos.\n",
"#gfx += circle((a_deimos,0), r_max, rgbcolor=(0,0,0)) #Don't bother plotting Deimos or zooming out to see it. If the sling misses Phobos it'll certainly miss Deimos.\n",
"gfx += line([(-periapsis_capture*cos(angle),-periapsis_capture*sin(angle)), (apoapsis_capture*cos(angle),apoapsis_capture*sin(angle))]) #Plot capture sling center of mass in ecliptic plane.\n",
"gfx += line([(0,-r_max), (apoapsis_capture,apoapsis_capture*tan(angle)-r_max)]) #Plot sling limits.\n",
"gfx += line([(0,r_max), (apoapsis_capture,apoapsis_capture*tan(angle)+r_max)])\n",
"if textonly == 0: gfx.show(xmin=-periapsis_capture*cos(angle), xmax=(a_phobos+r_max)*1.1, ymin=-2*radius_mars, ymax=2*radius_mars, figsize=[7,7])\n",
"else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"capture orbit eccentricity = 0.717360803768281\n",
"capture orbit's true anomaly at the moon = 173.712720193813\n",
"capture orbit's argument of periapsis which hits the moon = 6.28727980618746\n",
"capture orbit's flight path angle at the moon = 15.3109755656732\n"
]
}
],
"source": [
"#Capture orbit speed at the moon (either Phobos or Deimos)\n",
"capture_speed_moon = sqrt(mu*(2/a_moon-1/a_capture))\n",
"\n",
"#Capture orbit eccentricity\n",
"ecc = (apoapsis_capture - periapsis_capture) / (apoapsis_capture + periapsis_capture)\n",
"print 'capture orbit eccentricity =',ecc\n",
"\n",
"#Calculate cosine of capture orbit's true anomaly at the moon (either Phobos or Deimos).\n",
"var('cos_ta') #Resets cos_ta so it's just a variable again.\n",
"var('ta fpa') #true anomaly, flight path anomaly\n",
"eq3 = r == a*(1-ecc^2)/(1+ecc*cos(ta)) # https://en.wikipedia.org/wiki/True_anomaly#Radius_from_true_anomaly\n",
"soln3 = solve(eq3.subs(r=a_moon,a=a_capture),ta)\n",
"ta = soln3[0].rhs() #True anomaly of the capture orbit at the moon.\n",
"print 'capture orbit\\'s true anomaly at the moon =',(ta*180/pi).n()\n",
"\n",
"AP = pi - ta\n",
"print 'capture orbit\\'s argument of periapsis which hits the moon =',(AP*180/pi).n()\n",
"\n",
"#Capture orbit's flight path angle at the moon.\n",
"fpa = my_arccos((1 + ecc*cos(ta))/sqrt(1+ecc^2+2*ecc*cos(ta)),'fpa')\n",
"print 'capture orbit\\'s flight path angle at the moon =',(fpa*180/pi).n()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"fpa(ta) = arccos((1 + ecc*cos(ta*pi/180))/sqrt(1+ecc^2+2*ecc*cos(ta*pi/180)))\n",
"angles = 10\n",
"if textonly == 0: plot(fpa,(ta,-angles,angles),axes_labels=['true anomaly (degrees)','flight path angle (degrees)'])\n",
"else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"if textonly == 0:\n",
" plot(fpa+ta,(ta,-angles,angles),axes_labels=['true anomaly (degrees)','flight path angle + true anomaly (degrees)'])\n",
" #sum_fpa_ta(ta) = ta + arccos((1 + ecc*cos(ta*pi/180))/sqrt(1+ecc^2+2*ecc*cos(ta*pi/180)))\n",
" #plot(sum_fpa_ta,(ta,-angles,angles),axes_labels=['true anomaly (degrees)','flight path angle + true anomaly (degrees)'])\n",
"else: print 'Textonly =',textonly,'so this image wasn\\'t displayed.'"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## Appendix A: Counterbalance mass ratios above 4.6033 cause collisions. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dangerous mass ratio: 4.60333884875169\n"
]
}
],
"source": [
"#Consider an asymmetric double sling where each mass is released with equal and opposite linear momentum.\n",
"#Does the longer sling (radius r1) hit the heavier mass (m2) after it's released from the shorter sling (radius r2 < r1)?\n",
"var('t v1 v2 r1 r2 mass_ratio')\n",
"mass_ratio = 2\n",
"#mass_ratio = 4.60333884875169 #mass_ratio = m2/m1. Since m1*v1 = m2*v2, mass_ratio = v1/v2. Since v1/r1 = v2/r2 (= omega), mass_ratio = r1/r2.\n",
"v2 = 1000\n",
"r2 = 1000\n",
"theta1 = v1*t/r1 - pi #theta (angle) of end of sling that held mass 1, where t=0 is the time of release.\n",
"theta2 = arctan(v2*t/r2) #theta (angle) of mass 2, where t=0 is the time of release.\n",
"\n",
"#If a closed form solution existed, this is how to solve for it:\n",
"#eq_thetas = theta1 == theta2\n",
"#soln_thetas = solve(eq_thetas.subs(v1=mass_ratio*v2, r1 = mass_ratio*r2),t)\n",
"#soln_thetas\n",
"#time_collision = soln_thetas[0].rhs()\n",
"#print 'time after release when thetas are equal =',t.n()\n",
"#distance_m2 = sqrt(r2^2 + (v2*time_collision)^2)\n",
"#distance_m2\n",
"\n",
"theta1 = theta1.subs(v1=mass_ratio*v2, r1=mass_ratio*r2)\n",
"theta2 = theta2.subs(v1=mass_ratio*v2, r1=mass_ratio*r2)\n",
"theta1 - theta2\n",
"time_collision = find_root(theta1 - theta2,0,3*pi/2*mass_ratio*r2/(mass_ratio*v2))\n",
"time_collision\n",
"distance_m2 = sqrt(r2^2 + (v2*time_collision)^2)\n",
"distance_m2\n",
"r2*mass_ratio\n",
"print 'Dangerous mass ratio:',distance_m2/r2.n()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"16.6441331547577\n",
"42.4709021766231\n",
"126.920842043550\n"
]
}
],
"source": [
"#Thinking about the view from various orbits. How wide is Mars in the sky, in degrees? https://en.wikipedia.org/wiki/Angular_diameter\n",
"print (2*arcsin(radius_mars/a_deimos)*180/pi).n()\n",
"print (2*arcsin(radius_mars/a_phobos)*180/pi).n()\n",
"print (2*arcsin(radius_mars/(radius_mars+lmo_alt))*180/pi).n()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test xyz2kepler:\n",
"-------------------------------------------------------\n",
"Parabolic trajectory\n",
"Position vector (km) = (-10833.5478394527, 20478.0780800630, 3715.41566949513)\n",
"Velocity vector (km/s) = (-1.45376858821605, -0.919909591255915, 0.831268736298597)\n",
"Radial distance (km) = 23463.2000000000\n",
"Speed (km/s) = 1.91067644464357\n",
"Radial velocity (km/s) = 8.12910699654139e-17\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Specific relative angular momentum vector = (2.04406325973091e10, 3.60423502965454e9, 3.97362712246738e10)\n",
"Specific relative angular momentum (m^2/s) = 4.48305835559610e10\n",
"Specific orbital energy (kJ/kg) = -6.98491930961609e-13\n",
"Eccentricity vector = (-0.461725077544953, 0.872774305297787, 0.158350765006270)\n",
"Eccentricity = 1.00000000000000 , Alt. calc. = 0.999999999999999\n",
"Semi-latus rectum (km) = 46926.4000000000 , Alt. calc. = 0.000000000000000\n",
"Periapsis altitude (km) = 20067.2000000000\n",
"Periapsis (km) = 23463.2000000000\n",
"Inclination (degrees) = 27.5800000000000\n",
"Longitude of the ascending node (degrees) = 100.000000000000 , Alt. calc. = 100.000000000000\n",
"Argument of periapsis (degrees) = 19.9999975851635 , Alt. calc. = 20.0000000000000\n",
"Mean anomaly (degrees) = 2.43768914102554e-15\n",
"Eccentric anomaly (degrees) = 2.43768914102554e-15\n",
"True anomaly (degrees) = 4.87537828205107e-15 , Alt. calc. = 2.41483653945147e-6\n",
"Time since periapsis (hours) = 2.90257384956943e-16\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"Position vector at periapsis = (-1.08335478394527e7, 2.04780780800630e7, 3.71541566949513e6)\n",
"Velocity vector at periapsis = (-1453.76858821605, -919.909591255915, 831.268736298597)\n",
"\n",
"Test kepler2xyz:\n",
"-------------------------------------------------------\n",
"Parabolic trajectory\n",
"Position vector (km) = (-23048.7868495659, 8639.34623579946, 11072.8444292497)\n",
"Velocity vector (km/s) = (-0.966417705366111, -1.36176599074238, 0.620649929569519)\n",
"Radial distance (km) = 26990.6050760836 , Alt. calcs. = 0.000000000000000 , 0.000000000000000 , 26990.6050760836\n",
"Speed (km/s) = 1.78145337571948\n",
"Radial velocity (km/s) = 0.644014755453581\n",
"\n",
"Position vector after 10000. seconds = (-2.30487868495659e7, 8.63934623579946e6, 1.10728444292497e7)\n",
"Velocity vector after 10000. seconds = (-966.417705366111, -1361.76599074238, 620.649929569519)\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n",
"Position vector (km) = (-10833.5478394527, 20478.0780800630, 3715.41566949513)\n",
"Velocity vector (km/s) = (-1.45378312590194, -0.919918790351828, 0.831277048985960)\n",
"Radial distance (km) = 23463.2000000000\n",
"Speed (km/s) = 1.91069555140802\n",
"Radial velocity (km/s) = -2.03227674913535e-17\n",
"Flight path angle (degrees) = 1.20741826972573e-6\n",
"Specific relative angular momentum vector = (2.04408370036351e10, 3.60427107200484e9, 3.97366685873860e10)\n",
"Specific relative angular momentum (m^2/s) = 4.48310318617965e10\n",
"Specific orbital energy (kJ/kg) = 0.0365070272954181\n",
"Hyperbolic excess velocity at infinity (km/s) = 0.00854482618845089 , C3 (km/s)^2: 0.0000730140545908362\n",
"Periapsis vs infinity deflection (degrees) = 89.4875382285622\n",
"Eccentricity vector = (-0.461743546640400, 0.872809216444554, 0.158357099068541)\n",
"Eccentricity = 1.00004000020000 , Alt. calc. = 1.00004000020000\n",
"Semi-latus rectum (km) = 46927.3385326926 , Alt. calc. = 46927.3385326521\n",
"Semi-minor axis (km) = -5.24657036586308e6\n",
"Semi-major axis (km) = -5.86577067114080e8\n",
"Periapsis altitude (km) = 20067.2000000098\n",
"Periapsis (km) = 23463.2000000098\n",
"Inclination (degrees) = 27.5800000000000\n",
"Longitude of the ascending node (degrees) = 100.000000000000 , Alt. calc. = 100.000000000000\n",
"Argument of periapsis (degrees) = 20.0001064858035 , Alt. calc. = 20.0000000000000\n",
"Mean anomaly (degrees) = -2.18026459245927e-22\n",
"Eccentric anomaly (degrees) = -5.45063422797031e-18\n",
"True anomaly (degrees) = -1.21880800639446e-15 , Alt. calc. = 359.999893514197\n",
"Time since periapsis (hours) = -7.25614437670051e-17\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"-------------------------------------------------------\n",
"Hyperbolic trajectory\n",
"Position vector (km) = (-23048.9455436952, 8639.21594898299, 11072.9378802099)\n",
"Velocity vector (km/s) = (-0.966430751924128, -1.36177442747966, 0.620657406060744)\n",
"Radial distance (km) = 26990.7372294344 , Alt. calcs. = 26990.7372294129 , 26990.7372294111 , 26990.7372294344\n",
"Speed (km/s) = 1.78146950726086\n",
"Radial velocity (km/s) = 0.644037514592159\n",
"-------------------------------------------------------\n",
"Parabolic trajectory\n",
"Position vector (km) = (-23048.7868495659, 8639.34623579946, 11072.8444292497)\n",
"Velocity vector (km/s) = (-0.966417705366111, -1.36176599074238, 0.620649929569519)\n",
"Radial distance (km) = 26990.6050760836\n",
"Speed (km/s) = 1.78145337571948\n",
"Radial velocity (km/s) = 0.644014755453581\n",
"Flight path angle (degrees) = 21.1930126014065\n",
"Specific relative angular momentum vector = (2.04406325973091e10, 3.60423502965454e9, 3.97362712246738e10)\n",
"Specific relative angular momentum (m^2/s) = 4.48305835559610e10\n",
"Specific orbital energy (kJ/kg) = -2.32830643653870e-13\n",
"Eccentricity vector = (-0.461725045476852, 0.872774325589705, 0.158350746669645)\n",
"Eccentricity = 1.00000000000000 , Alt. calc. = 0.999999999999999\n",
"Semi-latus rectum (km) = 46926.4000000000 , Alt. calc. = 0.000000000000000\n",
"Periapsis altitude (km) = 20067.2000000000\n",
"Periapsis (km) = 23463.2000000000\n",
"Inclination (degrees) = 27.5800000000000\n",
"Longitude of the ascending node (degrees) = 100.000000000000 , Alt. calc. = 100.000000000000\n",
"Argument of periapsis (degrees) = 19.9999975851635 , Alt. calc. = 19.9999975851635\n",
"Mean anomaly (degrees) = 23.3288077272363\n",
"Eccentric anomaly (degrees) = 22.2155300083768\n",
"True anomaly (degrees) = 42.3860252028129 , Alt. calc. = 42.3860252028129\n",
"Time since periapsis (hours) = 2.77777777777778\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"Compare position vectors:\n",
"-------------------------------------------------------\n",
"#1 = (-2.30487868495659e7, 8.63934623579946e6, 1.10728444292497e7)\n",
"#2 = (-2.30489455436952e7, 8.63921594898299e6, 1.10729378802099e7)\n",
"delta = (-158.694129355252, -130.286816466600, 93.4509602226317)\n",
"#1 magnitude, inclination, azimuth (deg) = (2.69906050760836e7, 65.7795790422118, 159.452561246828)\n",
"#2 magnitude, inclination, azimuth (deg) = (2.69907372294344e7, 65.7794877145005, 159.452974871242)\n",
"delta magnitude, inclination, azimuth (deg) = (225.591584956874, 65.5280044514173, -140.614233972311)\n",
"angle between vectors #1 and #2 (degrees) = 0.000388112202576242\n",
"Normalized cross product = (0.455952855757493, 0.0803967904347765, 0.886365246055131)\n",
"\n",
"Compare velocity vectors:\n",
"-------------------------------------------------------\n",
"#1 = (-966.417705366111, -1361.76599074238, 620.649929569519)\n",
"#2 = (-966.430751924128, -1361.77442747966, 620.657406060745)\n",
"delta = (-0.0130465580169812, -0.00843673727740679, 0.00747649122502025)\n",
"#1 magnitude, inclination, azimuth (deg) = (1781.45337571948, 69.6108059769490, -125.362549506137)\n",
"#2 magnitude, inclination, azimuth (deg) = (1781.46950726086, 69.6107422826181, -125.362747032490)\n",
"delta magnitude, inclination, azimuth (deg) = (0.0172420744986282, 64.3025483330838, -147.110712056600)\n",
"angle between vectors #1 and #2 (degrees) = 0.000195804754040588\n",
"Normalized cross product = (-0.455952855779612, -0.0803967903679473, -0.886365246049814)\n"
]
}
],
"source": [
"print 'Test xyz2kepler:'\n",
"#xyz2kepler?\n",
"#rotateX?\n",
"#rotateY?\n",
"#rotateZ?\n",
"#xyz2spherical?\n",
"#spherical2xyz?\n",
"#compare_vectors?\n",
"\n",
"r4t = a_moon #Radial distance for test.\n",
"cv4t = sqrt(mu/r4t) #Circular speed for test, given r4t.\n",
"pv4t = sqrt(2*mu/r4t) #Parabolic speed for test, given r4t.\n",
"#v4t = pv4t*9e-18 #Speed to be used for test THAT CAUSES LOTS OF PROBLEMS.\n",
"v4t = pv4t*1.0 #Speed to be used for test.\n",
"\n",
"#Rotated trajectory\n",
"fpa4t = 0.0*pi/180 #Flight path angle for test. Convert degrees to radians.\n",
"inc4t = 0.0*pi/180 #Inclination for test. Convert degrees to radians.\n",
"inc4t = inc_moon #Inclination. Already in radians.\n",
"LAN4t = 100.0*pi/180 #Longitude of ascending node for test. Convert degrees to radians.\n",
"AP4t = 20.0*pi/180 #Argument of periapsis for test. Convert degrees to radians.\n",
"p_in_4t = vector([r4t,0,0]) #Position vector used for test.\n",
"v_in_4t = vector([0,v4t,0]) #Velocity vector used for test.\n",
"\n",
"#Radial trajectory.\n",
"#p_in_4t = vector([0,-r4t,0])\n",
"#v_in_4t = vector([0,v4t,0])\n",
"\n",
"#Capture orbit, tested at periapsis and apoapsis.\n",
"#p_in_4t = vector([periapsis_capture,0,0]) #position vector used for test. Velocity vector is here:\n",
"#v_in_4t = vector([0,capture_speed_periapsis,0])\n",
"#p_in_4t = vector([-apoapsis_capture,0,0]) #position vector used for test. Velocity vector is here:\n",
"#v_in_4t = vector([0,-capture_speed_apoapsis,0])\n",
"\n",
"#print 'Position vector =',p_in_4t.n()\n",
"#print 'Velocity vector =',v_in_4t.n()\n",
"\n",
"fpa_rotation = rotateZ(fpa4t)\n",
"#print 'Flight path angle rotation matrix ='\n",
"#fpa_rotation.n()\n",
"\n",
"v_in_4to = v_in_4t\n",
"v_in_4t = fpa_rotation*v_in_4t\n",
"#print 'Velocity vector =',v_in_4t.n()\n",
"#dv = compare_vectors(v_in_4to,v_in_4t)\n",
"\n",
"#https://en.wikipedia.org/wiki/Orbital_elements#Euler_angle_transformations\n",
"#http://web.archive.org/web/20171123040031/http://www.physics.csbsju.edu/orbit/orbit.3d.html\n",
"#\"I, J is in the equatorial plane of the central body. I is in the direction of the vernal equinox. J is perpendicular to I and with I defines the reference plane. K is perpendicular to the reference plane. Orbital elements of bodies (planets, comets, asteroids,...) in the solar system usually use the ecliptic as that plane.\"\n",
"#\"x, y are in the orbital plane and with x in the direction to the pericenter (periapsis). z is perpendicular to the plane of the orbit. y is mutually perpendicular to x and z.\"\n",
"#Check- is this orbit not inclined?\n",
"if inc4t < tolerance or inc4t > pi - tolerance:\n",
" if abs(LAN4t) > tolerance: #Even if a nonzero LAN is specified, set LAN=0 because that's true by definition for orbits that aren't inclined.\n",
" print warnstring,'Since this orbit isn\\'t inclined, LAN of',LAN4t,'was set to zero.'\n",
" LAN4t=0\n",
"#\"the transformation from the I, J, K coordinate frame to the x, y, z frame\":\n",
"#inverse_rotation = rotateZ(-AP)*rotateX(-inc)*rotateZ(-LAN)\n",
"#The capture orbit's position and velocity vectors were initially defined in the frame x, y, z. Now we need to rotate to the frame I, J, K. That's the inverse of the rotation given in the references above. So reverse the order and sign of the rotations:\n",
"rotation = rotateZ(LAN4t)*rotateX(inc4t)*rotateZ(AP4t)\n",
"#print 'Rotation matrix ='\n",
"#print rotation.n()\n",
"\n",
"p_in_4t = rotation*p_in_4t\n",
"v_in_4t = rotation*v_in_4t\n",
"\n",
"kepler = xyz2kepler(p_in_4t,v_in_4t)\n",
"(kepler).n()\n",
"\n",
"print '\\nPosition vector at periapsis =',p_in_4t.n()\n",
"print 'Velocity vector at periapsis =',v_in_4t.n()\n",
"\n",
"print '\\nTest kepler2xyz:'\n",
"#kepler2xyz?\n",
"\n",
"kepler[5] = 10000 #adjust time since periapsis.\n",
"\n",
"p_out_4t,v_out_4t = kepler2xyz(kepler)\n",
"\n",
"print '\\nPosition vector after',kepler[5].n(digits=5),'seconds =',p_out_4t.n()\n",
"print 'Velocity vector after',kepler[5].n(digits=5),'seconds =',v_out_4t.n()\n",
"\n",
"#If the first trajectory was parabolic, make a second one that's \"just barely hyperbolic\":\n",
"v2_in_4t = (1.0+10*tolerance)*v_in_4t\n",
"kepler2 = xyz2kepler(p_in_4t,v2_in_4t)\n",
"kepler2[5] = kepler[5] #adjust time since periapsis.\n",
"p2_out_4t,v2_out_4t = kepler2xyz(kepler2)\n",
"\n",
"xyz2kepler(p_out_4t,v_out_4t)\n",
"\n",
"print '\\nCompare position vectors:'\n",
"dv = compare_vectors(p_out_4t,p2_out_4t)\n",
"\n",
"print '\\nCompare velocity vectors:'\n",
"dv = compare_vectors(v_out_4t,v2_out_4t)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test rotateV:\n",
"rotateX =\n",
"[ 1.00000000000000 0.000000000000000 0.000000000000000]\n",
"[ 0.000000000000000 0.866025403784439 -0.500000000000000]\n",
"[ 0.000000000000000 0.500000000000000 0.866025403784439]\n",
"rotateV =\n",
"[ 1.00000000000000 0.000000000000000 0.000000000000000]\n",
"[ 0.000000000000000 0.866025403784439 -0.500000000000000]\n",
"[ 0.000000000000000 0.500000000000000 0.866025403784439]\n",
"rotateY =\n",
"[ 0.866025403784439 0.000000000000000 0.500000000000000]\n",
"[ 0.000000000000000 1.00000000000000 0.000000000000000]\n",
"[-0.500000000000000 0.000000000000000 0.866025403784439]\n",
"rotateV =\n",
"[ 0.866025403784439 0.000000000000000 0.500000000000000]\n",
"[ 0.000000000000000 1.00000000000000 0.000000000000000]\n",
"[-0.500000000000000 0.000000000000000 0.866025403784439]\n",
"rotateZ =\n",
"[ 0.866025403784439 -0.500000000000000 0.000000000000000]\n",
"[ 0.500000000000000 0.866025403784439 0.000000000000000]\n",
"[ 0.000000000000000 0.000000000000000 1.00000000000000]\n",
"rotateV =\n",
"[ 0.866025403784439 -0.500000000000000 0.000000000000000]\n",
"[ 0.500000000000000 0.866025403784439 0.000000000000000]\n",
"[ 0.000000000000000 0.000000000000000 1.00000000000000]\n"
]
}
],
"source": [
"print 'Test rotateV:'\n",
"#rotateV?\n",
"var('angle0')\n",
"angle0 = pi/6\n",
"unit0 = vector([1,0,0])\n",
"print 'rotateX =\\n',rotateX(angle0).n()\n",
"print 'rotateV =\\n',rotateV(unit0,angle0).n()\n",
"unit0 = vector([0,1,0])\n",
"print 'rotateY =\\n',rotateY(angle0).n()\n",
"print 'rotateV =\\n',rotateV(unit0,angle0).n()\n",
"unit0 = vector([0,0,1])\n",
"print 'rotateZ =\\n',rotateZ(angle0).n()\n",
"print 'rotateV =\\n',rotateV(unit0,angle0).n()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"These are old, discarded calculations that predate most of the variables used above such as grapples and ballast, etc.\n",
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"capture orbit speed at periapsis (m/s) = 4331.39333469434\n",
"dump orbit speed at periapsis (m/s) = 3305.19320027322\n",
"delta-v from capture orbit to dump orbit (m/s) = 1151.85799394513\n",
"required tip speed of capture sling = 1036.86561791447\n",
"mass ratio (counterbalance mass divided by payload mass) = 0.900167922925287\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (3920.46738022426, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (0.000000000000000, 3.17953534074921, 0.000000000000000)\n",
"Radial distance (km) = 3920.46738022426\n",
"Speed (km/s) = 3.17953534074921\n",
"Radial velocity (km/s) = 0.000000000000000\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 1.85846088569065\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 1.24652645876775e10)\n",
"Specific relative angular momentum (m^2/s) = 1.24652645876775e10\n",
"Specific orbital energy (kJ/kg) = -5869.57959959574\n",
"Eccentricity vector = (-0.0745912280035342, 0.000000000000000, 0.000000000000000)\n",
"Eccentricity = 0.0745912280035340 , Alt. calc. = 0.0745912280035342\n",
"Semi-latus rectum (km) = 3628.03490398553 , Alt. calc. = 3628.03490398553\n",
"Semi-minor axis (km) = 3638.17014020966\n",
"Semi-major axis (km) = 3648.33369011213\n",
"!!DANGER!! Periapsis is below surface by (km) = 19.7999999999972\n",
"Periapsis (km) = 3376.20000000000\n",
"Apoapsis altitude (km) = 524.467380224260\n",
"Apoapsis (km) = 3920.46738022426\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 180.000000000000 , Alt. calc. = 180.000000000000\n",
"Mean anomaly (degrees) = 180.000000000000\n",
"Eccentric anomaly (degrees) = 180.000000000000\n",
"True anomaly (degrees) = 180.000000000000 , Alt. calc. = 180.000000000000\n",
"Time since periapsis (hours) = 0.929230442845326\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n"
]
},
{
"data": {
"text/plain": [
"(3.64833369011213e6, 0.0745912280035340, 0.000000000000000, 3.14159265358979, 0.000000000000000, 3345.22959424317, 3.62803490398553e6)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print 'These are old, discarded calculations that predate most of the variables used above such as grapples and ballast, etc.'\n",
"print capture_frame_string\n",
"\n",
"#Consider throwing the counterbalance mass into an orbit with periapsis at the surface of Mars.\n",
"#This wastes the energy and momentum in the counterbalance mass and poses dangers to assets on the surface of Mars, but prevents the counterbalance mass from becoming a possible navigation hazard to spacecraft, slings or other orbital assets.\n",
"\n",
"a_dump = (periapsis_capture + polar_r_pl)/2 #Use polar radius as periapsis just to be sure it crashes.\n",
"v_dump = sqrt(mu*(2/periapsis_capture-1/a_dump))\n",
"delta_v_capture_to_dump = capture_speed_periapsis - v_dump\n",
"\n",
"print 'capture orbit speed at periapsis (m/s) =',capture_speed_periapsis\n",
"print 'dump orbit speed at periapsis (m/s) =',v_circ_at_periapsis\n",
"print 'delta-v from capture orbit to dump orbit (m/s) =',delta_v_capture_to_dump\n",
"print 'required tip speed of capture sling =',v_tip_capture_payload1\n",
"print 'mass ratio (counterbalance mass divided by payload mass) =',v_tip_capture_payload1/delta_v_capture_to_dump\n",
"\n",
"if v_tip_capture_payload1/delta_v_capture_to_dump > dmr:\n",
" print '!!! DANGER !!! mass ratio 2',v_tip_capture_payload1/delta_v_capture_to_dump,'is higher than the safe limit of',dmr,'!!!'\n",
"if v_tip_capture_payload1/delta_v_capture_to_dump < 1/dmr: print '!!! DANGER !!! mass ratio 2',v_tip_capture_payload1/delta_v_capture_to_dump,'is lower than the safe limit of',1/dmr,'!!!'\n",
"\n",
"p_i = com_cs_pos0 #Used to be vector([0,-periapsis_capture,0])\n",
"v_i = com_cs_vel0 - delta_v_capture_to_dump*com_cs_vel0/com_cs_vel0.norm() #Used to be vector([capture_speed_periapsis - delta_v_capture_to_dump,0,0])\n",
"kepler = xyz2kepler(p_i,v_i)\n",
"(kepler).n()"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"\n",
"## Appendix B: Indirect paths from the moon to the capture orbit. (Go back to the top)."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where the capture slings stay in the x-y (ecliptic) plane and Deimos is inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"capture orbit eccentricity = 0.717360803768281\n",
"cosine of capture orbit's true anomaly at the moon = -0.993985292993338\n",
"capture orbit's true anomaly at the moon (degrees) = 173.712720193813\n",
"cosine of capture orbit's flight path angle at the moon = 0.964506853303158\n",
"capture orbit's flight path angle at the moon = 15.3109755656732\n",
"delta v from moon directly into the non-inclined capture orbit = 657.867159072570\n",
"CHECK: delta-v directly into the non-inclined capture orbit (m/s) = 657.867159072570\n"
]
}
],
"source": [
"#Option 1 - throw capture orbit sling directly into the (non-inclined) capture orbit\n",
"print capture_frame_string\n",
"\n",
"#Capture orbit speed at the moon (either Phobos or Deimos)\n",
"capture_speed_moon = sqrt(mu*(2/a_moon-1/a_capture))\n",
"\n",
"#Capture orbit eccentricity\n",
"ecc = (apoapsis_capture - periapsis_capture) / (apoapsis_capture + periapsis_capture)\n",
"print 'capture orbit eccentricity =',ecc\n",
"\n",
"#Calculate cosine of capture orbit's true anomaly at the moon (either Phobos or Deimos).\n",
"var('r cos_ta') #Resets r and cos_ta so they're variables again rather than numbers.\n",
"eq2 = r == a*(1-ecc^2)/(1+ecc*cos_ta) # https://en.wikipedia.org/wiki/True_anomaly#Radius_from_true_anomaly\n",
"eq2\n",
"soln2 = solve(eq2.subs(r=a_moon,a=a_capture),cos_ta)\n",
"cos_ta = soln2[0].rhs() #Cosine of the true anomaly of the capture orbit at the moon.\n",
"print 'cosine of capture orbit\\'s true anomaly at the moon =',cos_ta.n()\n",
"print 'capture orbit\\'s true anomaly at the moon (degrees) =',(my_arccos(cos_ta,'ta')*180/pi).n()\n",
"\n",
"#Capture orbit's flight path angle at the moon.\n",
"var('cos_fpa') #Cosine of flight path angle.\n",
"cos_fpa = (1 + ecc*cos_ta)/sqrt(1+ecc^2+2*ecc*cos_ta) #cosine of flight path angle at the moon.\n",
"print 'cosine of capture orbit\\'s flight path angle at the moon =',cos_fpa\n",
"fpa = my_arccos(cos_fpa,'fpa')\n",
"print 'capture orbit\\'s flight path angle at the moon =',(fpa*180/pi).n()\n",
"\n",
"#Use cosine of flight path angle to calculate delta v from Phobos or Deimos directly into the (non-inclined) capture orbit.\n",
"delta_v_from_moon = sqrt(capture_speed_moon^2 + speed_moon^2 - 2*capture_speed_moon*speed_moon*cos_fpa)\n",
"print 'delta v from moon directly into the non-inclined capture orbit =',delta_v_from_moon\n",
"\n",
"#Check:\n",
"v_i = vector([speed_moon,0,0]).n()\n",
"v_f = rotateZ(my_arccos(cos_fpa,'fpa'))*vector([capture_speed_moon,0,0]).n()\n",
"v_i\n",
"v_f\n",
"delta_v = v_f - v_i\n",
"delta_v\n",
"print 'CHECK: delta-v directly into the non-inclined capture orbit (m/s) =',delta_v.norm()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"characteristic velocity (m/s) = 2726.88419920932\n",
"mass of sling on moon which tosses capture sling directly into (non-inclined) capture orbit (tons) = 0.121029033534396\n",
"!!!WARNING!!! THIS CALC PREDATES GRAPPLE_FRACTION SCALING WITH ACCELERATION! sum of masses of both slings (tons) = 0.508509438026693\n",
"fraction of original mass for direct launch from the moon = 0.296768881212361\n"
]
}
],
"source": [
"#Mass of sling on Phobos or Deimos which launches the capture sling directly into the (non-inclined) capture orbit.\n",
"mass_ratio_moon1 = tether_mass_ratio(delta_v_from_moon/v_c) #Use default characteristic velocity.\n",
"\n",
"print 'characteristic velocity (m/s) =',v_c #Characteristic velocity for a sling with specified tensile strength and density.\n",
"print 'mass of sling on moon which tosses capture sling directly into (non-inclined) capture orbit (tons) =',payload_mass/1000.0*mass_ratio_moon1.n()\n",
"print warnstring,'THIS CALC PREDATES GRAPPLE_FRACTION SCALING WITH ACCELERATION! sum of masses of both slings (tons) =',(payload_mass/1000.0*(1.0+grapple_fraction)*(mass_ratio_moon1+tether_mass_ratio(radii[1]*omegas[1]/v_cs[1][0]))).n()\n",
"print 'fraction of original mass for direct launch from the moon =',((mass_ratio_moon1+tether_mass_ratio(radii[1]*omegas[1]/v_cs[1][0]))/mass_ratio_direct_moon).n()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where Deimos stays in the x-y plane and the capture slings are inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"speed of moon = 1351.05227066087\n",
"temporary orbit speed at moon = 722.953336574843\n",
"delta v from moon = 628.098934086028\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (23463.2000000000, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (0.000000000000000, 0.722953336574843, 0.000000000000000)\n",
"Radial distance (km) = 23463.2000000000\n",
"Speed (km/s) = 0.722953336574843\n",
"Radial velocity (km/s) = 0.000000000000000\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 13.5115140611246\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 1.69627987267229e10)\n",
"Specific relative angular momentum (m^2/s) = 1.69627987267229e10\n",
"Specific orbital energy (kJ/kg) = -1564.01147462555\n",
"Eccentricity vector = (-0.713663818232359, 0.000000000000000, 0.000000000000000)\n",
"Eccentricity = 0.713663818232359 , Alt. calc. = 0.713663818232359\n",
"Semi-latus rectum (km) = 6718.36310005052 , Alt. calc. = 6718.36310005052\n",
"Semi-minor axis (km) = 9590.97024474989\n",
"Semi-major axis (km) = 13691.8336901121\n",
"Periapsis altitude (km) = 524.467380224260\n",
"Periapsis (km) = 3920.46738022426\n",
"Apoapsis altitude (km) = 20067.2000000000\n",
"Apoapsis (km) = 23463.2000000000\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 180.000000000000 , Alt. calc. = 180.000000000000\n",
"Mean anomaly (degrees) = 180.000000000000\n",
"Eccentric anomaly (degrees) = 180.000000000000\n",
"True anomaly (degrees) = 180.000000000000 , Alt. calc. = 180.000000000000\n",
"Time since periapsis (hours) = 6.75575703056229\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"characteristic velocity (m/s) = 2726.88419920932\n",
"!!!WARNING!!! THIS CALC PREDATES GRAPPLE_FRACTION SCALING WITH ACCELERATION! mass of sling on moon which tosses capture sling into temporary orbit (tons) = 0.137428797404502\n",
"temporary orbit speed at periapsis (m/s) = 4326.72869879931\n",
"capture orbit speed at periapsis (m/s) = 4331.39333469434\n",
"delta-v from temporary orbit to capture orbit (m/s) = 4.66463589502746\n",
"total delta-v from moon to (non-inclined) capture orbit (m/s) = 632.763569981055\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (3920.46738022426, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (0.000000000000000, 4.33139333469434, 0.000000000000000)\n",
"Radial distance (km) = 3920.46738022426\n",
"Speed (km/s) = 4.33139333469434\n",
"Radial velocity (km/s) = 0.000000000000000\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 13.7774796305327\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 1.69810862795899e10)\n",
"Specific relative angular momentum (m^2/s) = 1.69810862795899e10\n",
"Specific orbital energy (kJ/kg) = -1543.81798121506\n",
"Eccentricity vector = (0.717360803768281, 0.000000000000000, 0.000000000000000)\n",
"Eccentricity = 0.717360803768281 , Alt. calc. = 0.717360803768281\n",
"Semi-latus rectum (km) = 6732.85701124926 , Alt. calc. = 6732.85701124927\n",
"Semi-minor axis (km) = 9663.89991054697\n",
"Semi-major axis (km) = 13870.9260162561\n",
"Periapsis altitude (km) = 524.467380224261\n",
"Periapsis (km) = 3920.46738022426\n",
"Apoapsis altitude (km) = 20425.3846522879\n",
"Apoapsis (km) = 23821.3846522879\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Mean anomaly (degrees) = 0.000000000000000\n",
"Eccentric anomaly (degrees) = 0.000000000000000\n",
"True anomaly (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Time since periapsis (hours) = 0.000000000000000\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n"
]
},
{
"data": {
"text/plain": [
"(1.38709260162561e7, 0.717360803768281, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 6.73285701124926e6)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Option 2 - throw capture orbit sling into orbit with low periapsis but apoapsis at the moon, then raise apoapsis at periapsis.\n",
"#NOTE: This requires more total delta-v than raising apoapsis first, then lowering periapsis.\n",
"print moon_frame_string\n",
"\n",
"a_temp = (a_moon + periapsis_capture)/2\n",
"v_temp_at_moon = sqrt(mu*(2/a_moon-1/a_temp))\n",
"delta_v_moon_to_temp = speed_moon - v_temp_at_moon\n",
"\n",
"print 'speed of moon =',speed_moon\n",
"print 'temporary orbit speed at moon =',v_temp_at_moon\n",
"print 'delta v from moon =',delta_v_moon_to_temp\n",
"\n",
"p_i = p_moon0 #Used to be vector([0,-a_moon,0])\n",
"v_i = v_moon0 - delta_v_moon_to_temp*v_moon0/v_moon0.norm() #Used to be vector([speed_moon - delta_v_moon_to_temp,0,0])\n",
"kepler = xyz2kepler(p_i,v_i)\n",
"(kepler).n()\n",
"\n",
"#Mass of sling on Phobos or Deimos which launches the capture sling into the temporary orbit with no apoapsis change.\n",
"mass_ratio_sling2 = tether_mass_ratio(delta_v_moon_to_temp/v_c)\n",
"\n",
"print '\\ncharacteristic velocity (m/s) =',v_c #Characteristic velocity for a sling with specified tensile strength and density.\n",
"print warnstring,'THIS CALC PREDATES GRAPPLE_FRACTION SCALING WITH ACCELERATION! mass of sling on moon which tosses capture sling into temporary orbit (tons) =',payload_mass/1000.0*(1.0+grapple_fraction)*mass_ratio_sling2.n()\n",
"\n",
"#Necessary delta-v at periapsis to change temporary orbit into capture orbit.\n",
"v_temp_at_periapsis = sqrt(mu*(2/periapsis_capture-1/a_temp))\n",
"delta_v_temp_to_capture = capture_speed_periapsis - v_temp_at_periapsis\n",
"\n",
"print 'temporary orbit speed at periapsis (m/s) =',v_temp_at_periapsis\n",
"print 'capture orbit speed at periapsis (m/s) =',capture_speed_periapsis\n",
"print 'delta-v from temporary orbit to capture orbit (m/s) =',delta_v_temp_to_capture\n",
"print 'total delta-v from moon to (non-inclined) capture orbit (m/s) =',delta_v_moon_to_temp + delta_v_temp_to_capture\n",
"\n",
"p_i = com_cs_pos0 #Used to be vector([0,-periapsis_capture,0])\n",
"v_i = (v_temp_at_periapsis + delta_v_temp_to_capture)*com_cs_vel0/com_cs_vel0.norm() #Used to be vector([v_temp_at_periapsis + delta_v_temp_to_capture,0,0])\n",
"kepler = xyz2kepler(p_i,v_i)\n",
"(kepler).n()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final vectors in this cell are calculated in a frame where Deimos stays in the x-y plane and the capture slings are inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"speed of moon = 1351.05227066087\n",
"temporary orbit speed at moon = 1356.15978298029\n",
"delta v from moon = 5.10751231941663\n",
"escape delta v from moon = 559.624173982698\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (23463.2000000000, 0.000000000000000, 0.000000000000000)\n",
"Velocity vector (km/s) = (0.000000000000000, 1.35615978298029, 0.000000000000000)\n",
"Radial distance (km) = 23463.2000000000\n",
"Speed (km/s) = 1.35615978298029\n",
"Radial velocity (km/s) = 0.000000000000000\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 30.6581517081681\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 3.18198482200231e10)\n",
"Specific relative angular momentum (m^2/s) = 3.18198482200231e10\n",
"Specific orbital energy (kJ/kg) = -905.757559571325\n",
"Eccentricity vector = (0.00757508297729248, 0.000000000000000, 0.000000000000000)\n",
"Eccentricity = 0.00757508297729508 , Alt. calc. = 0.00757508297729248\n",
"Semi-latus rectum (km) = 23640.9356869128 , Alt. calc. = 23640.9356869128\n",
"Semi-minor axis (km) = 23641.6139967973\n",
"Semi-major axis (km) = 23642.2923261439\n",
"Periapsis altitude (km) = 20067.1999999999\n",
"Periapsis (km) = 23463.1999999999\n",
"Apoapsis altitude (km) = 20425.3846522880\n",
"Apoapsis (km) = 23821.3846522880\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Mean anomaly (degrees) = 0.000000000000000\n",
"Eccentric anomaly (degrees) = 0.000000000000000\n",
"True anomaly (degrees) = 0.000000000000000 , Alt. calc. = 0.0000476280213138561\n",
"Time since periapsis (hours) = 0.000000000000000\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n",
"\n",
"!!!WARNING!!! THIS CALC PREDATES GRAPPLE_FRACTION SCALING WITH ACCELERATION! mass of sling on moon which tosses capture sling into temporary orbit (tons) = 8.77054294039558e-6\n",
"temporary orbit speed at apoapsis (m/s) = 1335.76820510167\n",
"capture orbit speed at apoapsis (m/s) = 712.850513412914\n",
"circular orbit speed at apoapsis (m/s) = 1340.85640858803\n",
"delta-v from temporary orbit to (non-inclined) capture orbit (m/s) = 622.917691688756\n",
"total delta-v from moon to (non-inclined) capture orbit (m/s) = 628.025204008173\n",
"-------------------------------------------------------\n",
"Elliptical orbit: using true anomaly.\n",
"Position vector (km) = (-23821.3846522879, -0.000000000000000, -0.000000000000000)\n",
"Velocity vector (km/s) = (-0.000000000000000, -0.712850513412914, -0.000000000000000)\n",
"Radial distance (km) = 23821.3846522879\n",
"Speed (km/s) = 0.712850513412914\n",
"Radial velocity (km/s) = 0.000000000000000\n",
"Flight path angle (degrees) = 0.000000000000000\n",
"Orbital period (hours) = 13.7774796305327\n",
"Specific relative angular momentum vector = (0.000000000000000, 0.000000000000000, 1.69810862795899e10)\n",
"Specific relative angular momentum (m^2/s) = 1.69810862795899e10\n",
"Specific orbital energy (kJ/kg) = -1543.81798121507\n",
"Eccentricity vector = (0.717360803768281, 0.000000000000000, 0.000000000000000)\n",
"Eccentricity = 0.717360803768281 , Alt. calc. = 0.717360803768281\n",
"Semi-latus rectum (km) = 6732.85701124926 , Alt. calc. = 6732.85701124926\n",
"Semi-minor axis (km) = 9663.89991054696\n",
"Semi-major axis (km) = 13870.9260162561\n",
"Periapsis altitude (km) = 524.467380224262\n",
"Periapsis (km) = 3920.46738022426\n",
"Apoapsis altitude (km) = 20425.3846522879\n",
"Apoapsis (km) = 23821.3846522879\n",
"Inclination (degrees) = 0.000000000000000\n",
"Longitude of the ascending node (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Argument of periapsis (degrees) = 0.000000000000000 , Alt. calc. = 0.000000000000000\n",
"Mean anomaly (degrees) = 180.000000000000\n",
"Eccentric anomaly (degrees) = 180.000000000000\n",
"True anomaly (degrees) = 180.000000000000 , Alt. calc. = 180.000000000000\n",
"Time since periapsis (hours) = 6.88873981526634\n",
"returns [a, ecc, inc, AP, LAN2, tper, p]\n"
]
},
{
"data": {
"text/plain": [
"(1.38709260162561e7, 0.717360803768281, 0.000000000000000, 0.000000000000000, 0.000000000000000, 24799.4633349588, 6.73285701124926e6)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Option 3 - throw capture orbit sling into orbit with high apoapsis but periapsis at the moon, then lower periapsis at apoapsis.\n",
"#Similar to the reverse of fig 9 here: http://www.csc.caltech.edu/references/Hopkins-Phobos-Deimos-Paper.pdf\n",
"print moon_frame_string\n",
"\n",
"a_temp = (a_moon + apoapsis_capture)/2\n",
"v_temp_at_moon = sqrt(mu*(2/a_moon-1/a_temp))\n",
"delta_v_moon_to_temp = v_temp_at_moon - speed_moon\n",
"\n",
"print 'speed of moon =',speed_moon\n",
"print 'temporary orbit speed at moon =',v_temp_at_moon\n",
"print 'delta v from moon =',delta_v_moon_to_temp\n",
"\n",
"#Compare that delta-v to the delta-v needed to enter a parabolic trajectory.\n",
"v_escape_at_moon = sqrt(mu*(2/a_moon))\n",
"print 'escape delta v from moon =',v_escape_at_moon - speed_moon\n",
"\n",
"p_i = p_moon0 #Used to be vector([0,-a_moon,0])\n",
"v_i = v_moon0 + delta_v_moon_to_temp*v_moon0/v_moon0.norm() #Used to be vector([speed_moon + delta_v_moon_to_temp,0,0])\n",
"kepler = xyz2kepler(p_i,v_i)\n",
"(kepler).n()\n",
"\n",
"#Mass of sling on Phobos or Deimos which launches the capture sling into the temporary orbit with no periapsis change.\n",
"mass_ratio_sling2 = tether_mass_ratio(delta_v_moon_to_temp/v_c)\n",
"\n",
"print '\\n',warnstring,'THIS CALC PREDATES GRAPPLE_FRACTION SCALING WITH ACCELERATION! mass of sling on moon which tosses capture sling into temporary orbit (tons) =',payload_mass/1000.0*(1.0+grapple_fraction)*mass_ratio_sling2.n()\n",
"\n",
"#Necessary delta-v at apoapsis to change temporary orbit into capture orbit.\n",
"v_temp_at_apoapsis = sqrt(mu*(2/apoapsis_capture-1/a_temp))\n",
"delta_v_temp_to_capture = v_temp_at_apoapsis - capture_speed_apoapsis\n",
"apoapsis_capture_circ_speed = sqrt(mu/apoapsis_capture)\n",
"\n",
"print 'temporary orbit speed at apoapsis (m/s) =',v_temp_at_apoapsis\n",
"print 'capture orbit speed at apoapsis (m/s) =',capture_speed_apoapsis\n",
"print 'circular orbit speed at apoapsis (m/s) =',apoapsis_capture_circ_speed\n",
"print 'delta-v from temporary orbit to (non-inclined) capture orbit (m/s) =',delta_v_temp_to_capture\n",
"print 'total delta-v from moon to (non-inclined) capture orbit (m/s) =',delta_v_moon_to_temp + delta_v_temp_to_capture\n",
"\n",
"p_i = -apoapsis_capture*com_cs_pos0/com_cs_pos0.norm() #Used to be vector([0,-apoapsis_capture,0])\n",
"v_i = -(v_temp_at_apoapsis - delta_v_temp_to_capture)*com_cs_vel0/com_cs_vel0.norm() #Used to be vector([v_temp_at_apoapsis - delta_v_temp_to_capture,0,0])\n",
"kepler = xyz2kepler(p_i,v_i)\n",
"(kepler).n()"
]
},
{
"cell_type": "code",
"execution_count": 44,
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{
"name": "stdout",
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"text": [
"Final vectors in this cell are calculated in a frame where Deimos stays in the x-y plane and the capture slings are inclined by 27.58 degrees with a longitude of ascending node arbitrarily set to 0.0000 degrees.\n",
"\n",
"delta-v to change inclination of capture orbit (m/s) = 339.835741161220\n",
"Calculate both burns (lowering periapsis and changing inclination) performed at once.\n",
"delta-v to lower periapsis AND change inclination of capture orbit (m/s) = 777.453001392517\n",
"angle of that maneuver (degrees away from chosen moon's plane) = -25.1199536970472\n",
"\n",
"Calculate both burns (circularizing and changing inclination) performed at once.\n",
"delta-v to circularize AND change inclination of capture orbit (m/s) = 638.029976179054\n",
"angle of that maneuver (degrees away from chosen moon's plane) = -76.6537601069899\n"
]
}
],
"source": [
"#Inclination change - Phobos and Deimos are inclined 26.04 and 27.58 degrees away from the ecliptic plane, respectively.\n",
"print moon_frame_string\n",
"\n",
"#Circular orbit approximation:\n",
"delta_v_inclination = (2*capture_speed_apoapsis*sin(inc_moon/2)).n()\n",
"print 'delta-v to change inclination of capture orbit (m/s) =',delta_v_inclination\n",
"\n",
"print 'Calculate both burns (lowering periapsis and changing inclination) performed at once.'\n",
"v_i = vector([v_temp_at_apoapsis,0,0]).n()\n",
"v_f = vector([capture_speed_apoapsis*cos(inc_moon),capture_speed_apoapsis*sin(inc_moon),0]).n()\n",
"delta_v = v_f - v_i\n",
"delta_v\n",
"print 'delta-v to lower periapsis AND change inclination of capture orbit (m/s) =',delta_v.norm()\n",
"angle = (arctan(delta_v[1]/delta_v[0])*180/pi).n()\n",
"print 'angle of that maneuver (degrees away from chosen moon\\'s plane) =',angle\n",
"\n",
"print ''\n",
"print 'Calculate both burns (circularizing and changing inclination) performed at once.'\n",
"v_i2 = vector([v_temp_at_apoapsis,0,0]).n()\n",
"v_f2 = vector([apoapsis_capture_circ_speed*cos(inc_moon),apoapsis_capture_circ_speed*sin(inc_moon),0]).n()\n",
"delta_v2 = v_f2 - v_i2\n",
"delta_v2\n",
"print 'delta-v to circularize AND change inclination of capture orbit (m/s) =',delta_v2.norm()\n",
"angle2 = (arctan(delta_v2[1]/delta_v2[0])*180/pi).n()\n",
"print 'angle of that maneuver (degrees away from chosen moon\\'s plane) =',angle2"
]
}
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