Section 08 - Performance Analysis - D - 5X Lower atm uncertainty
[10]:
from AMAT.planet import Planet
from AMAT.vehicle import Vehicle
[11]:
import numpy as np
import matplotlib.pyplot as plt
[3]:
planet = Planet('URANUS')
planet.loadAtmosphereModel('../../../atmdata/Uranus/uranus-gram-avg.dat', 0 , 1 ,2, 3, heightInKmFlag=True)
planet.h_skip = 1000.0E3
planet.h_low = 120e3
planet.h_trap = 100e3
[12]:
vehicle=Vehicle('Titania', 3200.0, 146 , 0.24, np.pi*4.5**2.0, 0.0, 1.125, planet)
vehicle.setInitialState(1000.0,-15.22,75.55,29.2877,88.687,-11.7 ,0.0,0.0)
vehicle.setSolverParams(1E-6)
# Set the guidance parameters described in the paper.
# See the function description for parameter details.
# Set max roll rate constraint to 30 deg/s
vehicle.setMaxRollRate(30.0)
# Set Ghdot = 83
# Set Gq = 9.0
# Set v_switch_kms = 16.0 :: this parameter is not really used, and should just be set to low value
# Set low_Alt_km = 120
# Set numPoints_lowAlt = 101
# Set hdot_threshold = -500 m/s
vehicle.setEquilibriumGlideParams(83.0, 9.0, 16.0, 120.0, 101, -500.0)
# Set target orbit parameters
# periapsis = 4000.0 km
# apoapsis = 550,000 km :: set slightly above target (500e3) to account for bias in targeting algorithm
# apoapsis tolerance = 10 km
vehicle.setTargetOrbitParams(4000.0, 550.0E3, 10.0E3)
[13]:
# Set path to atmfiles with randomly perturbed atmosphere files.
atmfiles = ['../../../atmdata/Uranus/perturbed/G-01.txt',
'../../../atmdata/Uranus/perturbed/G-02.txt',
'../../../atmdata/Uranus/perturbed/G-03.txt',
'../../../atmdata/Uranus/perturbed/G-04.txt',
'../../../atmdata/Uranus/perturbed/G-05.txt']
[14]:
vehicle.setupMonteCarloSimulation(1201, 200, atmfiles, 0, 1, 2, 3, 4, True, \
-11.70, 0.10, 0.24, 0.013, 0.5, 0.1, 2400.0, atmSigmaFactor=0.20)
Small run N = 200
[15]:
vehicle.runMonteCarlo2(200, '../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD')
RUN #: 1, SAMPLE #: 99, EFPA: -11.74, SIGMA: 0.24, LD: 0.24, APO : 514282.65
RUN #: 2, SAMPLE #: 19, EFPA: -11.84, SIGMA: -0.36, LD: 0.22, APO : 527567.71
RUN #: 3, SAMPLE #: 63, EFPA: -11.64, SIGMA: -0.26, LD: 0.23, APO : 570864.29
RUN #: 4, SAMPLE #: 7, EFPA: -11.70, SIGMA: -0.02, LD: 0.25, APO : 515127.34
RUN #: 5, SAMPLE #: 8, EFPA: -11.95, SIGMA: -0.15, LD: 0.25, APO : 549124.79
RUN #: 6, SAMPLE #: 147, EFPA: -11.65, SIGMA: 0.24, LD: 0.25, APO : 545559.86
RUN #: 7, SAMPLE #: 4, EFPA: -11.81, SIGMA: -0.20, LD: 0.22, APO : 530742.60
RUN #: 8, SAMPLE #: 60, EFPA: -11.65, SIGMA: -0.16, LD: 0.25, APO : 548605.84
RUN #: 9, SAMPLE #: 98, EFPA: -11.61, SIGMA: -0.31, LD: 0.23, APO : 598446.91
RUN #: 10, SAMPLE #: 179, EFPA: -11.65, SIGMA: -0.03, LD: 0.25, APO : 531457.60
RUN #: 11, SAMPLE #: 159, EFPA: -11.48, SIGMA: 0.30, LD: 0.24, APO : 559304.34
RUN #: 12, SAMPLE #: 108, EFPA: -11.74, SIGMA: -0.15, LD: 0.24, APO : 520850.57
RUN #: 13, SAMPLE #: 145, EFPA: -11.81, SIGMA: 0.14, LD: 0.24, APO : 579002.85
RUN #: 14, SAMPLE #: 4, EFPA: -11.72, SIGMA: -0.23, LD: 0.27, APO : 541028.46
RUN #: 15, SAMPLE #: 5, EFPA: -11.53, SIGMA: 0.15, LD: 0.24, APO : 534233.87
RUN #: 16, SAMPLE #: 12, EFPA: -11.69, SIGMA: -0.14, LD: 0.24, APO : 487107.27
RUN #: 17, SAMPLE #: 125, EFPA: -11.74, SIGMA: 0.16, LD: 0.23, APO : 491739.98
RUN #: 18, SAMPLE #: 4, EFPA: -11.55, SIGMA: -0.04, LD: 0.23, APO : 511948.51
RUN #: 19, SAMPLE #: 146, EFPA: -11.66, SIGMA: -0.36, LD: 0.26, APO : 474139.37
RUN #: 20, SAMPLE #: 123, EFPA: -11.66, SIGMA: 0.01, LD: 0.24, APO : 510439.95
RUN #: 21, SAMPLE #: 102, EFPA: -11.77, SIGMA: -0.05, LD: 0.25, APO : 515974.03
RUN #: 22, SAMPLE #: 121, EFPA: -11.57, SIGMA: 0.01, LD: 0.24, APO : 547248.91
RUN #: 23, SAMPLE #: 83, EFPA: -11.67, SIGMA: 0.16, LD: 0.24, APO : 545805.02
RUN #: 24, SAMPLE #: 72, EFPA: -11.79, SIGMA: 0.04, LD: 0.23, APO : 513798.31
RUN #: 25, SAMPLE #: 79, EFPA: -11.61, SIGMA: -0.00, LD: 0.26, APO : 529806.65
RUN #: 26, SAMPLE #: 165, EFPA: -11.63, SIGMA: 0.02, LD: 0.24, APO : 537662.80
RUN #: 27, SAMPLE #: 19, EFPA: -11.72, SIGMA: -0.28, LD: 0.23, APO : 471795.95
RUN #: 28, SAMPLE #: 108, EFPA: -11.65, SIGMA: 0.12, LD: 0.24, APO : 534755.04
RUN #: 29, SAMPLE #: 183, EFPA: -11.85, SIGMA: -0.03, LD: 0.25, APO : 554202.85
RUN #: 30, SAMPLE #: 41, EFPA: -11.61, SIGMA: 0.13, LD: 0.23, APO : 539022.64
RUN #: 31, SAMPLE #: 52, EFPA: -11.68, SIGMA: -0.33, LD: 0.25, APO : 536556.23
RUN #: 32, SAMPLE #: 199, EFPA: -11.65, SIGMA: 0.23, LD: 0.25, APO : 553440.97
RUN #: 33, SAMPLE #: 122, EFPA: -11.69, SIGMA: 0.19, LD: 0.25, APO : 506181.62
RUN #: 34, SAMPLE #: 18, EFPA: -11.74, SIGMA: -0.04, LD: 0.25, APO : 493782.44
RUN #: 35, SAMPLE #: 10, EFPA: -11.79, SIGMA: -0.09, LD: 0.25, APO : 523719.47
RUN #: 36, SAMPLE #: 175, EFPA: -11.56, SIGMA: 0.26, LD: 0.23, APO : 503775.96
RUN #: 37, SAMPLE #: 162, EFPA: -11.69, SIGMA: -0.25, LD: 0.23, APO : 489752.32
RUN #: 38, SAMPLE #: 197, EFPA: -11.79, SIGMA: 0.21, LD: 0.23, APO : 568741.12
RUN #: 39, SAMPLE #: 18, EFPA: -12.00, SIGMA: -0.19, LD: 0.24, APO : 513416.86
RUN #: 40, SAMPLE #: 15, EFPA: -11.59, SIGMA: -0.13, LD: 0.24, APO : 597206.47
RUN #: 41, SAMPLE #: 192, EFPA: -11.54, SIGMA: -0.02, LD: 0.25, APO : 488663.29
RUN #: 42, SAMPLE #: 165, EFPA: -11.76, SIGMA: -0.37, LD: 0.23, APO : 529303.49
RUN #: 43, SAMPLE #: 173, EFPA: -11.73, SIGMA: -0.13, LD: 0.24, APO : 514870.36
RUN #: 44, SAMPLE #: 168, EFPA: -11.68, SIGMA: 0.22, LD: 0.22, APO : 576507.79
RUN #: 45, SAMPLE #: 176, EFPA: -11.57, SIGMA: 0.01, LD: 0.23, APO : 562147.48
RUN #: 46, SAMPLE #: 96, EFPA: -11.62, SIGMA: 0.26, LD: 0.25, APO : 526386.76
RUN #: 47, SAMPLE #: 154, EFPA: -11.60, SIGMA: -0.24, LD: 0.25, APO : 517134.59
RUN #: 48, SAMPLE #: 123, EFPA: -11.67, SIGMA: 0.32, LD: 0.25, APO : 504999.10
RUN #: 49, SAMPLE #: 10, EFPA: -11.60, SIGMA: -0.19, LD: 0.23, APO : 497315.88
RUN #: 50, SAMPLE #: 4, EFPA: -11.90, SIGMA: -0.34, LD: 0.24, APO : 539655.68
RUN #: 51, SAMPLE #: 158, EFPA: -11.74, SIGMA: -0.10, LD: 0.25, APO : 472195.43
RUN #: 52, SAMPLE #: 63, EFPA: -11.74, SIGMA: 0.17, LD: 0.25, APO : 506773.32
RUN #: 53, SAMPLE #: 139, EFPA: -11.55, SIGMA: -0.17, LD: 0.21, APO : 553604.75
RUN #: 54, SAMPLE #: 75, EFPA: -11.64, SIGMA: 0.10, LD: 0.24, APO : 487564.32
RUN #: 55, SAMPLE #: 140, EFPA: -11.86, SIGMA: -0.00, LD: 0.23, APO : 527326.75
RUN #: 56, SAMPLE #: 158, EFPA: -11.81, SIGMA: 0.34, LD: 0.22, APO : 517620.22
RUN #: 57, SAMPLE #: 8, EFPA: -11.71, SIGMA: 0.02, LD: 0.24, APO : 554479.56
RUN #: 58, SAMPLE #: 131, EFPA: -11.72, SIGMA: -0.01, LD: 0.25, APO : 521475.48
RUN #: 59, SAMPLE #: 90, EFPA: -11.74, SIGMA: 0.18, LD: 0.24, APO : 531602.05
RUN #: 60, SAMPLE #: 21, EFPA: -11.75, SIGMA: 0.12, LD: 0.24, APO : 556677.47
RUN #: 61, SAMPLE #: 40, EFPA: -11.70, SIGMA: 0.08, LD: 0.23, APO : 508162.91
RUN #: 62, SAMPLE #: 12, EFPA: -11.81, SIGMA: -0.14, LD: 0.23, APO : 539471.79
RUN #: 63, SAMPLE #: 172, EFPA: -11.54, SIGMA: -0.14, LD: 0.25, APO : 535894.61
RUN #: 64, SAMPLE #: 148, EFPA: -11.57, SIGMA: -0.13, LD: 0.24, APO : 488131.05
RUN #: 65, SAMPLE #: 81, EFPA: -11.64, SIGMA: -0.21, LD: 0.24, APO : 569669.10
RUN #: 66, SAMPLE #: 50, EFPA: -11.80, SIGMA: 0.08, LD: 0.24, APO : 551286.21
RUN #: 67, SAMPLE #: 77, EFPA: -11.71, SIGMA: -0.45, LD: 0.22, APO : 499383.35
RUN #: 68, SAMPLE #: 195, EFPA: -11.66, SIGMA: -0.13, LD: 0.23, APO : 543671.65
RUN #: 69, SAMPLE #: 93, EFPA: -11.88, SIGMA: 0.47, LD: 0.24, APO : 559472.75
RUN #: 70, SAMPLE #: 125, EFPA: -11.64, SIGMA: 0.09, LD: 0.23, APO : 531047.21
RUN #: 71, SAMPLE #: 126, EFPA: -11.67, SIGMA: -0.03, LD: 0.25, APO : 574187.25
RUN #: 72, SAMPLE #: 160, EFPA: -11.83, SIGMA: 0.07, LD: 0.27, APO : 497795.45
RUN #: 73, SAMPLE #: 175, EFPA: -11.63, SIGMA: -0.05, LD: 0.24, APO : 478618.69
RUN #: 74, SAMPLE #: 97, EFPA: -11.62, SIGMA: 0.08, LD: 0.24, APO : 570896.28
RUN #: 75, SAMPLE #: 194, EFPA: -11.76, SIGMA: 0.06, LD: 0.24, APO : 494400.57
RUN #: 76, SAMPLE #: 57, EFPA: -11.78, SIGMA: 0.33, LD: 0.25, APO : 500527.31
RUN #: 77, SAMPLE #: 45, EFPA: -11.70, SIGMA: 0.11, LD: 0.25, APO : 538203.49
RUN #: 78, SAMPLE #: 143, EFPA: -11.77, SIGMA: 0.05, LD: 0.23, APO : 520796.14
RUN #: 79, SAMPLE #: 164, EFPA: -11.59, SIGMA: -0.28, LD: 0.26, APO : 551268.12
RUN #: 80, SAMPLE #: 156, EFPA: -11.74, SIGMA: -0.16, LD: 0.23, APO : 497619.87
RUN #: 81, SAMPLE #: 39, EFPA: -11.62, SIGMA: -0.16, LD: 0.23, APO : 498815.85
RUN #: 82, SAMPLE #: 189, EFPA: -11.71, SIGMA: 0.18, LD: 0.24, APO : 529105.26
RUN #: 83, SAMPLE #: 44, EFPA: -11.69, SIGMA: -0.51, LD: 0.24, APO : 521975.12
RUN #: 84, SAMPLE #: 81, EFPA: -11.68, SIGMA: 0.00, LD: 0.24, APO : 553596.11
RUN #: 85, SAMPLE #: 198, EFPA: -11.69, SIGMA: -0.17, LD: 0.24, APO : 546093.80
RUN #: 86, SAMPLE #: 75, EFPA: -11.66, SIGMA: 0.00, LD: 0.21, APO : 591016.13
RUN #: 87, SAMPLE #: 62, EFPA: -11.93, SIGMA: -0.10, LD: 0.24, APO : 509990.10
RUN #: 88, SAMPLE #: 23, EFPA: -11.71, SIGMA: -0.03, LD: 0.24, APO : 478278.90
RUN #: 89, SAMPLE #: 185, EFPA: -11.85, SIGMA: -0.15, LD: 0.22, APO : 536186.01
RUN #: 90, SAMPLE #: 116, EFPA: -11.62, SIGMA: -0.46, LD: 0.22, APO : 519430.32
RUN #: 91, SAMPLE #: 180, EFPA: -11.82, SIGMA: 0.17, LD: 0.26, APO : 531103.10
RUN #: 92, SAMPLE #: 92, EFPA: -11.63, SIGMA: -0.18, LD: 0.26, APO : 515854.19
RUN #: 93, SAMPLE #: 110, EFPA: -11.76, SIGMA: -0.06, LD: 0.24, APO : 545347.45
RUN #: 94, SAMPLE #: 136, EFPA: -11.72, SIGMA: -0.09, LD: 0.25, APO : 526877.07
RUN #: 95, SAMPLE #: 124, EFPA: -11.69, SIGMA: -0.10, LD: 0.24, APO : 475372.65
RUN #: 96, SAMPLE #: 120, EFPA: -11.69, SIGMA: 0.14, LD: 0.26, APO : 561497.34
RUN #: 97, SAMPLE #: 57, EFPA: -11.61, SIGMA: 0.10, LD: 0.24, APO : 500049.61
RUN #: 98, SAMPLE #: 15, EFPA: -11.73, SIGMA: -0.05, LD: 0.27, APO : 531419.67
RUN #: 99, SAMPLE #: 157, EFPA: -11.79, SIGMA: -0.27, LD: 0.24, APO : 497580.38
RUN #: 100, SAMPLE #: 46, EFPA: -11.77, SIGMA: 0.03, LD: 0.24, APO : 581221.95
RUN #: 101, SAMPLE #: 26, EFPA: -11.67, SIGMA: 0.12, LD: 0.24, APO : 494456.27
RUN #: 102, SAMPLE #: 2, EFPA: -11.80, SIGMA: 0.04, LD: 0.23, APO : 513269.11
RUN #: 103, SAMPLE #: 190, EFPA: -11.74, SIGMA: -0.15, LD: 0.24, APO : 594832.92
RUN #: 104, SAMPLE #: 181, EFPA: -11.61, SIGMA: 0.11, LD: 0.24, APO : 546465.13
RUN #: 105, SAMPLE #: 25, EFPA: -11.58, SIGMA: -0.31, LD: 0.24, APO : 509099.63
RUN #: 106, SAMPLE #: 189, EFPA: -11.73, SIGMA: -0.37, LD: 0.24, APO : 551971.90
RUN #: 107, SAMPLE #: 92, EFPA: -11.58, SIGMA: 0.10, LD: 0.24, APO : 515073.19
RUN #: 108, SAMPLE #: 65, EFPA: -11.67, SIGMA: 0.21, LD: 0.26, APO : 529090.20
RUN #: 109, SAMPLE #: 176, EFPA: -11.54, SIGMA: 0.01, LD: 0.23, APO : 527371.53
RUN #: 110, SAMPLE #: 135, EFPA: -11.79, SIGMA: -0.02, LD: 0.23, APO : 591233.63
RUN #: 111, SAMPLE #: 51, EFPA: -11.71, SIGMA: 0.20, LD: 0.23, APO : 539993.72
RUN #: 112, SAMPLE #: 43, EFPA: -11.69, SIGMA: -0.04, LD: 0.24, APO : 557435.50
RUN #: 113, SAMPLE #: 115, EFPA: -11.69, SIGMA: 0.17, LD: 0.24, APO : 491406.73
RUN #: 114, SAMPLE #: 54, EFPA: -11.69, SIGMA: -0.27, LD: 0.24, APO : 534599.91
RUN #: 115, SAMPLE #: 54, EFPA: -11.76, SIGMA: -0.38, LD: 0.24, APO : 507677.15
RUN #: 116, SAMPLE #: 67, EFPA: -11.72, SIGMA: -0.09, LD: 0.24, APO : 590278.56
RUN #: 117, SAMPLE #: 86, EFPA: -11.63, SIGMA: 0.10, LD: 0.25, APO : 576631.08
RUN #: 118, SAMPLE #: 31, EFPA: -11.79, SIGMA: 0.06, LD: 0.24, APO : 526059.61
RUN #: 119, SAMPLE #: 40, EFPA: -11.71, SIGMA: 0.02, LD: 0.24, APO : 551787.15
RUN #: 120, SAMPLE #: 134, EFPA: -11.80, SIGMA: -0.19, LD: 0.23, APO : 515938.99
RUN #: 121, SAMPLE #: 161, EFPA: -11.65, SIGMA: 0.11, LD: 0.25, APO : 520572.74
RUN #: 122, SAMPLE #: 120, EFPA: -11.69, SIGMA: -0.02, LD: 0.23, APO : 495048.40
RUN #: 123, SAMPLE #: 115, EFPA: -11.70, SIGMA: 0.06, LD: 0.25, APO : 522407.91
RUN #: 124, SAMPLE #: 34, EFPA: -11.68, SIGMA: -0.19, LD: 0.25, APO : 472035.82
RUN #: 125, SAMPLE #: 86, EFPA: -11.64, SIGMA: -0.10, LD: 0.25, APO : 504280.34
RUN #: 126, SAMPLE #: 189, EFPA: -11.62, SIGMA: -0.17, LD: 0.26, APO : 535594.01
RUN #: 127, SAMPLE #: 89, EFPA: -11.84, SIGMA: 0.22, LD: 0.24, APO : 481660.11
RUN #: 128, SAMPLE #: 97, EFPA: -11.78, SIGMA: 0.10, LD: 0.25, APO : 543471.19
RUN #: 129, SAMPLE #: 91, EFPA: -11.55, SIGMA: -0.45, LD: 0.24, APO : 569628.23
RUN #: 130, SAMPLE #: 102, EFPA: -11.86, SIGMA: 0.25, LD: 0.25, APO : 506779.54
RUN #: 131, SAMPLE #: 68, EFPA: -11.66, SIGMA: 0.13, LD: 0.24, APO : 569248.50
RUN #: 132, SAMPLE #: 179, EFPA: -11.78, SIGMA: -0.18, LD: 0.26, APO : 557113.99
RUN #: 133, SAMPLE #: 112, EFPA: -11.75, SIGMA: -0.08, LD: 0.23, APO : 496289.75
RUN #: 134, SAMPLE #: 50, EFPA: -11.83, SIGMA: -0.27, LD: 0.23, APO : 529924.94
RUN #: 135, SAMPLE #: 28, EFPA: -11.41, SIGMA: 0.03, LD: 0.26, APO : 483371.72
RUN #: 136, SAMPLE #: 154, EFPA: -11.75, SIGMA: 0.30, LD: 0.25, APO : 461171.99
RUN #: 137, SAMPLE #: 195, EFPA: -11.70, SIGMA: -0.01, LD: 0.22, APO : 547726.46
RUN #: 138, SAMPLE #: 71, EFPA: -11.90, SIGMA: 0.21, LD: 0.23, APO : 493344.72
RUN #: 139, SAMPLE #: 135, EFPA: -11.75, SIGMA: 0.00, LD: 0.24, APO : 530890.16
RUN #: 140, SAMPLE #: 167, EFPA: -11.74, SIGMA: -0.06, LD: 0.25, APO : 539678.23
RUN #: 141, SAMPLE #: 26, EFPA: -11.67, SIGMA: -0.17, LD: 0.24, APO : 555315.63
RUN #: 142, SAMPLE #: 54, EFPA: -11.79, SIGMA: 0.24, LD: 0.25, APO : 526216.45
RUN #: 143, SAMPLE #: 62, EFPA: -11.69, SIGMA: -0.03, LD: 0.21, APO : 551104.09
RUN #: 144, SAMPLE #: 144, EFPA: -11.71, SIGMA: 0.01, LD: 0.26, APO : 575564.72
RUN #: 145, SAMPLE #: 13, EFPA: -11.83, SIGMA: 0.33, LD: 0.24, APO : 492731.00
RUN #: 146, SAMPLE #: 91, EFPA: -11.61, SIGMA: 0.04, LD: 0.25, APO : 521115.67
RUN #: 147, SAMPLE #: 183, EFPA: -11.61, SIGMA: -0.04, LD: 0.22, APO : 496073.00
RUN #: 148, SAMPLE #: 50, EFPA: -11.79, SIGMA: 0.04, LD: 0.25, APO : 545364.16
RUN #: 149, SAMPLE #: 184, EFPA: -11.69, SIGMA: 0.18, LD: 0.24, APO : 559243.22
RUN #: 150, SAMPLE #: 183, EFPA: -11.62, SIGMA: -0.21, LD: 0.25, APO : 525042.96
RUN #: 151, SAMPLE #: 148, EFPA: -11.65, SIGMA: 0.15, LD: 0.25, APO : 494740.62
RUN #: 152, SAMPLE #: 20, EFPA: -11.61, SIGMA: -0.17, LD: 0.24, APO : 547846.56
RUN #: 153, SAMPLE #: 147, EFPA: -11.70, SIGMA: -0.19, LD: 0.26, APO : 499458.33
RUN #: 154, SAMPLE #: 134, EFPA: -11.67, SIGMA: -0.10, LD: 0.24, APO : 560478.52
RUN #: 155, SAMPLE #: 62, EFPA: -11.66, SIGMA: -0.20, LD: 0.23, APO : 550900.69
RUN #: 156, SAMPLE #: 186, EFPA: -11.80, SIGMA: 0.39, LD: 0.25, APO : 536764.85
RUN #: 157, SAMPLE #: 122, EFPA: -11.79, SIGMA: 0.09, LD: 0.22, APO : 488050.41
RUN #: 158, SAMPLE #: 157, EFPA: -11.76, SIGMA: 0.14, LD: 0.25, APO : 562608.53
RUN #: 159, SAMPLE #: 45, EFPA: -11.50, SIGMA: -0.44, LD: 0.25, APO : 524761.85
RUN #: 160, SAMPLE #: 138, EFPA: -11.68, SIGMA: -0.11, LD: 0.23, APO : 514741.46
RUN #: 161, SAMPLE #: 106, EFPA: -11.62, SIGMA: 0.24, LD: 0.25, APO : 561136.14
RUN #: 162, SAMPLE #: 8, EFPA: -11.83, SIGMA: 0.05, LD: 0.24, APO : 523420.70
RUN #: 163, SAMPLE #: 13, EFPA: -11.56, SIGMA: -0.29, LD: 0.24, APO : 561305.04
RUN #: 164, SAMPLE #: 196, EFPA: -11.66, SIGMA: -0.08, LD: 0.25, APO : 528206.34
RUN #: 165, SAMPLE #: 31, EFPA: -11.84, SIGMA: -0.23, LD: 0.23, APO : 543614.09
RUN #: 166, SAMPLE #: 82, EFPA: -11.76, SIGMA: -0.18, LD: 0.27, APO : 498872.56
RUN #: 167, SAMPLE #: 159, EFPA: -11.64, SIGMA: -0.04, LD: 0.21, APO : 540300.12
RUN #: 168, SAMPLE #: 185, EFPA: -11.93, SIGMA: -0.07, LD: 0.23, APO : 572351.43
RUN #: 169, SAMPLE #: 79, EFPA: -11.75, SIGMA: -0.33, LD: 0.25, APO : 531967.51
RUN #: 170, SAMPLE #: 32, EFPA: -11.71, SIGMA: 0.08, LD: 0.22, APO : 587888.53
RUN #: 171, SAMPLE #: 39, EFPA: -11.82, SIGMA: 0.04, LD: 0.24, APO : 537292.66
RUN #: 172, SAMPLE #: 96, EFPA: -11.82, SIGMA: 0.08, LD: 0.24, APO : 557135.61
RUN #: 173, SAMPLE #: 165, EFPA: -11.76, SIGMA: 0.16, LD: 0.24, APO : 561592.87
RUN #: 174, SAMPLE #: 14, EFPA: -11.71, SIGMA: -0.10, LD: 0.24, APO : 571666.44
RUN #: 175, SAMPLE #: 44, EFPA: -11.83, SIGMA: -0.04, LD: 0.25, APO : 529701.28
RUN #: 176, SAMPLE #: 118, EFPA: -11.57, SIGMA: 0.06, LD: 0.23, APO : 540164.92
RUN #: 177, SAMPLE #: 182, EFPA: -11.65, SIGMA: 0.00, LD: 0.25, APO : 513947.10
RUN #: 178, SAMPLE #: 33, EFPA: -11.75, SIGMA: -0.11, LD: 0.25, APO : 544113.07
RUN #: 179, SAMPLE #: 107, EFPA: -11.73, SIGMA: -0.25, LD: 0.25, APO : 557206.72
RUN #: 180, SAMPLE #: 8, EFPA: -11.59, SIGMA: 0.07, LD: 0.24, APO : 473081.31
RUN #: 181, SAMPLE #: 7, EFPA: -11.64, SIGMA: -0.10, LD: 0.26, APO : 542501.05
RUN #: 182, SAMPLE #: 110, EFPA: -11.54, SIGMA: 0.11, LD: 0.24, APO : 516666.71
RUN #: 183, SAMPLE #: 19, EFPA: -11.53, SIGMA: -0.04, LD: 0.24, APO : 525676.64
RUN #: 184, SAMPLE #: 159, EFPA: -11.59, SIGMA: -0.29, LD: 0.25, APO : 465039.02
RUN #: 185, SAMPLE #: 162, EFPA: -11.68, SIGMA: 0.03, LD: 0.24, APO : 489552.72
RUN #: 186, SAMPLE #: 16, EFPA: -11.62, SIGMA: 0.14, LD: 0.26, APO : 479498.42
RUN #: 187, SAMPLE #: 28, EFPA: -11.76, SIGMA: 0.09, LD: 0.25, APO : 574378.91
RUN #: 188, SAMPLE #: 65, EFPA: -11.65, SIGMA: -0.09, LD: 0.25, APO : 516317.15
RUN #: 189, SAMPLE #: 112, EFPA: -11.56, SIGMA: -0.09, LD: 0.24, APO : 480544.44
RUN #: 190, SAMPLE #: 105, EFPA: -11.70, SIGMA: -0.17, LD: 0.26, APO : 523843.44
RUN #: 191, SAMPLE #: 172, EFPA: -11.70, SIGMA: -0.18, LD: 0.24, APO : 544246.42
RUN #: 192, SAMPLE #: 135, EFPA: -11.49, SIGMA: 0.18, LD: 0.24, APO : 517420.23
RUN #: 193, SAMPLE #: 172, EFPA: -11.60, SIGMA: -0.04, LD: 0.21, APO : 532617.98
RUN #: 194, SAMPLE #: 189, EFPA: -11.57, SIGMA: 0.09, LD: 0.23, APO : 513937.45
RUN #: 195, SAMPLE #: 123, EFPA: -11.77, SIGMA: -0.17, LD: 0.25, APO : 510125.29
RUN #: 196, SAMPLE #: 108, EFPA: -11.78, SIGMA: 0.07, LD: 0.23, APO : 491605.20
RUN #: 197, SAMPLE #: 190, EFPA: -11.63, SIGMA: -0.35, LD: 0.20, APO : 581432.24
RUN #: 198, SAMPLE #: 162, EFPA: -11.82, SIGMA: 0.10, LD: 0.25, APO : 524038.39
RUN #: 199, SAMPLE #: 82, EFPA: -11.56, SIGMA: -0.22, LD: 0.27, APO : 624429.49
RUN #: 200, SAMPLE #: 110, EFPA: -11.68, SIGMA: -0.16, LD: 0.25, APO : 506569.82
[12]:
p1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/terminal_periapsis_arr.txt')
x1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/terminal_apoapsis_arr.txt')
y1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/acc_net_g_max_arr.txt')
z1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/q_stag_max_arr.txt')
h1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/heatload_max_arr.txt')
u1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/periapsis_raise_DV_arr.txt')
v1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/apoapsis_raise_DV_arr.txt')
[13]:
fig, axes = plt.subplots(1, 2, figsize = (6.5, 3.25))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].hist(x1/1000.0, bins=10, color='xkcd:periwinkle')
axes[0].set_xlabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(p1, x1/1000.0, 'bo', markersize=5, mfc='None')
axes[1].set_xlabel('Periapsis altitude, km',fontsize=12)
axes[1].set_ylabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.show()
Remove cases which did not capture
[19]:
del_index1 = np.where(x1 < 0)
del_index1
[19]:
(array([], dtype=int64),)
[21]:
fig, axes = plt.subplots(1, 2, figsize = (6.5, 3.25))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].hist(x1/1000.0, bins=10, color='xkcd:periwinkle')
axes[0].set_xlabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(p1, x1/1000.0, 'bo', markersize=5, mfc='None')
axes[1].set_xlabel('Periapsis altitude, km',fontsize=12)
axes[1].set_ylabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.show()
[14]:
print("--------------------------------------------------------------------------------------------------------")
print("Parameter | Min. 0.13-percentile Mean 99.87-percentile Max")
print("--------------------------------------------------------------------------------------------------------")
print("Apoapsis altitude, km | " + str(round(np.min(x1))) + " " + str(round(np.percentile(x1, 0.13))) +\
" " + str(round(np.average(x1))) +
" " + str(round(np.percentile(x1, 99.87)))+\
" " + str(round(np.max(x1))))
print("Periapsis altitude, km | " + str(round(np.min(p1))) + " " + str(round(np.percentile(p1, 0.13))) +\
" " + str(round(np.average(p1))) +
" " + str(round(np.percentile(p1, 99.87)))+\
" " + str(round(np.max(p1))))
print("Peak deceleration, g | " + str(round(np.min(y1), 2)) + " " + str(round(np.percentile(y1, 0.13), 2)) +\
" " + str(round(np.average(y1), 2)) +
" " + str(round(np.percentile(y1, 99.87), 2))+\
" " + str(round(np.max(y1), 2)))
print("Peak heat rate, W/cm2 | " + str(round(np.min(z1))) + " " + str(round(np.percentile(z1, 0.13))) +\
" " + str(round(np.average(z1))) +
" " + str(round(np.percentile(z1, 99.87)))+\
" " + str(round(np.max(z1))))
print("Peak heat load, kJ/cm2 | " + str(round(np.min(h1))) + " " + str(round(np.percentile(h1, 0.13))) +\
" " + str(round(np.average(h1))) +
" " + str(round(np.percentile(h1, 99.87)))+\
" " + str(round(np.max(h1))))
print("Periapsis raise dV, m/s| " + str(round(np.min(u1), 2)) + " " + str(round(np.percentile(u1, 0.13), 2)) +\
" " + str(round(np.average(u1), 2)) +
" " + str(round(np.percentile(u1, 99.87), 2))+\
" " + str(round(np.max(u1), 2)))
print("Apoapsis corr. dV, m/s | " + str(round(np.min(v1), 2)) + " " + str(round(np.percentile(v1, 0.13), 2)) +\
" " + str(round(np.average(v1), 2)) +
" " + str(round(np.percentile(v1, 99.87), 2))+\
" " + str(round(np.max(v1), 2)))
print("--------------------------------------------------------------------------------------------------------")
--------------------------------------------------------------------------------------------------------
Parameter | Min. 0.13-percentile Mean 99.87-percentile Max
--------------------------------------------------------------------------------------------------------
Apoapsis altitude, km | 461172 462172 529271 617708 624429
Periapsis altitude, km | 171 172 192 208 208
Peak deceleration, g | 7.13 7.15 8.0 9.42 9.52
Peak heat rate, W/cm2 | 1604 1610 1744 1862 1865
Peak heat load, kJ/cm2 | 209 210 221 229 229
Periapsis raise dV, m/s| 56.23 56.83 65.52 73.59 73.81
Apoapsis corr. dV, m/s | -54.23 -49.7 19.01 84.38 85.5
--------------------------------------------------------------------------------------------------------
[15]:
fig, axes = plt.subplots(1, 2, figsize = (6.75, 3.5))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].axvline(x=550.0, linewidth=1.5, color='k', linestyle='dashed')
axes[0].hist(x1/1000.0, bins=10, color='xkcd:periwinkle')
axes[0].set_xlabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(p1, x1/1000.0, 'bo', markersize=5, mfc='None')
axes[1].axhline(y=550.0, linewidth=1.5, color='k', linestyle='dashed')
axes[1].set_xlabel('Periapsis altitude, km',fontsize=12)
axes[1].set_ylabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/periapsis-vs-apoapsis-XD.png', dpi= 300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/periapsis-vs-apoapsis-XD.pdf', dpi=300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/periapsis-vs-apoapsis-XD.eps', dpi=300,bbox_inches='tight')
plt.show()
[25]:
fig, axes = plt.subplots(1, 2, figsize = (6.75, 3.5))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].plot(y1, z1, 'ro', markersize=5, mfc='None')
axes[0].axhline(y=np.percentile(z1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[0].axvline(x=np.percentile(y1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[0].set_xlabel('Peak deceleration, Earth g', fontsize=12)
axes[0].set_ylabel('Peak stagnation-point heat rate, '+r'$W/cm^2$', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(z1, h1, 'go', markersize=5, mfc='None')
axes[1].axhline(y=np.percentile(h1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[1].axvline(x=np.percentile(z1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[1].set_xlabel('Peak stagnation-point heat rate, '+r'$W/cm^2$', fontsize=12)
axes[1].set_ylabel('Peak stagnation-point heat load, '+r'$kJ/cm^2$', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/heat-rate-vs-g-load-XD.png', dpi= 300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/heat-rate-vs-g-load-XD.pdf', dpi=300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/heat-rate-vs-g-load-XD.eps', dpi=300,bbox_inches='tight')
plt.show()
[26]:
fig, axes = plt.subplots(1, 2, figsize = (6.75, 3.5))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].hist(u1, bins=10, color='xkcd:periwinkle')
axes[0].axvline(x=np.percentile(u1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[0].set_xlabel('Periapse raise '+r'$\Delta V$'+', m/s', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].hist(v1, bins=10, color='xkcd:orange')
axes[1].axvline(x=np.percentile(v1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[1].set_xlabel('Apoapsis correction '+r'$\Delta V$'+', m/s', fontsize=12)
axes[1].set_ylabel('Number of cases', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/prm-arm-maneuvers-XD.png', dpi= 300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/prm-arm-maneuvers-XD.pdf', dpi=300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/prm-arm-maneuvers-XD.eps', dpi=300,bbox_inches='tight')
plt.show()
[3]:
def percent_between(a, lower, upper, closed_left=True, closed_right=True):
"""
Finds the percentage of values between a range for a numpy array
Parameters
----------
a: np.ndarray
numpy array to calculate percentage
lower: int, float
lower bound
upper: int, float
upper bound
closed_left:
closed left bound ( > vs >= )
closed_right:
closed right bound ( < vs <= )
"""
l = np.greater if not closed_left else np.greater_equal
r = np.less if not closed_right else np.less_equal
c = l(a, lower) & r(a, upper)
return np.count_nonzero(c) / float(a.size)
[5]:
p1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/terminal_periapsis_arr.txt')
x1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/terminal_apoapsis_arr.txt')
y1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/acc_net_g_max_arr.txt')
z1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/q_stag_max_arr.txt')
h1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/heatload_max_arr.txt')
u1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/periapsis_raise_DV_arr.txt')
v1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXD/apoapsis_raise_DV_arr.txt')
[6]:
percent_between(x1, -3e9, 0)
[6]:
0.0
[7]:
percent_between(x1, 500e3, 600e3)
[7]:
0.79
[8]:
percent_between(x1, 450e3, 650e3)
[8]:
1.0
[9]:
percent_between(x1, 350e3, 750e3)
[9]:
1.0
[ ]: