design space pruning heuristics and global optimization method for ...

More documents

Recommendations

Info

[29] McConaghy, T.T., Debban, T.J., Petropoulos, E., Longuski, M., “Design and Optimization of Low-Thrust Trajectories with Gravity Assists”, Journal of Spacecraft and Rockets, Vol. 40, No. 3, May-June 2003. [30] Okutsu, M., Yam, C.H., Longuski, J.M., “Low-Thrust Trajectories to Jupiter via Gravity Assists from Venus, Earth, and Mars”, AIAA 2006-6745, AIAA/AAS Astrodynamics Specialist Conference, 21-24 August 2006, Keystone, CO. [31] Yam, C. H. and Longuski, J., M., “Optimization of Low-Thrust Gravity-Assist Trajectories with a Reduced Parameterization of the Thrust Vector,” AAS/AIAA Astrodynamics Specialist Conference and Exhibit, AAS Paper 05-375, 1995, Lake Tahoe, CA. [32] Yam, C.H., Longuski, J.M., “Reduced Parameterization for Optimization of Low- Thrust Gravity-Assist Trajectories: Case Studies”, AIAA 2006-6744, AIAA/AAS Astrodynamics Specialist Conference, 21-24 August 2006, Keystone, CO. [33] Petropoulos, A.E., Longuski, J.M., “A Shape-Based Algorithm for the Automated Design of Low-Thrust, Gravity-Assist Trajectories,” AAS 01-467, AAS/AIAA Astrodynamics Specialist Conference, Quebec City, Quebec, Canada, 30 July – 2 August 2001. [34] Petropoulos, A.E., Longuski, J.M., Vinh, N.X., “Shape-Based Analytic Representations of Low-Thrust Trajectories for Gravity-Assist Applications,” AAS/AIAA Astrodynamics Specialist Conference, AAS Paper 99-337, Girdwood, AL, August 1999. [35] Petropoulos, A.E., Longuski, J.M, “Automated Design of Low-Thrust Gravity- Assist Trajectories,” AIAA/AAS Astrodynamics Specialist Conference, AIAA 2000-4033, Denver, CO, 14-17 August 2000. [36] “1st ACT Global Trajectory Optimisation Competition,” European Space Agency. [http://www.esa.int/gsp/ACT/mission_analysis/globaloptimisationcompetition.htm. Accessed 27 January 2007]. [37] Izzo, D., “Problem Description – 1st ACT Competition on Global Trajectory Optimisation,” European Space Agency, November 2005. [http://www.esa.int/gsp/ACT/doc/ACT-4100-DI-The%20Problem_V4.pdf. Accessed 27 January 2007]. 183
[38] Petropoulos, A., Kowalkowski, T., Parcher, D., Finlayson, P., Rinderle, E., Vavrina, M., Sims, J., Russell, R., Lam, T., Williams, P., Whiffen, G., Strange, N., Johannesen, J., Yen, C.W., Sauer, C., Lee, S., Williams, S., “Response to the First ACT Competition on Global Trajectory Optimisation,” NASA Jet Propulsion Lab, Pasadena, CA, 5 December 2005. [http://trsnew.jpl.nasa.gov/dspace/handle/2014/38539?mode=simple. Accessed 27 January 2007]. [39] Polsgrove, T., Kos, L, Hopkins, R., Crane, T., “Comparison of Performance Predictions for New Low-Thrust Trajectory Tools”, AIAA 2006-6742, AIAA/AAS Astrodynamics Specialist Conference, 21-24 August 2006, Keystone, CO. [40] Kos, L, Polsgrove, T., Hopkins, R., Thomas, D., “Overview of the Development for a Suite of Low-Thrust Trajectory Analysis Tools”, AIAA 2006-6743, AIAA/AAS Astrodynamics Specialist Conference, 21-24 August 2006, Keystone, CO. [41] Ocampo, C., “COPERNICUS: A Trajectory Design and Optimization System,” University of Texas, Austin, TX. [www.ieec.fcr.es/libpoint/abstracts/ocampo.pdf. Accessed 25 January 2007]. [42] Whiffen, G.J., “Mystic: Implementation of the Static Dynamic Control Algorithm for High-Fidelity, Low-Thrust Trajectory Design,” AIAA/AAS Astrodynamics Specialist Conference, 21-24 August 2006, Keystone, CO. [43] Paris, S.W., Riehl, J.P., Sjauw, W.K., “Enhanced Procedures for Direct Trajectory Optimization Using Nonlinear Programming and Implicit Integration,” AIAA/AAS Astrodynamics Specialist Conference and Exhibit, 21-24 August 2006, Keysone, CO. [44] Riehl, J.P., Paris, S.W., Sjauw, W.K., “Comparison of Implicit Integration Methods for Solving Aerospace Trajectory Optimization Problems,” AIAA/AAS Astrodynamics Specialist Conference and Exhibit, 21-24 August 2006, Keysone, CO. [45] Back, T., Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York, NY, 1996. [46] Gen, M., Cheng, R., Genetic Algorithms and Engineering Design, John Wiley & Sons, Inc., New York, NY, 1997. 184
Page 1 and 2:
DESIGN SPACE PRUNING HEURISTICS AND
Page 3 and 4:
“The mediocre teacher tells. The
Page 5 and 6:
I am thankful for all of my friends
Page 7 and 8:
2.5 Small Sample Problem...........
Page 9 and 10:
LIST OF TABLES Table 1 Ten best ast
Page 11 and 12:
LIST OF FIGURES Figure 1 Mars round
Page 13 and 14:
Figure 35: Low-thrust optima as a f
Page 15 and 16:
LIST OF SYMBOLS AND ABBREVIATIONS A
Page 17 and 18:
σBXB υ φ ω Ω Sample standard d
Page 19 and 20:
optimization scheme to locate a bro
Page 21 and 22:
provide clues to the nature of the
Page 23 and 24:
additional constraints and objectiv
Page 25 and 26:
Pontryagin’s Minimum Principle, w
Page 27 and 28:
improved accuracy (as compared to d
Page 29 and 30:
Figure 2: Trajectory structure of t
Page 31 and 32:
flyby problems with numerous interm
Page 33 and 34:
Gravity assists are modeled as inst
Page 35 and 36:
Prior to the LTTT effort, the prima
Page 37 and 38:
the shape of the trajectory and ana
Page 39 and 40:
draws upon the theory of niche and
Page 41 and 42:
the sequence of encounter bodies, t
Page 43 and 44:
1.2.2 Evolutionary Neurocontrollers
Page 45 and 46:
Figure 6: Converting an evolutionar
Page 47 and 48:
which find good solutions but can n
Page 49 and 50:
functions. In this problem, only im
Page 51 and 52:
As aforementioned, branch-and-bound
Page 53 and 54:
consuming, user-intensive, and ofte
Page 55 and 56:
asteroid tour mission design proble
Page 57 and 58:
CHAPTER II DEVELOPMENT OF METHODOLO
Page 59 and 60:
Kˆ ĥ i ν ê v r ω Ĵ Ω nˆ Î
Page 61 and 62:
Finally, both the eccentricity of a
Page 63 and 64:
maximum possible number of revoluti
Page 65 and 66:
outer loop: a genetic algorithm and
Page 67 and 68:
2.3.2 Branch-and-Bound The branch-a
Page 69 and 70:
The order in which the branches are
Page 71 and 72:
approach performs best, followed by
Page 73 and 74:
Figure 16: Effect of number of segm
Page 75 and 76:
Within the sample problem, MALTO wa
Page 77 and 78:
Table 2: Orbital elements of astero
Page 79 and 80:
final mass. The correlation coeffic
Page 81 and 82:
Maximum Final Mass (kg) 1500 1250 L
Page 83 and 84:
The next approach is to compare the
Page 85 and 86:
Final Mass (kg) 950 900 850 800 750
Page 87 and 88:
pruning metric must be calculated f
Page 89 and 90:
educed-size problem. Furthermore, t
Page 91 and 92:
aBiB < 25%, and 15% for Leg 1, Leg
Page 93 and 94:
Earth departure date and three time
Page 95 and 96:
algorithm, which determines the opt
Page 97 and 98:
is calculated using the same specif
Page 99 and 100:
metrics which were calculated in th
Page 101 and 102:
sorted by final mass. All of the se
Page 103 and 104:
impulse optima. Figure 35 plots the
Page 105 and 106:
CHAPTER III OVERVIEW OF METHODOLOGY
Page 107 and 108:
(1) All asteroid sequences are rank
Page 109 and 110:
two-impulse optimum solutions. Ther
Page 111 and 112:
identifying another metric that cou
Page 113 and 114:
CHAPTER IV VALIDATION OF METHODOLOG
Page 115 and 116:
Table 8 lists the 10 best asteroid
Page 117 and 118:
In order to further validate the pr
Page 119 and 120:
impulse ∆V). The first iteration
Page 121 and 122:
Because the impulsive multiplier ha
Page 123 and 124:
Table 12: Effectiveness of the meth
Page 125 and 126:
function calls to MALTO were requir
Page 127 and 128:
order of 0.8 (assuming a final mass
Page 129 and 130:
Table 16: Design variables for gene
Page 131 and 132:
known solution to 1621 kg. Based on
Page 133 and 134:
MBfB (kg) #1, #63, and #16, respect
Page 135 and 136:
problem (Table 17). Additionally, t
Page 137 and 138:
≤ Inclination (deg) 60 50 40 30 2
Page 139 and 140:
order to have a benchmark with whic
Page 141 and 142:
Of the remaining sequences, the 1 s
Page 143 and 144:
Table 22: Settings for the genetic
Page 145 and 146:
120 Impulsive Solutions Low-Thrust
Page 147 and 148:
of exactly four weeks. As a benchma
Page 149 and 150:
Objective Function (kg/yr) 120 100
Page 151 and 152: these two problems, the best known
Page 153 and 154: asteroid sequences would be require
Page 155 and 156: Table 27: Best known solutions rema
Page 157 and 158: Table 29: Best known solutions rema
Page 159 and 160: good solutions exist in the design
Page 161 and 162: heuristics chosen were based on the
Page 163 and 164: problem, the best set of solutions
Page 165 and 166: Finally, for each set of inner loop
Page 167 and 168: solutions from the GTOC2 competitio
Page 169 and 170: sequences were optimized in low-thr
Page 171 and 172: application is for the conceptual d
Page 173 and 174: types of the TSP, they have not bee
Page 175 and 176: methodology would be applied in the
Page 177 and 178: APPENDIX A SET OF GTOC2 ASTEROIDS T
Page 179 and 180: 3054373 "2000 UK11" 0.88325596 0.24
Page 181 and 182: 3152317 "2003 GQ22" 0.87232869 0.18
Page 183 and 184: 3283227 "2005 MR5" 0.85281863 0.295
Page 185 and 186: 2000089 Julia 2.5500653 0.18377079
Page 187 and 188: 2000496 Gryphia 2.1987751 0.079568
Page 189 and 190: 2001216 Askania 2.2322234 0.1793551
Page 191 and 192: 2000134 Sophrosyne 2.5632069 0.1166
Page 193 and 194: 2000712 Boliviana 2.5738464 0.18812
Page 195 and 196: 2005209 "1989 CW1" 5.1533221 0.0495
Page 197 and 198: 36 2006 RJ1 0.9508113 0.30070707 1.
Page 199 and 200: REFERENCES [1] Rayman, M.D., Willia
Page 201: [20] Hargraves, C. R., and Paris, S
Page 205 and 206: [57] Wuerl, A., Crain, T., Braden,
Page 207 and 208: [78] Cosmic Vision: Space Science f
Page 209: time, she enjoys traveling as well
show all

design space pruning heuristics and global optimization method for ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?