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

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ACKNOWLEDGEMENTS At times our own light goes out and is rekindled by a spark from another person. Each of us has cause to think with deep gratitude of those who have lighted the flame within us. -- Albert Schweitzer First and foremost, I would like to thank God for all the blessings He has bestowed upon me, the greatest of which has been the love and support of family. I would like to express my never-ending gratitude to my parents, who were the first to instill in me a love of learning and a strong work ethic, and have always believed in me and supported me in each and every endeavor I have pursued in my life. You were the first to light the flame within me, and constantly inspire me to pursue my goals and dreams. And to my brother Andrew, whose good-natured teasing about becoming a “doctor” has always made me laugh. Often times, laughter has truly been the best medicine. I am grateful for the opportunity to have worked with my advisor, Dr. Robert Braun, during my time at Georgia Tech. I am especially thankful for the flexibility he allowed me in choosing not only my dissertation topic, but also in allowing me to finish my dissertation remotely from California. I would also like to express my gratitude to the other members of my thesis committee, who all enthusiastically provided me with helpful advice and insight towards the completion of my dissertation. I would also like to thank Dr. John Olds, who first gave me the opportunity to work in the Space Systems Design Lab at Georgia Tech. It seems like each and every day I apply something I learned from Dr. Olds. He was an inspirational teacher and mentor. iv
I am thankful for all of my friends that have supported me throughout the dissertation process, particularly my labmates and friends at Georgia Tech. They made sure that no day was dull or boring, even the many late nights and weekends spent in lab. They were always there to laugh with, cry with, and share my achievements with. Thank you for your indispensable help in classes and on my research, and simply for being my friends. I would like to especially thank Ian Clark and Chris Tanner, who devoted many hours to helping me set up my work on the computer cluster, and Richard Otero, without whose Fortran help I would not have been able to complete my dissertation. To Bailey and Griffey, thank you for being my constant companions while working on my dissertation from home in California, and for never failing to put a smile on my face. Last, and certainly not least, I do not have the words to properly thank my fiancé, Devin Kipp, who has been my unwavering source of support and encouragement, even through the most trying times. You have been endlessly patient, and it is your faith in me that has enabled me to complete the PhD program, beginning with qualifying exams and ending with my PhD dissertation and defense. Having you in my life to share this accomplishment with makes it all the more special. And to everyone else who has inspired me along the way, thank you. I would not be the person I am today without the influence of my friends, family, teachers, and mentors. v
Page 1 and 2: DESIGN SPACE PRUNING HEURISTICS AND
Page 3: “The mediocre teacher tells. The
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
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Finally, both the eccentricity of a
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maximum possible number of revoluti
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outer loop: a genetic algorithm and
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2.3.2 Branch-and-Bound The branch-a
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The order in which the branches are
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approach performs best, followed by
Page 73 and 74:
Figure 16: Effect of number of segm
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Within the sample problem, MALTO wa
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Table 2: Orbital elements of astero
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final mass. The correlation coeffic
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Maximum Final Mass (kg) 1500 1250 L
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The next approach is to compare the
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Final Mass (kg) 950 900 850 800 750
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pruning metric must be calculated f
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educed-size problem. Furthermore, t
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aBiB < 25%, and 15% for Leg 1, Leg
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Earth departure date and three time
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algorithm, which determines the opt
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is calculated using the same specif
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metrics which were calculated in th
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sorted by final mass. All of the se
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impulse optima. Figure 35 plots the
Page 105 and 106:
CHAPTER III OVERVIEW OF METHODOLOGY
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(1) All asteroid sequences are rank
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two-impulse optimum solutions. Ther
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identifying another metric that cou
Page 113 and 114:
CHAPTER IV VALIDATION OF METHODOLOG
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Table 8 lists the 10 best asteroid
Page 117 and 118:
In order to further validate the pr
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impulse ∆V). The first iteration
Page 121 and 122:
Because the impulsive multiplier ha
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Table 12: Effectiveness of the meth
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function calls to MALTO were requir
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order of 0.8 (assuming a final mass
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Table 16: Design variables for gene
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known solution to 1621 kg. Based on
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MBfB (kg) #1, #63, and #16, respect
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problem (Table 17). Additionally, t
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≤ Inclination (deg) 60 50 40 30 2
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order to have a benchmark with whic
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Of the remaining sequences, the 1 s
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Table 22: Settings for the genetic
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120 Impulsive Solutions Low-Thrust
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of exactly four weeks. As a benchma
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Objective Function (kg/yr) 120 100
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these two problems, the best known
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asteroid sequences would be require
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Table 27: Best known solutions rema
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Table 29: Best known solutions rema
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good solutions exist in the design
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heuristics chosen were based on the
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problem, the best set of solutions
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Finally, for each set of inner loop
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solutions from the GTOC2 competitio
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sequences were optimized in low-thr
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application is for the conceptual d
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types of the TSP, they have not bee
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methodology would be applied in the
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APPENDIX A SET OF GTOC2 ASTEROIDS T
Page 179 and 180:
3054373 "2000 UK11" 0.88325596 0.24
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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 and 202:
[20] Hargraves, C. R., and Paris, S
Page 203 and 204:
[38] Petropoulos, A., Kowalkowski,
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
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