managing uncertainty in space systems conceptual design using portfolio theory aug 13 2002 aero ...

242 Pages·2013·30.23 MB·English

MANAGING UNCERTAINTY IN SPACE SYSTEMS CONCEPTUAL
DESIGN USING PORTFOLIO THEORY
By
Myles Alexander Walton
Bachelor of Science Mechanical Engineering, Worcester Polytechnic Institute, 1997
Master of Science Aeronautics and Astronautics, Massachusetts Institiute of Technology, 1999
Submitted to the Department of Aeronautics/Astronautics
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in AERONAUTICS and ASTRONAUTICS
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June, 2002
( 2002 Massachusetts Institute of Technology. All rights reserved
Author........................................... ................
Department of Aeronautics/Astronautics
C ertified b y ..................................... ............
Daniel Hastings, Professor of of Aeronautics and Astronautics and Engineering Systems
Associate Director of Engineering Systems Division, Thesis Supervisor
Dxestor, MIT Technology and Policy Program
C ertified by........................................ ...........
Edward Crawley, Professor of of Aeronautics and Astr g Systems
Department Head, Aeronautics and Astronautics, Thesis Committee Member
C ertified by......................................................... .... . .. . . .
Earll Murman, Professor of of Aeronautics and Nstronautics and Engineering Systems
Ford Professor of Engineering, Thesis Committee Member
C ertified by...........................
Joyce Warmkessel, Senior Lecturer{Department of Aeronautics and Astronautics
Thesis Committee Member
A ccepted by................................. .....
Wallace Vander Velde, Professor Emeritus of Aeronautics and Astronautics
Chairman, Department Graduate Committee
MASSACHUSETT$ INSTIUTE
OF TECHNOLOGY
AUG 13 2002 AERO I
LIBRARIES

MANAGING UNCERTAINTY IN SPACE SYSTEMS
CONCEPTUAL DESIGN USING PORTFOLIO THEORY
by
Myles Alexander Walton
Submitted to the Department of Aeronautics and Astronautics in
partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Aeronautics and Astronautics
Abstract
One of the most significant challenges in conceptual design is managing the tradespace of potential
architectures-choosing which design to pursue aggressively, which to keep on the table and which
to leave behind. This thesis provides a framework for managing a tradespace of architectures not
through traditional effectiveness measures like cost and performance, but instead through a
quantitative analysis of the embedded uncertainty in each potential space system architecture. Cost
and performance in this approach remain central themes in decision making, but uncertainty serves
as the focal lense to identify potentially powerful combinations of architectures to explore
concurrently in further design phases.
Presented is an approach to identify, assess, and quantify uncertainty in space system architectures,
as well as a means to manage it using portfolio theory and optimization. Perhaps best known to
economists and investors, portfolio theory is based around the objective of maximizing return
subject to a decision maker's risk aversion. This simple concept, as well as the theoretical rigor that
has evolved the theory to practice, is presented as one means of exploring the tradespace of
potential architectures around the central theme of uncertainty.
The approach presented relies upon previous work to model space system architectures using
simulations that capture attributes of performance and cost. The first step in the approach is an
analysis of the tradespace of potential architectures, including the bounding of architectural concepts
that will be evaluated and the potential uncertainties and scenarios that will be investigated. The
second step is to adjust the simulation models to include sources of uncertainty. The third step is to
quantify the impact of the uncertainties on the evaluation criteria for each architecture through
propagation techniques. Finally, portfolio theory is incorporated as an approach to manage
uncertainty effectively.
Illustrative cases present the changing shape of the decision process with uncertainty as a focal
point. The three cases, a military space based radar mission, a commercial broadband system, and
an scientific observing mission, illustrate the this new approach on tradespace exploration and
highlight some of the intuitive and non-intuitive characteristics that can be discovered about the
tradespace.
Thesis Supervisor: Professor Daniel Hastings
Professor of Aeronautics and Astronautics and Engineering Systems
Associate Director of Engineering Systems Division, Thesis Supervisor
Director, MIT Technology and Policy Program
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ACKNOWLEDGMENTS
I'd first like to acknowledge my advisor, Professor Daniel Hastings. In his capacity as my research
supervisor and doctoral committee chair, Prof. Hastings afforded me the freedom to explore my
interests in cross-disciplinary techniques that served as the basis for this research. His continuous
support and encouragement made the conceptual blockbusting possible.
The rest of my doctoral committee deserves a great deal of credit for the work presented in this thesis.
Prof. Earll Murman's generosity in bringing me into interesting and exciting projects outside of the
narrow scope of my research broadened my experience here at MIT and allowed me to step back from
the thesis from time to time. Dr. Joyce Warmkessel, my Master's Thesis advisor, provided me with the
ever-important practical perspective of the industry expectations of research. Professor Ed Crawley
served me with the amazing ability to wrap his mind around a problem quickly and an uncanny gift to
craft effective messages of the research for communication. Professor Andreas Schulz's initial class in
operations research served as some of the original impetus to this work.
Other faculty members who have contributed to this work through insightful feedback and review
include Hugh McManus, David Miller and Olivier de Weck. I'd also like to acknowledge those
organizations and individuals in industry who hosted site visits for me during the initial stages of the
research and provided valuable feedback as it matured.
MIT has been my home for the past five years and it has been the amazing people here that have made
the stay a wonderful experience, most notably, my graduate student colleagues in the Lean Aerospace
Initiative (ILAI) [Josh Bernstein, Carmen Carrera, Jim Chase, Rob Dare, Heidi Davidz, Jason Derleth,
Bobeck Ferdowsi, Chris Forseth, Cory Hallam, Sean Hitchings, Brian Ippolito, Sandra Kassin-
Deardorff, Aaron Kirtley, Jacob Markish, Michelle McVey, Rich Millard, Jeff Munson, Matthew
Nuffort, Nirav Shah, Larry Siegel, Alexis Stanke, Dave Tonaszuck, Mandy Vaughn], the Space Systems
Lab (SSL) [Cyrus Jilla and John Enright], and the Space Systems Policy and Architecture Research
Consortium (SSPARC) [Adam Ross, Nathan Diller, Satwick Seshasai.].
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Also, researchers from Prof. Hastings's research group have each provided insightful feedback on the
work as it has evolved, notably Elisabeth Lamassoure, Roshanak Nilchiani, Peter Panetta, Chris
Roberts and Joseph Saleh.
Living near my family has afforded me the luxury of staying in close contact with my parents, Steven
and Evelyn Walton, and siblings [Vicky and Robert, Cindy and J6rg, Neal and Lynne, David and Jen,
Tam and Steve, and Clint] whose constant encouragement and love made the PhD journey much
easier. I'd also like to thank the family I married into [Arthur and Lillian Weigel and John] for
welcoming me in as one of their own.
I've heard stories that getting a PhD can be an isolating experience. For me, it was anything but
isolated. I had the unique advantage of a loving wife, Annalisa, going through the PhD process at the
same time. A constant role model for me in research and life, Annalisa was and is my greatest source
of support and inspiration.
The author gratefully acknowledges the financial support for this research from the Space Systems
Policy and Architecture Research Consortium (SSPARC), a joint research program funded by the U.S.
government involving the Massachusetts Institute of Technology, the California Institute of
Technology, and Stanford University.
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TABLE OF CONTENTS
LIST OF FIGURES .......................................................................................................................... 13
LIST OF TABLES ............................................................................................................................ 17
G L O SSA R Y ....................................................................................................................................... 19
CHAPTER 1
IN TRO D U C TION ............................................................................................................................--- 2 1
1.1 Problem Statement and Approach ............................................................................. 24
1.2 Structure of D ocum ent................................................................................................. 26
CHAPTER 2
UNCERTAINTY AS A DECISION CRITERIA IN DESIGN ................................................................... 29
2.1 Simple illustration of the impact of uncertainty included as a decision criteria..........31
2.2 Vision for UncertaintyA nalysis in Space Systems ConceptualD esign....................35
2.3 Three Principleso f UncertaintyA nalysis in Space Systems ConceptualD esign.........37
PART I: AN APPROACH TO QUANTIFY AND MANAGE UNCERTAINTY IN SPACE
SYSTEMS CONCEPTUAL DESIGN....................................................................................... 39
CHAPTER 3
CURRENT STATE OF UNCERTAINTY ANALYSIS IN SPACE SYSTEMS DESIGN AT FOUR MAJOR SPACE
SY STEM S D EV ELO PER S ................................................................................................................----- 4 1
3 .1 In trod u ction :. .................................................................................................................. 4 1
3.2 Case 1 [6 Interviewees-Group].................................................................................. 42
3.3 Case 2 [6 Interviewees-Group].................................................................................. 45
3.4 Case 3 [9 Interviewees-Individual]........................................................................... 46
3.5 Case 4 [5 Interviewees-Individual]........................................................................... 49
3.6 OverarchingT hemes and Challenges.........................................................................53
3.6.1 Challenges taken up by the research......................................................................54
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3.6.2 Challenges posed to future research from the site visits ................... 55
CHAPTER 4
CURRENT APPROACHES TO ASSESSING UNCERTAINTY AND RISK IN SPACE SYSTEMS CONCEPTUAL
D E S IG N ............................................................................................................................................. 5 7
4 .1 In trod u ction .................................................................................................................... 5 7
4.2 Literature on qualitative techniques to managing uncertainty and risk...................59
4.2.1 R isk Exposure An alysis ......................................................................................... 59
4.3 Literature on semi-quantitativet echniques to managing uncertainty and risk........61
4.4 Literature on quantitative techniques to managing uncertainty and risk.................62
4.4.1 Statistical Techniques of Measuring Uncertainty................................................. 62
4.4.2 Fuzzy Logic applied to managing uncertainty in space systems ......................... 63
4.4.3 Probabilistic R isk A ssessm ent ............................................................................... 63
4.4.4 Other relevant methods of uncertainty analysis in conceptual design................. 66
4.5 Limitations of methods for current methods for managing uncertainty and risk.........67
CHAPTER 5
QUANTIFICATION OF EMBEDDED LIFECYCLE UNCERTAINTY......................................................71
5.1 De fining Em bedded Uncertainty............................................................................... 71
5.2 Quantifying Embedded Uncertainty in Space System Architectures ........................ 72
5.2.1 Developing the boundaries for uncertainty........................................................... 73
5.2.2 Quantifying Individual Sources of Uncertainty ................................................... 73
5.2.3 GI N A D esign Ap proach.......................................................................................... 76
5.2.4 Propagating U ncertainties .................................................................................... 78
5.3 Visualizing Architectural Uncertainty. ..................................................................... 81
5.3.1 Focusing on individual architectures .................................................................... 81
5.3.2 C om parative techniques ......................................................................................... 84
CHAPTER 6
PORTFOLIO THEORY APPLIED TO SPACE SYSTEMS CONCEPTUAL DESIGN .................................. 89
6.1 M odern P ortfolio Theory............................................................................................. 89
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6.1.1 M athem atics of Portfolio Optim ization.....................................................................90
6.1.2 An example of financial portfolio analysis................................................................96
6.1.3 An example of portfolio analysis applied to space systems................................. 97
6.2 Uncovering risk aversion in stakeholders ................................................................. 99
6.2.1 M ethods of capturing uncertainty aversion ............................................................. 100
6.3 Extensions ofportfolio theory to space system design ................................................ 105
6.3.1 A ccounting for upside potential from uncertainty...................................................105
6.3.2 Cost of D iversification ............................................................................................. 107
6.4 Putting it together ......................................................................................................... 108
6.5 Implem enting the Algorithm ......................................................................................... 109
6.6 Where the portfolio theory breaks down in space systems design .............................. 110
6.6.1 Practical Lim itations.................................................................................................110
6.6.2 Theoretical Limi tations ............................................................................................ 111
PA RT II: CA SE STU D IES A ND RESU LTS...............................................................................113
CHAPTER 7
TECHSAT 21: CUTTING EDGE DESIGN INTRODUCES UNCERTAINTY ............................................... 115
7.1 M ission and M odel De scription...................................................................................115
7.1.1 GI N A M odel.......................................................................................................... 116
7.1.2 M odel R esults...........................................................................................................118
7.2 Uncertainty Quantification...........................................................................................121
7.2.1 Sources of uncertainty .............................................................................................. 121
7.2.2 Emb edded architectural uncertainty ........................................................................ 123
7.3 PortfolioA nalysis ......................................................................................................... 125
7.3.1 Quantifying D ecision M aker Risk A version ........................................................... 127
7.3.2 Implications of incorporating the extensions to portfolio theory............................132
7.4 Conclusions...................................................................................................................138
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CHAPTER 8
COMMERCIAL BROADBAND SATELLITE SYSTEM: MARKET UNCERTAINTIES MAKE OR BREAK THE
BUSINESS M ODEL...........................................................................................................................139
8.1 M ission and M odel De scription...................................................................................139
8.1.1 G IN A M odel.......................................................................................................... 141
8.1.2 M odel R esults...........................................................................................................142
8.2 Uncertainty Qu antification...........................................................................................143
8.2.1 Sources of uncertainty .............................................................................................. 144
8.2.2 Em bedded Ar chitectural Un certainty ...................................................................... 147
8.3 Portfolio Assessm ent .................................................................................................... 149
8.3.1 Q uantifying D ecision M aker R isk Av ersion ........................................................... 151
8.3.2 Implications of incorporating the extensions to portfolio theory............................158
8.4 Conclusions...................................................................................................................165
CHAPTER 9
TOP SIDE SOUNDING IONOSPHERIC MAPPING MISSION: UNCERTAINTIES IN UTILITY..................167
9.1 M ission and M odel D escription................................................................................... 167
9.1.1 The Ionosphere ......................................................................................................... 167
9.1.2 Ionospheric Influence ............................................................................................... 169
9.1.3 The A TO S M ission .................................................................................................. 170
9.1.4 D erived Ut ility Function .......................................................................................... 172
9.1.5 G IN A /U tility M odel.................................................................................................176
9.1.6 M odel R esults...........................................................................................................178
9.2 Uncertainty Q uantification...........................................................................................181
9.2.1 Sources of uncertainty .............................................................................................. 181
9.2.2 Emb edded Ar chitectural U ncertainty ...................................................................... 184
9.3 Portfolio Assessm ent .................................................................................................... 187
9.3.1 Q uantifying D ecision M aker Risk Av ersion ...................................................... 189
9.3.2 Implications of incorporating the extensions to portfolio theory............................197
9.4 Conclusions...................................................................................................................203
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