Post on 21-Jun-2020
Concept Design
and
Tradespace Exploration
28 October 2014
Dr. Donna H. Rhodes Dr. Adam M. Ross
rhodes@mit.edu adamross@mit.edu
The following slides are an excerpt from this lecture
The Design Knowledge Gap
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How can we make good decisions and design choices?
Adapted from Fabrycky and Blanchard 1991
Value is primarily determined at the beginning of a program
The Scope of Upfront Decisions
Key Phase Activities
Concept(s) Selected
Needs Captured
Resources Scoped
DesignDesign
In SituIn Situ
Top-side sounderTop-side soundervs.
In SituIn Situ
Top-side sounderTop-side soundervs.
After Fabrycky and Blanchard 1991
~66%
Conceptual Design is a
high leverage phase in
system development
Reliance upon BOGGSAT could have large consequences
How can we make better decisions?
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Three keys to good upfront
decisions
• Structured program selection process– Choosing the programs that are right for the organization’s
stakeholders
• Systems engineering– Determining stakeholder needs, generating concept of
operations, and deriving requirements
• Conceptual design practices– Finding the right form to maximize stakeholder value over the
product (or product family) lifetime
“Good” system decisions must both answer the right
questions as well as answer the questions right
What is involved in getting the right questions?
Decision Maker defined objectives
What is involved in getting the questions right?
Rigorous decision and analytic methods
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Determining “Value” Proposition
Create a system that fulfills some need while efficiently utilizing resources
within some context
Unknown
Stakeholders
Stakeholders
context
resources
need
Decision Makers Designers systemfulfills
efficiently
Decision makers are a subset that have significant
influence/control over the driving need and/or resources
There may be many stakeholders, but…
… so focus on satisfying decision makersseari.mit.edu © 2014 Massachusetts Institute of Technology 5
Define the Mission
Mission
Objectives
Attributes
Utility
• Understand the
“mission”
• Create a list of
attributes
• Interview the
decision maker(s)
• Create utility
curves
Decision
Makers
(Goals)
(Selection rule:
maximize “utility”)
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What is the
problem to
be solved?
Stakeholders-Attributes-Utilities
• In order to ensure a successful mission, the implied value proposition must be fulfilled
• Each system stakeholder has a value proposition—what they want to “get out” of the mission
• Decision makers are stakeholders with influence over the mission objectives for needs and/or resources
• Meeting the objectives for each decision maker can be assessed in terms of “attributes”
• An alternative that scores well in a set of attributes gives a decision maker value, or “utility”
• The goal for the selection of a good alternative is to maximize the utility for individuals and groups
Stakeholders
Decision
Makers
Resource objectives
Need objectivesSelection criteria
(Attributes)
Alternatives
Ranked
Alternatives
1
2
3
Incre
asin
g
Utilit
y“good”
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MATE Method
for Tradespace Exploration
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Example: Space Tug
Recently flown Astro/NextSat
mission:
Robotic, autonomous on-orbit
refueling and reconfiguration test
program-
Proof of concept!
General purpose vehicle to intercept,
interact with, and accelerate other vehicles:
General question - what would be a useful
design for such a vehicle?
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What is a Tradespace?
Attributes– Delta-V
– Capability
– Response time
Design Space>Manipulator Mass
– Low (300kg)– Medium (1000kg)– High (3000 kg)– Extreme (5000 kg)
>Propulsion Type– Storable bi-prop– Cryogenic bi-prop– Electric (NSTAR)– Nuclear Thermal
>Fuel Load - 8 levels
>Simple performance model–Delta-V calculated from rocket equation
–Binary response time (electric propulsion slow)
–Capability solely dependent on manipulator mass
–Cost calculated from vehicle wet and dry mass
Number of Architectures Explored: 50488Number of Architectures Explored: 50488
Each point is
a specific
architecture
Assessment of the benefit (e.g. utility) and cost
of a large space of possible system designs
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Tradespace Data Representation
Rich data sets can
be explored to
reveal complex
relationships
between
design-space and
value-space
for generating
intuition into
problem—a multi-
dimensional
analogy to graphing
y=f(x)
“Explore” tradespace data to develop intuition into complex design-value relationships
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Steps for Multi-Attribute Tradespace
Exploration (MATE)
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• Determine Key Decision Makers
• Scope and Bound the Mission
• Elicit Attributes–Determine Utilities
• Define Design Vector Elements–Includes Fixing Constants Vector
• Develop Model(s) to link Design and Attributes
–Includes Cost Modeling
• Generate the Tradespace
• Tradespace Exploration
Mission
Concept
Attributes
Calculate
Utility
Develop System
Model
Estimate
Cost
System
Tradespace
Define Design
Vector
Decision
Makers
Cost
Utility
Cost
Utility
Stakeholder
preferences
multi-attribute set
design variable set
model
costs
evaluated alternatives
alternatives
tradespace
insights
benefits
eval’d attributes
(utility)
eval’d costs
defines
informs
Exploration
Enumeration
Evaluation
{size, shape, material}
{fitness, safety, unit cost}
Utilit
y
Cost
Generation
Selection
Key Concepts in MATE
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Decision Makers
and Mission Concept
• Choose Decision Makers that you have to satisfy -
they will define the utility
• Choose Mission Concept(s) - the basic framework
you will use to define the design vector
– Open enough so that creative solutions are not excluded
– Defined enough to be tractable
Space Tug Example:
• (potential) Stakeholder need is for infrastructure to
maintain on-orbit assets
• Mission concept is vehicle that can rendezvous with and
interact with on-orbit assets
• Project Mission is to assess how potential systems could
satisfy potential stakeholders
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Framework for Identifying Key
Decision Makers
Definition of Levels
Level 2 – Close connection to System
Level 1 – Distant connection to System
Level 0 – Little or no connection to System
External Stakeholders
Firm Customer
Designer User
Level 0
Level 1
Level 2
Contracts
Organizational
Goals
Operational
Strategy
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Bound and Scope: Space Tug
Choose the parts of the
overall system that you will
design
Choose finite (but as open as
possible) set of potential
solutions
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Define Attributes
• Defined by the decision maker, with designer assistance
• Define units, lowest acceptable value, highest meaningful value
• Set of 3-7 attributes should obey, to the extent possible, perceived independence and other rules
• Ideally:– Reflect what the decision maker cares about
– Computable
– Sensitive to design decisions
You will probably have to iterate
1) Delta-V: How much velocity can the
vehicle impart on itself and/or the
target? (km/sec) [>012]
2) Interaction Capability: What can the
vehicle do to the target? (kg of
equipment carried) [>05000]
3) Speed: Can the Space Tug change
orbits in days? Months? (binary) [01]
Space Tug Example:
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Defining Utilities
• Each attribute (worst to best value) monotonically maps to utility (0 to 1)
• Need rule for under/over values
Space Tug Example:Capability vs. UtilityDelta-V vs. Utility
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Aggregating Utilities
• If possible, define an aggregating function– Weighted sum often used (iff Ski=1!)
– MAUT Keeney-Raiffa function better
– Weights defined by decision maker
DV Utility
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 2000 4000 6000 8000 10000 12000
DV
DV
sin
gle
-att
rib
ute
Uti
lity
Leo-Geo
Leo-Geo RT
Diminishing returns, with
breakpoints at targets
Uti
lity
Delta-V (km/sec)
0 2 4 6 81
0
1
2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Low Medium High Extreme
Cap
ab
ilit
y S
ing
le-A
ttrib
ute
Uti
lity
=300kg =1000kg =3000kg =5000kg
Diminishing returns,
discrete levels
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Capability DeltaV Fast
We
igh
t (d
ime
nsio
nle
ss
)
N
i
ii XUKkXKU1
1)(1)(
Single attribute utilities
Weighting
factors
Aggregating Function
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Defining the Design Vector
• The design vector defines the space of designs that will be considered -a key step!
• Define units, range to be considered, sampling levels
• Good design vector elements (DV)– Capture the range of possible solutions
– Are realistic, physically or in terms of available technology or components
– Are under the direct control of the designer
– Impact the attributes
• Steps: – Brainstorm individually & in groups, consider how to best affect the attributes
– Use a DVM to map DV to attributes to screen out unnecessary DV and to motivate creation of more DV if attributes are not affected
• Manipulator Mass (=Capability)
– Low (300kg), Medium (1000kg), High (3000 kg), Extreme (5000 kg)
• Propulsion Type
– Storable bi-prop, Cryogenic bi-prop, Electric (NSTAR), Nuclear Thermal
• Fuel/Reaction Mass Load
– 8 levels, geometric progression (30 to 30000kg)
Space Tug Example:
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The Constants Vector
• To keep modeling general and adaptable to later changes, do not “hardwire” assumptions into code
• Instead, keep list (data structure) of “constants”
• Five types (at least):– True constants (g, ), value may change if your units change…
– Constraints (policies, standards…)
– Modeling assumptions ($/kg, W/GHz, Margins…)
– Quantities associated with design vector choices
– Potential design vector elements (things under designers control) that have been fixed - record reason
Space Tug Example:
Design Var. Associated Quantities
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Modeling
• Calculate Costs & Attributes from Design Vector & Constants Vector
• Approaches:– Tabulation - car example from previous lecture
– Explicit calculation - Attributes = f(DV, CV)
– Implicit or iterative calculations• May involve local optimizations
– Simulations/Scenarios
• Calculate single- and multi-attribute Utilities
• All but the simplest models will involve important Intermediate Variables (e.g. system mass, power) which should be explicitly calculated and tracked
Design Vector
Constants VectorModel(s)
Costs
Attributes/Utilities
Intermediate Variables
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Motivating the Model: Space Tug
• Very simple, explicit, physics-
based model
• Intermediate Variables (masses):
• Attributes
Design-Value Mapping (DVM) can also
be used to motivate and identify
relationships for models
Purpose of model: Calculate “performance” of each
design in terms of attributes, costs, and utilities
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Cost Modeling
• Need cost estimates for each design– For decision making, not budget planning
– Order of magnitude costs for concepts
– Relative costs of various concepts
• Many approaches and tools available
• Fidelity will be low in early design (this is OK)– Expect +/- 30% error, even in simple stuff
• Need to keep track of limits and weaknesses of cost (and other) models (ROM uncertainties)
C cw Mw cdMd
Space Tug Example:
• Very very simple parametric model
• Appropriate to broad survey
• Important to understand what is not modeled
– Software
– Launch
– Technology Development
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Baseline Study: Space Tug
• Existing MATE* study of space tug tradespace– Three attributes
• Delta-V
• Capability
• Response time
– Three design variables
Design Space>Manipulator Mass
– Low (300kg)– Medium (1000kg)– High (3000 kg)– Extreme (5000 kg)
>Propulsion Type– Storable bi-prop– Cryogenic bi-prop– Electric (NSTAR)– Nuclear Thermal
>Fuel Load - 8 levels
>Simple performance model–Delta-V calculated from rocket equation
–Binary response time (electric propulsion slow)
–Capability solely dependent on manipulator mass
–Cost calculated from vehicle wet and dry mass
* MATE: Multi-Attribute Tradespace Exploration; see McManus, H., and Schuman, T.,
“Understanding the Orbital Transfer Vehicle Tradespace,” AIAA-2003-6370, Sept. 2003.
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The Tradespace
• Now have a lot of information:– Design Vector (many possible designs)
– Constants Vector
– Attributes (for each design)
– Utilities (for each design)
– Cost (for each design)
• This is, collectively, the tradespace
Need to distill this information into knowledge
useful to decision makers
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Space Tug Tradespace
Aggregated Utility vs. Cost
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0
500
1000
1500
2000
2500
3000
3500
4000
0.0 0.2 0.4 0.6 0.8 1.0
Utility (dimensionless)
Co
st
($M
)
Biprop
Cryo
Electric
Nuclear
Each point is a complete design
Co
st
($M
)
The Pareto Front
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0
500
1000
1500
2000
2500
3000
3500
4000
0.0 0.2 0.4 0.6 0.8 1.0
Utility (dimensionless)
Co
st
($M
)
Biprop
Cryo
Electric
Nuclear
Points on the front give best Utility for Cost
Other points are dominated - better utility for same cost or
same utility for less cost available
Pareto
FrontDominated
Solutions
“Good” Direction
Co
st
($M
)
Understanding Design Choices
• Propulsion system
identified by color
• Different parts of
the Pareto front
favor different
propulsion
systems
Identifying design choices by color identifies
good/bad choices, sensitivities
0
500
1000
1500
2000
2500
3000
3500
4000
0.0 0.2 0.4 0.6 0.8 1.0
Utility (dimensionless)
Co
st
($M
)
Biprop
Cryo
Electric
Nuclear
Co
st
($M
)
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Understanding Design Choices:
Example II
• Interface system capability identified by color
• More capability costs more money
• Low capability (=small) designs dominate low cost/utility systems
• Only a meaningful choice at the high utility end
0
500
1000
1500
2000
2500
3000
3500
4000
0.0 0.2 0.4 0.6 0.8 1.0
Utility (dimensionless)
Co
st
($M
)
Low Capability
Medium Capability
High Capablity
Extreme Capability
Interdependency of choices a sign
of a “rich” tradespace
Co
st
($M
)
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Understanding Design Choices:
Example III
• Line represents a set of designs differing only by fuel/reaction mass load
• More fuel, more utility but very non-linear
• At high end, hit “walls” – some are physics
(rocket equation)
– some utility (electric vehicles can “max out” the utility functions)
Physical phenomena create
patterns; must look at full data set to
find root causes
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
3500.00
4000.00
0.00 0.20 0.40 0.60 0.80 1.00
Utility (dimensionless)
Co
st
(M$
)
Low Biprop
Medium Biprop
High Biprop
Extreme Biprop
Low Cryo
Medium Cryo
High Cryo
Extreme Cryo
Low Electric
Medium Electric
High Electric
Extreme Electric
Low Nuclear
Medium Nuclear
High Nuclear
Extreme Nuclear
Co
st
($M
)
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Tradespace Reveals Promising
Designs
Sets of “good” designs share features/architectures
Often insight is emergent - the tradespace can reveal
good design concepts
•Small, light, cheap vehicles with low-mid V capability (tenders)
•Small, slow, high-V electric propulsion vehicles (cruisers)
•Big, fast vehicles require nuclear power (expensive)
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Structuring Exploration: Question-
Guided Approach
• Decade of tradespace research encapsulated in papers and theses, but “art” of exploration resided in experts
• Need for codifying exploration in order to mature the method and aid in deployment
• Proposed series of case study explorations guided by inquiry (i.e., “questions”) in order to codify expertise (through hypothetical “user” sessions)
– Activity uses VisLab* software and pre-populated database
– During a TSE session, users will seek to answer the following questions:
Questions guide the exploration of the datasets, helping to select the proper tools and
representations for knowledge-generation
*VisLab software is MATLAB®-based, in-house analysis and visualization program for interactive TSE,
but any suite of applications that can perform the necessary functions can be used
1. Can we find “good value” designs?
2. What are strengths and weakness of selected designs?
3. Are lower cost designs feasible?
4. What about time and change?
5. What about uncertainty
6. How can detailed design development be initiated to have increased chance of program success?
Find details in: Ross, A.M., McManus, H.L., Rhodes, D.H., and Hastings, D.E., "Revisiting the Tradespace Exploration Paradigm: Structuring the
Exploration Process," AIAA Space 2010, Anaheim, CA, September 2010.
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Finding “Compromises” Across Missions
and Decision Makers
Discover “best” alternatives for individual missions, as well as “efficient” compromises
1 2 3 4 5 6 7 8
x 107
0.65
0.7
0.75
0.8
0.85Epoch 171 Only Valid Designs
Lifecycle Cost
Image U
tilit
y
1 2 3 4 5 6 7 8
x 107
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9Epoch 171 Only Valid Designs
Lifecycle Cost
Tra
ck U
tilit
y
3435
6027
21701
21697
13929
13925
13921
6038
3758
3446
21701
21697
13929
13925
13921
6038
3758
3446
6153
6149
6145
1285
6153
6149
6145
1285
6003
5967
3887
3883
3877
3519
3483
3411
3375
6003
5967
3887
3883
3877
3519
3483
3411
3375
1287 3433 3434 3436 3445 3757 6025 6026 6028
6037 3363 3399 3447 3555 3559 3879 5955 5991
6029 3769 6147 6469 6741
Imaging Tracking
Joint
Compromise
Pareto Efficient SetsMission A
Mission B
Method provides quantitative approach for discovering “best”
mission-specific designs, as well as “efficient” (benefit at cost)
compromises across missions and decision makers
“Best” for
mission
“Best” for
mission
“Efficient”
compromise
Mission
A
Mission
B
Dec. M
ake
r A
Dec. M
ake
r B
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Role-Playing Session
• Decision makers filled out preferences in advance using a provided worksheet that constrained preferences to derive from existing model data set
• 2 hour role-playing session using VisLab 1.0 with the following tasks:– Find “best” designs per mission
– Seek “compromise” solutions across missions
– Vary mission priorities (weights) and repeat
– Vary mission acceptance ranges (time permitting)
– Vary mission contexts (time, capability permitting)
Preferences
changing during
negotiation
Motivation: Wanted to test proposed methods using decision makers, not analysts
Hypothesis: real-time database
interaction with multiple
simultaneous decision makers will
allow for feedback between
preference updating and “favorite”
solutions, allowing for better
compromises
For details see: Ross, A.M., McManus, H.L., Rhodes, D.H., and Hastings, D.E., "A Role for Interactive Tradespace
Exploration in Multi-Stakeholder Negotiations," AIAA Space 2010, Anaheim, CA, September 2010.
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Typical Benefits:Assessing Changing Requirements
Space Tug example:
added requirement for rapid response
drastically lowers utility of
electric propulsion designs
User needs change, so
Utilities recalculated
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Example MATE Benefits
• Forces alignment of solutions to needs
• Reveals structure of design-value spaces not apparent with few point designs
– Akin to graphing calculator showing function shapes, tradespace gives insight/intuition into complex design-value space relationships
• Facilitates cross-domain socio-technical conversation
• Ability to discover compromise solutions
– Beyond “optimized” per stakeholder solutions
– Experts often unable to find “suboptimal” solution that may be better compromise across stakeholders
• Structured means for considering large array of possible futures for discovering robust systems and strategies
The following strengths of MATE were identified by a user of the method
MATE highlights and helps to focus attention on important trades, possibly
overlooked by traditional methods
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Tradespace Exploration Paradigm:
Avoiding Point Designs
Cost
Utility
Tradespace exploration enables big picture understanding
Differing types of “trades”
1. Local point solution trades
2. Multiple points with trades
3. Frontier solution set
Designi = {X1, X2, X3,…,Xj}
4. Full tradespace exploration
0. Choose a solution
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Value-Driven Conceptual Design
Through Tradespace Exploration
Value-centric perspective enables unified evaluation of technically diverse system concepts
Operationalized through the application of decision theory to engineering design -- quantifies benefits, costs, and risks
Value is a measure of net benefit specified by a stakeholder
Tradespace exploration uses computer-based models to compare many alternatives on a common basis
– Avoids limits imposed by myopic local point solutions
– Maps decision maker preference structure to potential designs
– Reveals conflicts and confluence in delivering value to stakeholders
A MATE study can be conducted at different levels of effort and should
be scaled appropriately
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Summary
1. Scoping and bounding the mission
2. Choosing attributes
3. Choosing design variables
4. Modeling and evaluating concepts
5. Creating the tradespace
6. Conducting tradespace exploration
MATE includes an ordered set of steps for
creating an information-rich tradespace:
The goals of concept design and tradespace exploration are the extraction
of knowledge and the identification of potentially valuable solutions
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Decade of Foundational Research in
Tradespace Exploration (TSE)
• Model-based, high-level assessment of system capabilities
• Ideally, many designs assessed
• Avoids optimized point solutions that will not support evolution in
environment or user needs
• Provides basis to explore technical and policy uncertainties
• Provides a way to assess the value of potential capabilities
A method for understanding complex solutions to complex problems
Better informs “upfront” decisions and planning
Evolved through 14 multi-faceted case studies using method
Importance lies in power to generate knowledge and to engage senior decision makers in effective dialogue and negotiations
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Changing the Picture
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Increased knowledge (including understanding of uncertainties) and
value-focused thinking allows for better up front decisions
Classic decision impacts New paradigm decision impacts
APPENDIX
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MATE Applications
Name Num DV Num Att Size TradespaceA-TOS 7 2 1380
B-TOS 6 5 4033
C-TOS NA 5 1
X-TOS 9 5 50488
SDB 5 2-4 8700+8700+35000
SBR 5 7 1872
Space Tug 3-5 3 256
TPF 8-10 4-6, 2-3 10611
JDAM 8-11 5-8, 1-3 7151
ORDSS 5,7 10 2340+8640
SRS 10 5,5,5 23328x245
ORS 6 6 1100+1100+1100
Chicago Exp 10 3,3,5 3.6x109
MarSec 12-14 6, 6, 4 16x106x11520
There have been 14 case studies as of June 2012
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A-TOSSwarm making in-situ ionosphere measurements
In Situ Ionospheric Measurements
DESIGN VARIABLES (7)
• Bulk Orbit Variables– Swarm inclination (deg) 63.4
– Swarm perigee altitude (km) 200 – 800
– Swarm apogee altitude (km) 200 – 800
– Swarm argument of perigee (deg) 0
– Number of orbit planes 1
– Swarms per plane 1
• Swarm Orbit Variables
– Subsats per swarm 1 – 26
– Number of subplanes in each swarm 1 – 2
– Number of suborbits in each subplane 1 – 4
– Yaw angle of subplanes (deg: vector) 60
– Maximum satellite separation (km) 0.001 – 200
• Non-orbit Variables– Mothership (yes/no)
ATTRIBUTES (2)– “Value” in Low latitude mission, “Value” in High latitude mission
Number of Designs Explored: 1380
0 2 4 6 8 10 120
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Life Cycle Cost, $100M
To
tal U
tility
Life Cycle Cost vs. Total Utility (N=1380)
0 2 4 6 8 10 120
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 120
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Life Cycle Cost, $100M
To
tal U
tility
Life Cycle Cost vs. Total Utility (N=1380)
Valu
e
Value
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B-TOSHills swarm making ionosphere soundings
Top-side sounder swarm
Number of Designs Explored: 4033
0.98
0.985
0.99
0.995
1
100 1000
Lifecycle Cost ($M)
Uti
lity
A
ED
C
B
DESIGN VARIABLES (6)
• Large Scale Arch– Circular orbit altitude (km) 1100, 1300
– Number of Planes 1, 2, 3, 4, 5
• Swarm Arch– Number of Swarms/Plane 1, 2, 3, 4, 5
– Number of Satellites/Swarm 4, 7, 10, 13
– Radius of Swarm (km) 0.18, 1.5, 8.75, 50
• Vehicle Arch– 5 Configuration Studies Trades payload,
communication, and
processing capability
ATTRIBUTES (5)– Spatial Resolution (deg2), Revisit Time (min), Latency (min), Accuracy (deg), Inst.
Global Coverage (%),
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C-TOSICE design of B-TOS like vehicles*
Number of Designs Explored: 1All dimensions in meters
All dimensions in meters
System Engineer
SubsystemEngineer
SubsystemEngineer
SubsystemEngineer
SubsystemEngineer
SubsystemEngineer
SubsystemEngineer
SubsystemEngineer
ICEMakerPerformance Specifications
Performance EvaluationParameters
Interface Parameters (I.P.s)
I.P.sI.P.s
I.P.s
I.P.s
I.P.sI.P.s
I.P.s
Daughters
Mother
Daughters
Velocity
Vector
*Design similar to “D”
from B-TOS
seari.mit.edu © 2014 Massachusetts Institute of Technology 47
X-TOSSingle-Vehicle in-situ density measurements
Number of Designs Explored: 50488
DESIGN VARIABLES (9)
• Mission Design– Scenario 1 sat, single launch
2 sats, sequential launch
2 sats, parallel launch
• Orbital Parameters– Apogee altitude (km) 200-2000
– Perigee altitude (km) 150-350
– Orbit inclination (deg) 0, 30, 60, 90
• Physical Spacecraft Parameters– Antenna gain low, high
– Communication architecture TDRSS, AFSCN
– Propulsion type electric, chemical
– Power type solar, fuel cell
– Delta_v (m/s) 200-1000
ATTRIBUTES (5)– Data lifespan (mos), Sample altitude (km), Latitude diversity (deg), Equatorial time
(hr/day), Latency (hrs)
Total Lifecycle Cost ($M2002)
seari.mit.edu © 2014 Massachusetts Institute of Technology 48
Space TugGeneral purpose orbital service vehicle
Number of Designs Explored: 137
DESIGN VARIABLES (3)
• Physical Spacecraft Parameters– Tugging Capability low, med, high, extreme
– Propulsion Type storable-bi, cryogenic, electric, nuclear
– Propellant Mass (kg) 0-50000
ATTRIBUTES (3)– Capability (kg), DeltaV (m/s), Fast (y/n)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 500 1000 1500 2000 2500 3000 3500 4000
Cost ($M)
Uti
lity
(d
imen
sio
nle
ss)
Biprop
Cryo
Electric
Nuclear
0
10
20
30
40
50
60
70
80
Nu
mb
er
Of S
ate
llit
es
<4
0
40
-42
42-4
4
44
-46
46
-48
48-5
0
50-5
2
52
-54
54
-56
56-5
8
58-6
0
60
-62
62
-64
64
-66
66-6
8
68
-70
70
-72
72
-74
74-7
6
76
-78
78
-80
80-8
2
82
-84
84
-86
86
-88
88-9
0
90
-92
92
-94
94-9
6
96-9
8
98
-100
10
0-1
02
102
-104
10
4-1
06
10
6-1
08
10
8-1
10
110
-112
11
2-1
14
11
4-1
16
116
-11
8 200-300
700-800
1200-1300
Inclination[deg]
Altitude [k
m]
70-80
60-70
50-60
40-50
30-40
20-30
10-20
0-10
0
10
20
30
40
50
60
70
80
Nu
mb
er
Of S
ate
llit
es
<4
0
40
-42
42-4
4
44
-46
46
-48
48-5
0
50-5
2
52
-54
54
-56
56-5
8
58-6
0
60
-62
62
-64
64
-66
66-6
8
68
-70
70
-72
72
-74
74-7
6
76
-78
78
-80
80-8
2
82
-84
84
-86
86
-88
88-9
0
90
-92
92
-94
94-9
6
96-9
8
98
-100
10
0-1
02
102
-104
10
4-1
06
10
6-1
08
10
8-1
10
110
-112
11
2-1
14
11
4-1
16
116
-11
8 200-300
700-800
1200-1300
Inclination[deg]
Altitude [k
m]
70-80
60-70
50-60
40-50
30-40
20-30
10-20
0-10
LEO Targets
seari.mit.edu © 2014 Massachusetts Institute of Technology 49
Terrestrial Planet Finder (TPF) A “Big Science” Project
Number of Designs Explored: 10611
DESIGN VARIABLES (8+2)• Orbit Type {L2, LO, DA}
• Num Apertures 4,6,8,10
• Wavelength 7,10,20 microns
• Interferometer Type {sci, ssi, tsi}
• Aperture type {circular optics, strip optics}
• Aspect Ratio {multi, const}
• Aperture Size Variable
• Interferometer Baseline Variable
• [Schedule] [0-1 for each obs type]
• [Design Lifetime] 5 or 10
ATTRIBUTES (4+2, 2+1)• Science
– Num of Surveys (num), Num Medium Spectroscopies (num), Num Deep
Spectroscopies (num), Number of Images (um) [Num Long Baseline Images
(num), Num Short Baseline Images (num),
• Agency
– Lifecycle Cost ($M), Operational Lifetime (years), [Annual Ops Cost $M)]
From Ross, 2006
“Fixed”
Schedule
“Optimal”
Schedule
seari.mit.edu © 2014 Massachusetts Institute of Technology 50
Operationally Responsive Disaster Surveillance System
A Multi-Concept Responsive System
satellites
swarms
aircraft
DESIGN VARIABLES (5+7)
• Aircraft DV– Configuration Flag 1-13 (3x jet, 2x small prop, 2x med UAV, small UAV, existing
Cessna, Orion, Predator, ScanEagle, Global Hawk)
– Gross Weight Flag low, medium, high
– Number of Assets 1-6
– Payload Type visible, infrared
– Aperture Size 0.01, 0.02, 0.04, 0.07, 0.08 m
• Spacecraft DV– Deployment Strategy 1-4 (on-orbit, launch-ready, pre-fab parts, classic design)
– Altitude 120-1100 km
– Inclination 0,23,90,sun-synch
– Number of assets 1-5
– Payload Type visible, infrared
– Excess Delta-V 600-1200 m/s
– Ops Lifetime 5-10 yrs
ATTRIBUTES (10): Firefighter/Owner– Acquisition Cost ($M), Price/day ($K/day), Cost/day ($K/day), Responsiveness (hrs),
Time to IOC (days), Max % of AOI (%), Time to Max Coverage (min),
Time between AOI (min), Imaging Capability (NIIRS level), Data Latency (min)
Number of Designs Explored: 2340+8640
Data Latency
Decide to
“build”
Time to IOC
Request
“service”
Responsiveness
AOI_1 in
“view”
AOI_1
“target”
Time between AOI
AOI_2
“target”
Max AOI Covered
Time to Max Coverage
End of
“service”
“Service” durationCost/dayAcquisition Cost
Time of first “need” AOI(s) Type of image(s)Mission Variables
(disaster-specific)
Imaging Capability
Price/day
Data Latency
Decide to
“build”
Time to IOC
Request
“service”
Responsiveness
AOI_1 in
“view”
AOI_1
“target”
Time between AOI
AOI_2
“target”
Max AOI Covered
Time to Max Coverage
End of
“service”
“Service” durationCost/dayAcquisition Cost
Time of first “need” AOI(s) Type of image(s)Mission Variables
(disaster-specific)
Imaging Capability
Price/day
Witch Creek Fire
October 2007
Image * from http://www.boeing.com/companyoffices/gallery/images/scaneagle/dvd-1390-1.html
*
seari.mit.edu © 2014 Massachusetts Institute of Technology 51
Satellite Radar SystemSatellite-based radar multi-mission system of system
U
0
Epoch i
TiU
0
Epoch i
TiTj
Epoch jU
0
Tj
Epoch jU
0
Epoch jU
0
U
0
Epoch i
TiU
0
Epoch i
TiTj
Epoch jU
0
Tj
Epoch jU
0
Epoch jU
0
U
0
Epoch i
TiU
0
Epoch i
TiTj
Epoch jU
0
Tj
Epoch jU
0
Epoch jU
0
U
0
Epoch i
TiU
0
Epoch i
TiTj
Epoch jU
0
Tj
Epoch jU
0
Epoch jU
0
U
0
Epoch i
TiU
0
Epoch i
TiTj
Epoch jU
0
Tj
Epoch jU
0
Epoch jU
0
U
0
Epoch i
TiU
0
Epoch i
TiTj
Epoch jU
0
Tj
Epoch jU
0
Epoch jU
0
Peak Transmit Power 1.5 10 20 [KW] 9 9 9 3 1 1 9 9 9 0 1 9 9 9 9 96
Radar Bandwidth .5 1 2 [GHz] 9 9 3 3 1 1 9 9 9 0 1 3 3 3 3 66
Physical Antenna Area 10 40 100 200 [m^2] 9 9 9 3 1 1 9 9 9 1 1 9 9 9 9 97
Antenna Type Mechanical vs. AESA 9 9 9 3 3 1 9 9 9 1 1 9 9 9 9 99
Satellite Altitude 800 1200 1500 [km] 9 9 3 9 9 3 9 9 9 9 3 1 1 1 1 85
Constellation Type 8 Walker IDs 0 0 1 9 9 3 0 0 3 9 3 9 9 9 9 73
Comm. Downlink Relay vs. Downlink 0 0 0 0 0 9 0 0 0 0 9 9 9 3 9 48
Tactical Downlink Yes vs. No 0 0 0 0 3 9 0 0 0 0 9 9 9 3 9 51
Maneuver Package 1x, 2x, 4x 1 1 1 1 1 0 1 1 1 1 0 9 3 3 3 27
Constellation Option none, long-lead, spare 0 0 0 0 0 0 0 0 0 0 0 9 9 9 9 36
Baseline S
chedule
Actu
al Schedule
(Era
)
To
tal Im
pact
Tracking Imaging
Min
. D
iscern
able
Velo
city
Num
ber
of Targ
et
Boxes
Targ
et
ID T
ime
Targ
et
Tra
ck L
ife
Tra
ckin
g L
ate
ncy
Mission
ATTRIBUTES
Schedule
Programmatics
Cost
Definition RangeVariable Name Min
imum
Targ
et
RCS
DES
IG
N V
AR
IA
BLES
Baseline C
ost
Actu
al Costs
(Era
)
Imagin
g L
ate
ncy
Resolu
tion (
Pro
xy)
Targ
ets
per
Pass
Fie
ld o
f Regard
Revis
it F
requency
Satellite Radar SystemProgram Manager
comptroller
Nation
SI&E
SRS Enterprise Boundary
Capital(non-fungible assets)
Capital(non-fungible assets)
National Security Strategy/Policy
National Security Strategy/Policy
Resources(fungible assets)
Resources(fungible assets)
RadarProduct
RadarProduct
DNI
NGAJ2
Military
USD(I)
ExtendedSRS
Enterprise
SRS Context
OMB
Congress
Which SRS Architecture?
R&DR&D Comm/GrndComm/Grnd
Infr
a-St
ruct
.
0 1 2 3
x 104
0
20
40
60
80
100
Par
eto
Tra
ce
Design ID
image v. cost
0 1 2 3
x 104
0
20
40
60
80
100
120
Par
eto
Tra
ceDesign ID
track v. cost
0 1 2 3
x 104
0
20
40
60
80
100
120
Par
eto
Tra
ce
Design ID
combined v. cost
1.5 2 2.5
x 104
0
20
40
60
80
Par
eto
Tra
ce
Design ID
image v. track
0 1 2 3
x 104
0
20
40
60
80
100
120
Par
eto
Tra
ce
Design ID
image v. track v. cost
DESIGN VARIABLES (10)
• Orbit DV– Orbit Altitude 800, 1200, 1500 km
– Constellation design 8 Walker IDs
• Spacecraft DV– Antenna Size 40, 80, 100 m2
– Peak power 1.5, 10, 20 kW
– Radar bandwidth 0.5, 1, 2 GHz
– Comm Arch relay, downlink
– Tactical Downlink yes/no
– Path enablers (Extra sats, fuel)
EPOCH VARIABLES (6)– Technology avail, Comm infrastructure, Target list, AISR avail,
Environment, Mission priorities
ATTRIBUTES (3 “missions”, 15 total)– Tracking, Imaging, Programmatic
Number of Designs Explored: 23328; Number of Epochs Explored: 245
seari.mit.edu © 2014 Massachusetts Institute of Technology 52