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SEAri Short Course Series Course: PI.27s Value-driven Tradespace Exploration for System Design Lecture: Lecture 7: Case Studies in Tradespace Exploration Author: Adam Ross and Donna Rhodes Lecture Number: SC-2009-1-7-1 Revision Date: August 11, 2009 A slightly earlier version of this course was taught at PI.27s as a part of the MIT Professional Education Short Programs in June 2009 in Cambridge, MA. The lectures are provided to satisfy demand for learning more about Multi-Attribute Tradespace Exploration, Epoch-Era Analysis, and related SEAri-generated methods. The course is intended for self-study only. The materials are provided without instructor support, exercises or “course notebook” contents. Do not separate this cover sheet from the accompanying lecture pages. The copyright of the short course is retained by the Massachusetts Institute of Technology. Reproduction, reuse, and distribution of the course materials are not permitted without permission.

PI.27s VALUE-DRIVEN TRADESPACE EXPLORATIONFOR SYSTEM DESIGN

Lecture 7Case Studies in Tradespace Exploration

Dr. Donna Rhodes and Dr. Adam M. RossMIT

seari.mit.edu © 2009 Massachusetts Institute of Technology 2

Outline• Case studies• Look out for Artifacts

– Discretization– Modeling– Mathematical

• Find Root Causes of Real Patterns– Physics– Design Differentiators

• Explore the Pareto Front– What is there and Why?

• Explore Alternate Views– A cautionary tale

• Drill down on some designs– Additional insights from more detailed models


Example TSE 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 TSE were identified by a user of the method

MATE highlights and helps to focus attention on important trades, possibly overlooked by traditional methods


Case StudiesName Num DV Num Att Size TradespaceA-TOS 7 2 1380B-TOS 6 5 4033C-TOS NA 5 1X-TOS 9 5 50488SDB 5 2-4 8700+8700+35000SBR 5 7 1872Space Tug 3 3 137TPF 8-10 4-6, 2-3 10611JDAM 8-11 5-8, 1-3 7151RDSS 5,7 10 2340+8640SRS 10 5,5,5 23328x245ORS 6 6 1100+1100+1100HSR 15 20 TBD

There have been 13 case studies as of June 2009


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

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0.9

1

Life Cycle Cost, $100M

Tota

l Util

ity

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

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0.8

0.9

1

Life Cycle Cost, $100M

Tota

l Util

ity

Life Cycle Cost vs. Total Utility (N=1380)

Valu

e

Value


B-TOSHills swarm making ionosphere soundings

Top-side sounder swarm


0.98

0.985

0.99

0.995

1

100 1000

Lifecycle Cost ($M)

Util

ity

A

EDC

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 (%),


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

MotherDaughters

Velocity

Vector

*Design similar to “D” from B-TOS


X-TOSSingle-Vehicle in-situ density measurements


DESIGN VARIABLES (9)• Mission Design

– Scenario 1 sat, single launch2 sats, sequential launch2 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)


Space TugGeneral purpose orbital service vehicle


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

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0 500 1000 1500 2000 2500 3000 3500 4000

Cost ($M)

Util

ity (d

imen

sion

less

)

Biprop CryoElectricNuclear

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10

20

30

40

50

60

70

80

Num

ber O

f Sat

ellit

es

<40

40-4

242

-44

44-4

646

-48

48-5

050

-52

52-5

454

-56

56-5

858

-60

60-6

262

-64

64-6

666

-68

68-7

070

-72

72-7

474

-76

76-7

878

-80

80-8

282

-84

84-8

686

-88

88-9

090

-92

92-9

494

-96

96-9

898

-100

100-

102

102-

104

104-

106

106-

108

108-

110

110-

112

112-

114

114-

116

116-

118 200-300

700-800

1200-1300

Inclination[deg]

70-8060-7050-6040-5030-4020-3010-200-10

0

10

20

30

40

50

60

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80

Num

ber O

f Sat

ellit

es

<40

40-4

242

-44

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646

-48

48-5

050

-52

52-5

454

-56

56-5

858

-60

60-6

262

-64

64-6

666

-68

68-7

070

-72

72-7

474

-76

76-7

878

-80

80-8

282

-84

84-8

686

-88

88-9

090

-92

92-9

494

-96

96-9

898

-100

100-

102

102-

104

104-

106

106-

108

108-

110

110-

112

112-

114

114-

116

116-

118 200-300

700-800

1200-1300

Inclination[deg]

70-8060-7050-6040-5030-4020-3010-200-10

LEO Targets


Terrestrial Planet Finder (TPF) A “Big Science” Project


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


Broad-Band Communication Architectures for future communication nets

DESIGN VARIABLES (6)• Altitude• Inclination• Satellites per plane• Number of planes• Power• Antenna Area

ATTRIBUTES (1)• Total System Subscriber Time

[Boeing, 2002]

Many designs explored, emphasis placed on 13 Pareto front architectures


Operationally Responsive Disaster Surveillance System

A Multi-Concept Responsive System

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” duration Cost/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” duration Cost/dayAcquisition Cost

Time of first “need” AOI(s) Type of image(s)Mission Variables (disaster-specific)

Imaging Capability

Price/day

Witch Creek FireOctober 2007

Image * from http://www.boeing.com/companyoffices/gallery/images/scaneagle/dvd-1390-1.html

*


Satellite Radar SystemSatellite-based radar multi-mission system of system

Tj

Epoch jU

0

Tj

Epoch jU

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Epoch jU

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Epoch i

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Epoch i

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Peak Transmit Power 1.5 10 20 [KW] 9 9 9 3 1 1 9 9 9 0 1 9 9 9 9 96Radar Bandwidth .5 1 2 [GHz] 9 9 3 3 1 1 9 9 9 0 1 3 3 3 3 66Physical Antenna Area 10 40 100 200 [m^2] 9 9 9 3 1 1 9 9 9 1 1 9 9 9 9 97Antenna Type Mechanical vs. AESA 9 9 9 3 3 1 9 9 9 1 1 9 9 9 9 99Satellite Altitude 800 1200 1500 [km] 9 9 3 9 9 3 9 9 9 9 3 1 1 1 1 85Constellation Type 8 Walker IDs 0 0 1 9 9 3 0 0 3 9 3 9 9 9 9 73Comm. Downlink Relay vs. Downlink 0 0 0 0 0 9 0 0 0 0 9 9 9 3 9 48Tactical Downlink Yes vs. No 0 0 0 0 3 9 0 0 0 0 9 9 9 3 9 51Maneuver Package 1x, 2x, 4x 1 1 1 1 1 0 1 1 1 1 0 9 3 3 3 27Constellation Option none, long-lead, spare 0 0 0 0 0 0 0 0 0 0 0 9 9 9 9 36

Bas

elin

e S

ched

ule

Act

ual S

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ule

(Era

)

Tota

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pac

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Tracking Imaging

Min

. D

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of T

arge

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Targ

et I

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ime

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Trac

king

Lat

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MissionATTRIBUTES

ScheduleProgrammaticsCost

Definition RangeVariable Name Min

imum

Tar

get

RC

S

DES

IGN

VA

RIA

BLE

S

Bas

elin

e C

ost

Act

ual C

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(Er

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(Pro

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Satellite Radar SystemProgram Manager

comptroller

Nation

SI&E

SRS Enterprise Boundary

Capital(non‐fungible assets)

Capital(non‐fungible assets)

National Security Strategy/PolicyNational Security Strategy/Policy

Resources(fungible assets)

Resources(fungible assets)

RadarProductRadarProduct

DNI

NGAJ2

Military

USD(I)

ExtendedSRS

Enterprise

SRS Context

OMBCongress

Which SRS Architecture?

R&DR&D Comm/GrndComm/Grnd

Infra‐

Struct.

0 1 2 3

x 104

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40

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100

Par

eto

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im age v. c os t

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Par

eto

Trac

e

D es ign ID

c om bined v. c os t

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


Tradespace Exploration

• Begins with verification of tradespace– Remove errors and biases from model– Gain confidence of results– Identify and compensate for artifacts

• Look at patterns– Display variations in…

• Design variables• Attributes• Single attribute utilities

– Identify expected and emergent trades


Artifacts

• Some tradespace “features” may be caused by artifacts of:– Discretization: to keep design space finite, some variables to

which the utility is sensitive may be coarsely surveyed, resulting in a grainy or stripy tradespace

– Modeling: to keep model tractable, some phenomena to which the utility is sensitive may be coarsely modeled, resulting in similar effects

– Mathematics: modeling or utility math may create unexpected or strange-seeming results

• Pathological - unconverged iterations, implicit formulae without solutions

• Unexpected but correct behavior of utility functions, normalizations, etc.

This is not a compete list!


Discretization Artifacts (very common)

SPACETUG• General

purpose orbit transfer vehicles

Utility-Cost Discriminated by Tugging Capability

Utility (dimensionless)

0

500

1000

1500

2000

2500

3000

3500

4000

0.0 0.2 0.4 0.6 0.8 1.0

Low Capability Medium CapabilityHigh CapablityExtreme Capability

Bands due to discrete levels of capability

Cos

t ($M

)


Discretization Artifacts (very common)

SPACETUG• General


Utility-Cost Discriminated by Tugging Capability


0

500

1000

1500

2000

2500

3000

3500

4000

0.0 0.2 0.4 0.6 0.8 1.0

Low Capability Medium CapabilityHigh CapablityExtreme Capability

Bands due to discrete levels of capability

Cos

t ($M

)


Modeling Artifacts: B-TOS Swarm

0.98

0.985

0.99

0.995

1

100 1000

Lifecycle Cost ($M)

Util

ity

A

EDC

B

Max Baseline Length

• Design Vector includes number of Vehicles in Swarm

• Vehicles populate rings in Hills orbit around central vehicle

• Model assumes only FULL rings add to quality of data

Ring

Vehicle

• Tradespace shows steps in utility corresponding to filled rings

• Designs with unfilled rings have less utility at the same cost

Question - is this modeling assumption correct??


• B-TOS utilities run from .98 to 1• Intuition (incorrectly) concludes all designs are good• Artifact of Utility Function

– One attribute weighted very highly by user is always satisfied

– Non-linear aggregate utility function creates utilities shown

– Could have removed attribute from tradespace to rescale utility (made a constraint)

0.98

0.985

0.99

0.995

1

100 1000

Lifecycle Cost ($M)

Util

ity

A

EDC

B

1.000

0.995

0.990

0.985

0.980Lifecycle Cost ($M)100 1000

Utility

Mathematical Artifacts


Root Causes of Patterns

• Physics– Simple patterns can be seen by looking at variation in one

design variable (e.g. space tug fuel load vs. rocket equation)– Some patterns are more difficult to discern - detective work

(e.g. drag/altitude/life/fuel load trades in X-TOS)– Often confounded with modeling artifacts

• Design discriminators– Obvious good/bad patterns (some design choices

dominated) - be careful of these– Trades (design choices in different parts of the trades space

and/or Pareto Front)• Decision maker needs

– May require impossible/unrealistic performance



Understanding Limiting Physical or Mission constraints

Hits a “wall” of either physics (can’t change!) or utility (can)

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

Low BipropMedium BipropHigh BipropExtreme BipropLow CryoMedium CryoHigh CryoExtreme CryoLow ElectricMedium ElectricHigh ElectricExtreme ElectricLow NuclearMedium NuclearHigh NuclearExtreme Nuclear

SPACETUG• Lines show increasing

fuel mass fraction• Past a certain point,

add only cost (no more utility)

• Some of this is Rocket Equation (big vehicles need more fuel to push themselves around

• Some is “maxing out” of utility function -extra fuel adds no further utility

Cos

t ($M

)

Utility-Cost Discriminated by Capability, Prop Subsys, and Fuel Mass Fraction


Prop SubsysLowMediumHighExtreme

CapabilityBipropCryoElectricNuclear


Understanding Trades

Total Lifecycle Cost

($M2002)

X-TOS single vehicle• Pareto designs collect low

altitude data (most valuable) at the expense of lifetime

• On the Pareto Front, a complex trade of altitude, lifetime, fuel load, and cost

• Depends on the details of the utility functions -different trades for different preferences

Each point is a specific design

Pareto Front of “best” designs “Dominated”

designs are inferior in cost or utility

Warning: “good” is not always in the upper left (read the axes!)

“Good” Direction


Confounding of Real and Artificial Effects

0.98

0.985

0.99

0.995

1

100 1000

Lifecycle Cost ($M)

Util

ity

A

EDC

B

Max Baseline Length

• Vehicles populate rings in Hills orbit around central vehicle

• Model assumes only FULL rings add to quality of data

• Real physics DOES favor filled rings, but is it this binary?

Ring

Vehicle

• Tradespace shows steps in utility corresponding to filled rings

• Designs with unfilled rings have less utility at the same cost

Question - how much is modeling, how much real physics?


Design Choices


Total Lifecycle Cost ($M2002)

• Full X-TOS tradespace includes concepts with multiple vehicles

• Multi-vehicle designs add a lot of cost, some utility; might prompt decision-maker reassessment

• Be careful: reassess in terms of attributes, and check DM set, before making “easy” decisions

Single VehicleDesigns

Multi-VehicleDesigns


Architecture Tradeoffs

TPF• Terrestrial Planet

Finder - a large astronomy system

• Connected and separated architectures clearly separated into to “islands”

• Separated architectures only appropriate for large, high-performance, expensive mission

Image from Beichman et al, 1999, tradespace from Jilla, 2002

Separated

Connected


Tracing the Pareto Front

• Examining the designs on the Pareto Front yields information on:– Best designs in various regions - often a region is

dominated by a set of similar designs– Trades in various regions - within each region, a

dominant cost/utility trade may exist• Look for artifacts, physical limitations, and

design decisions behind the observed patterns

This is not a compete description!


Different Parts of the Pareto Front have Different “Best” Concepts

SPACETUG• General



Tracing the Space Tug Pareto Front

• Tenders:– Small, light, inexpensive vehicles– Low capability– Any propulsion except nuclear– Trade is utility vs. cost, driver is fuel load

• Cruisers– Small, light, inexpensive vehicles– But high Delta-V– Only possible with high Isp propulsion (electric)

• Monsters– Desire for large, capable, fast vehicles can only be met by

using high thrust, high Isp propulsion (nuclear)– Trade is capability vs. cost, driver is manipulator mass


TPF Pareto Front

Many different designs on the Pareto Front

Several detailed studies were of dominated solutions


Terrestrial Planet Finder Pareto Designs

# “Images” LCC ($B) Orbit (AU) # Apert.’s Architecture Apert. Diam. (m)

502 0.743 1.5 4 SCI-1D 1 577 0.762 2.0 4 SCI-1D 1 651 0.767 2.5 4 SCI-1D 1 1005 0.768 1.5 4 SCI-1D 2 1114 0.788 2.0 4 SCI-1D 2 1171 0.790 2.5 4 SCI-1D 2 1195 0.807 1.5 6 SCI-1D 2 1292 0.811 1.5 6 SCI-2D 2 1317 0.830 1.5 8 SCI-1D 2 1424 0.836 2.0 4 SCI-1D 3 1426 0.838 1.5 8 SCI-2D 2 1464 0.867 2.5 6 SCI-2D 2 1631 0.877 1.5 6 SCI-1D 3 1684 0.881 1.5 6 SCI-2D 3 1687 0.932 2.0 6 SCI-1D 3 1828 0.936 2.0 6 SCI-2D 3 1881 0.980 1.5 8 SCI-2D 3 1978 0.982 1.5 6 SCI-1D 4 2035 1.086 2.0 8 SCI-2D 3 2132 1.112 1.5 8 SCI-1D 4 2285 1.120 1.5 8 SCI-2D 4 2328 1.190 2.5 6 SCI-2D 4 2398 1197 3.0 6 SCI-2D 4 2433 1.212 4.0 6 SCI-2D 4 2472 1.221 4.5 6 SCI-2D 4 2482 1.227 5.0 6 SCI-2D 4 2487 1.232 5.5 6 SCI-2D 4 2634 1.273 2.5 8 SCI-2D 4 2700 1.280 3.0 8 SCI-2D 4 2739 1.288 3.5 8 SCI-2D 4 2759 1.296 4.0 8 SCI-2D 4 2772 1.305 4.5 8 SCI-2D 4 2779 1.312 5.0 8 SCI-2D 4 2783 1.317 5.5 8 SCI-2D 4 2788 1.569 3.0 6 SSI-2D 4 2844 1.609 3.5 6 SSI-2D 4 2872 1.655 4.0 6 SSI-2D 4 2988 1.691 2.0 8 SSI-1D 4 3177 1.698 2.5 8 SSI-1D 4 3289 1.739 3.0 8 SSI-1D 4 3360 1790 3.5 8 SSI-1D 4 3395 1.850 4.0 8 SSI-1D 4 3551 1.868 2.5 10 SSI-1D 4 3690 1.919 3.0 10 SSI-1D 4

Family4 ap.

SCI-1D1 m Diam.

Family8 ap.

SCI-2D4 m Diam.

Family8 ap.

SSI-1D4 m Diam.

Intersection of MultipleFamilies

Family4 ap.

SCI-1D2 m Diam.

Family6 ap.

SCI-2D4 m Diam.

Family6 ap.

SSI-2D4 m Diam.

Family10 ap.SSI-1D

4 m Diam.

Transition fromSCI to SSIDesigns

Mission Cost& Performance

Low

Medium

High


Understanding the TPF Pareto Front

• A variety of connected architectures dominate the cheaper portion of the front– Trade is adding reflector numbers and size

• Separate architectures dominate the expensive high-performance systems– Trade is increasing numbers of reflectors

Remember that the Pareto Front is not the only part of the tradespace

we care about


Alternate Views

• Often views other than the simple Utility/Cost plots are needed

• There are MANY possible views– Single attribute utilities vs. cost or design vector elements– Single attribute utilities vs. each other– Plots for various stakeholders (differing utility functions)

• Use to explore suspected issues, display issues, conflicts, and trades for decision makers



Clashing Stakeholders in A-TOS

Clashing stakeholder needs discovered and illustrated

A-TOS• In-situ

measurement of upper atmosphere

• Two missions-high and low

• Plot shows best designs for low latitude missions ignore high latitude mission (!)

• Fundamental disconnect - these missions should not share a vehicle.


Terrestrial Planet Finder (TPF)A cautionary tale…

From Jilla, 2002

Jilla (2002) study used a single attribute (number of images)

This was aggregated from a number of different image types, for different science stakeholders

Study showed viable (if expensive) concepts

A “rich” tradespace used in several studies

Single attribute


Terrestrial Planet Finder (TPF) A cautionary tale…

• Recast in terms of 4 separate image types

• NO designs could meet minimal acceptable performance for some image types!

• Examining alternate view (individual attributes) exposed both the problem, and paths in the direction of solutions

This is information that decision makers need up front…


Looking at Individual Attributes

From Ross, 2006

• Look at one attribute (image type) at a time

• For short baseline attribute, designs exist within the acceptable range (0<utility<1)

• For long baseline attribute, no design meets minimum requirements

Need to:• Look at a wider design space for

solutions• Re-assess stakeholder needs

Shor

t bas

elin

e im

ages

Long

bas

elin

e im

ages

Lifecycle Cost ($M)

Lifecycle Cost ($M)

“best”

“worst”

“best”

“worst”


Expanding the Tradespace

From Ross, 2006

• In original studies, imaging sequence was assumed to be fixed

• Instead, pick optimum sequence for each design

• Move from constants vector to intermediate variable

• Result is improvement in performance (though in this case not enough)

“Fixed” Schedule

“Optimal” Schedule

Shor

t bas

elin

e im

ages

Long

bas

elin

e im

ages

Lifecycle Cost ($M)

Lifecycle Cost ($M)

“best”

“worst”

“best”

“worst”


Deeper Exploration of Promising Designs

• More designs in interesting regions• Integrated Concurrent Engineering (ICE) to

flesh out designs• Reality / feasibility check• Details may lead to further insights

– Configuration – Component availability and capabilities– External interfaces (e.g., TDRSS)

• Iterating on ICE designs may increase utility


Example: Bipropellant GEO Tug

• Approx. 1300 kg dry mass, 11700 kg wet mass• Quite big (and therefore expensive); not very practical (?);

Scale for all images:

black cylinder is 1 meter long by

1 meter in diameter

ManipulatorSystem

Solar Panels

Spacecraft Busw/subsystems

Propulsion Systemw/fuel


Electric Propulsion RT GEO Tug

• Approx. 700 kg dry mass, 1100 kg wet mass• Includes return of tug to safe orbit• A reasonable, versatile system

The “Electric Cruiser”


Bi-prop Tender Designs

• Lower Utility, lower cost systems • Can’t go to GEO (though can work there if inserted)• 700-1000 kg dry mass; 1000-4000 kg wet mass• A family of potential vehicles with reasonable sizes and mass

fractions

For Space Tug, ICE confirms general conclusions of MATE study


Exploring X-TOS Designs in Detail (with ICE)

• Original X-TOS tradespace included designs with unrealistic form factors - “telephone pole sats”

• Discovered in ICE sessions

• MATE models modified to eliminate them

• Detailed ICE iterations resulted in designs not in original MATE enumeration

• Added to modified MATE tradespace


Baseline X-TOS Design

• Est. Cost: $71.7 M• Utility*: 0.705• Wet Mass: 449.6 kg• Dry Mass: 188.9 kg• Lifetime: 0.534 years• Orbit: 185 km circular• LV: Minotaur I

* Denotes “Original” User Utility

This design was not on the original tradespace

00.05

0.10.15

0.20.25

0.30.35

0.40.45

Latency Latitude EquatorTime

Lifespan Altitude

Weight Factors of each Attribute (k values)

Original Revised


X-TOS Last Design• Est. Cost: $75.0 M• Utility*: 0.556• Wet Mass: 324.3 kg• Dry Mass: 205.5 kg• Lifetime: 2.204 years• Orbit: 300 km circular• LV: Minotaur I

* Denotes “Revised” User Utility

This design motivated by revised user preferences

00.05

0.10.15

0.20.25

0.30.35

0.40.45

Latency Latitude EquatorTime

Lifespan Altitude

Weight Factors of each Attribute (k values)

Original Revised


Good Ideas for the Art of Tradespace Exploration

• Look out for artifacts• Find root causes of real patterns• Explore the Pareto Front• Examine alternate views

– use to find or explain issues and try solutions• Drill down on some designs

Remember goal: turn data generated by model into knowledge for decisions makers.

Back up


B-TOS Swarm Geometry• Max baseline length is defined

by desired angle of arrival accuracy (.0005 degrees)

• Minimum baseline length limited by beacon frequency (100 MHz)

• Swarm suborbit spacing is a factor of 5.7 and defined by:• Phase error of the swarm

• Frequency of the beacons

• Filling the baselines ensures no ambiguity in the angle of arrival measurement

Mothership

Daughterships

Swarm Suborbits

Max Baseline Length


B-TOS Frontier Architectures

Point A B C D EAltitude (km)Num of PlanesSwarms/Plane 1 1 1 1 2Satellites/Swarm 4 7 10 13 13Swarm Radius (km) 0.18 1.5 8.75 50 50Functionality Study

<-- 1100 --><-- 1 -->

<-- #5 -->

Point A B C D EAltitude (km)Num of PlanesSwarms/Plane 1 1 1 1 2Satellites/Swarm 4 7 10 13 13Swarm Radius (km) 0.18 1.5 8.75 50 50Functionality Study

<-- 1100 --><-- 1 -->

<-- #5 -->

StudyType M DNumber 1 3+Payload (Tx) Yes NoPayload (Rx) Yes YesProcessing Yes NoTDRSS Link Yes NoIntra-Swarm Link Yes Yes

5StudyType M DNumber 1 3+Payload (Tx) Yes NoPayload (Rx) Yes YesProcessing Yes NoTDRSS Link Yes NoIntra-Swarm Link Yes Yes

5

seari.mit.edu C-TOS Technical Summary© 2009 Massachusetts Institute of Technology 49

C-TOS Mission Requirements• Altitude: 1100 km• Orbit inclination: 63.4°• Swarm shape is 2:1 ellipse• Swarm radius: (40° angular spacing between all daughters)

– inner ring (3 spacecraft) full major axis 45 km– outer ring (6 spacecraft) full major axis 90 km

• Lifetime: 18 month reliability requirements– mother; 95% (consumables for 5 years)– swarm

• 90% full swarm (9 daughters and one mother)• 95% (2 inner ring daughters, 5 outer ring, and one mother)

• Data latency: 45 minutes from payload to ground• Not designed to cost, parametric cost models• Payload and interface defined by sponsor• Daughters replaceable

seari.mit.edu C-TOS Technical Summary© 2009 Massachusetts Institute of Technology 50

C-TOS Swarm Geometry Requirements

Daughters

MotherDaughters

• All spacecraft in the swarm are coplanar• The velocity vector is on the major axis• The swarm plane is perpendicular to the surface of the earth (90° roll about the

velocity vector)

Velocity

Vector

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