IAC-02-U.5.01: Application of the Abbreviated Technology … · (ATIES) Methodology to a Mars Orbit...

Engineering Today, Enabling TomorrowSpaceWorks Engineering, Inc. (SEI)www.sei.aero

World Space Congress – 200210-19 Oct 2002/Houston, Texas

SpaceWorks Engineering, Inc. (SEI)

IAC-02-U.5.01:

Application of the Abbreviated Technology Identification, Evaluation, and Selection (ATIES) Methodology to a Mars Orbit Basing (MOB) Solar Clipper Architecture

Senior Futurist:Mr. A.C. Charania

President / CEO:Dr. John R. Olds

October 2002

Overview of the Firm

About

SpaceWorks Engineering, Inc. (SEI)www.sei.aero


SpaceWorks Engineering, Inc. (SEI) is a small aerospace engineering and consulting company located in metro Atlanta. We specialize in providing timely and unbiased analysis of advanced space concepts ranging from space launch vehicles to deep space missions.

Our practice areas include:- Space Systems Analysis- Technology Prioritization- Financial Engineering- Future Market Assessment- Policy and Media Consultation

Engineering Today, Enabling Tomorrow



From Vision to Concept

Including:- Engineering design and analysis- New concept design- Independent concept assessment- Full, life cycle analysis- Programmatic and technical analysis


Recent Firm EngagementsNASA MSFC Advanced Concepts Group: 3rd Gen RLV concept assessment and engineering tool development

NASA 2nd Gen RLV / Space Launch Initiative (SLI) Program: Advanced Engineering Environment (AEE)

NASA Headquarters: FY2002 RLV technology goals assessment

NASA inter-center Value Stream Analysis Program: Micro and macro level technology implications for 3rd Gen RLVs

NASA MSFC Integrated Technology Assessment Center (ITAC): Space transportation technology prioritization

Revolutionary Aerospace Systems Concept (RASC) Program at NASA MSFC: Database and tool development

NASA Institute for Advanced Concepts (NIAC): Phase I Award for Mars Telecommunication Networks

SAIC and NAL (Japan): ATREX engine test program performance assessment

Lockheed Martin Astronautics: Assessment of optimization codes for space transportation case studies

DARPA: Responsive Access Small Cargo Affordable Launch (RASCAL) program subcontract for performance analysis

NASA MSFC Program Planning Office: Heavy-lift launch vehicle configurations predicated on SLI technologies

White paper (available at www.sei.aero) on past case studies and future investment strategies for RLVs




Motivation



Motivation

Any envisioned future with ubiquitous space transportation systems as defined by NASA’s ASTP will rely on revolutionary improvements in the development and integration of technologies

Given the limitation of financial resources by both the government and industry, strategic decision makers need a method to assist them in the prioritization of advanced space transportation technological investment

New methods have to be developed that are proactive in forecasting the impact of new technologies, even before the maturation of those technologies

Case Study Concept Overview: Mars Orbit Basing (MOB) Solar Clipper



Concept Description

MREVs use Boeing EELV for launch, price inelastic towards Mars cargo delivery marketAll other mission payloads use next generation reusable launch vehicle: Hyperion vehicle from HRST study

Earth Launch

Based upon Transhab configuration and NASA JSC Crew And Thermal Systems Division estimatesSurface Hab is payload of MREV

Configuration:Surface / Transit Habitats (HAB)

IOC of 2026 with a program start year / technology freeze date of 20192 Solar clippers per mission, 3 missions total in program, every 4 years starting at IOC

Programmatic

Spiraling outbound and inbound trajectoryEarth-Mars-Earth round trip transit time approximately 500-600 daysSurface stays approximately 100-400 days

Trajectory

Roundtrip Payload baseline at 5 MT (science and crew)Lox/LH2 rockets with TABI blanket TPSExcursion time 14 days with 1 excursion possible per MREV

Configuration:Mars Reusable Excursion Vehicle

(MREV)

Payload is MREV (1), Transit Hab (1), and Surface Hab (1)Elliptical solar concentrators with separate PV arrays and with high power gridded ion enginesPedal shaped arrangement of concentrators on central boomBased upon SunTower Space Solar Power (SSP) Configuration

Configuration:Solar Clipper (SC)

Solar Electric Propulsion (SEP) based in-space transportation system with atmospheric transfer vehicle called a Mars Reusable Excursion Vehicle (MREV) and transit / orbital and surface habitats; based upon Mars Orbit Basing (MOB) concept as developed by John Mankins (NASA HQ) and defined in the working white paper entitled “An Advanced Concept for Affordable Human Exploration Beyond Earth Orbit Using Megawatt-Class Solar Electric Propulsion, Reusable Systems and Orbital Basing,” prepared by the Advanced Projects Office, Office of Space Flight, NASA Headquarters

Concept

CharacteristicsItem



Solar Clipper

Source: Pat Rawlings, SAIC Source: Charania, A., Tooley, J., Cowart, K., Sakai, T., Salinas, R., Sorensen, K., St. Germain, B., Wilson, S., “Mars Scenario-Based Visioning: Logistical Optimization of Transportation Architectures,” Presented at the 1999 Mars Society Conference, Boulder, CO, August 12-15, 1999.

Solar Clipper



Mars Reusable Excursion Vehicle (MREV)

Top View

Base View

SSME Derived Engines (4)

Landing Gear

Liquid Hydrogen Tank

Liquid Oxygen Tank

Crew Compartment

TABI Blankets

Gross Mass 256,700 kgDry Mass 27,800 kg

Mass Ratio 1.2903

Takeoff Thrust 501 kNCrew Complement 3



Transit and Surface Habs

Source: NASA Human Spaceflight [http://spaceflight.nasa.gov/station/assembly/elements/transhab/]

ROSETTA Model



ROSETTA Model

Reduced Order Simulation for Evaluation of Technologies and Transportation Architectures (ROSETTA)

- A spreadsheet-based meta-model that is a representation of the design process for a specific architecture (ETO, in-space LEO-GEO, HEDS, etc.)

- Each traditional design discipline is represented as a contributing analysis in the Design Structure Matrix (DSM)

- Based upon higher fidelity models (i.e. POST, APAS, CONSIZ, etc.) and refined through updates from such models

- Executes each architecture simulation in only a few secondsRequirement for uncertainty analysis through Monte-Carlo simulation

- Architectures are modified through influence factorsPIFs: Programmatic Influence Factors (i.e. govt. contribution, market growth, etc.)VIFs: Vehicle Influence Factors (i.e. Isp, wing weight, T/We, cost, etc.)

- Outputs measure progress towards NASA Goals ($/lb, safety, etc.)Standard deterministic outputs as well as probabilistic through Monte Carlo

ROSETTA models contain representations of the full design process. Individual developer of each ROSETTA model determines depth and breadth of appropriate contributing analyses.

More assumptions, fewer DSM links than higher fidelity models due to need for faster calculation speeds.

ROSETTA models contain representations of the full design process. Individual developer of each ROSETTA model determines depth and breadth of appropriate contributing analyses.

More assumptions, fewer DSM links than higher fidelity models due to need for faster calculation speeds.



Mars Orbit Basing (MOB) Solar Clipper Design Structure Matrix (DSM)

AInputs

TrajectorySC

PropulsionSC

PowerSC

MassSC

CostSC

TrajectoryMREV

WeightsMREV

CostMREV

MassHab

CostHab

CostMissions

Outputs

B C F G H I J K L

A1

A2

B1

B2

C1

C2

C2

D

D1

D2

D3

D4

E3

E1

E2

G1

G2

H1

H2

I1

J1

J2

K3

K1

K2

K6

K4

K5

K7

Z1

Z2

L3

L2

L5

L4

L1



ROSETTA Model Categories

Category I- Produces traditional physics-based outputs such as transportation system

weight, size, payload and the NASA metric in-space trip time

Category II- In addition to above, adds additional ops, cost, and economic analysis

outputs such as turn-around-time, LCC, cost/flight, ROI, IRR, and the NASA metric price/lb. of payload

Category III- In addition to above, adds parametric safety outputs such as catastrophic

failure reliability, mission success reliability, and the NASA metric probability of loss of passengers/crew



ROSETTA Model Operation: Mars Orbit Basing (MOB) Solar Clipper

The ROSETTA spreadsheet model for this concept contains 11 disciplinary worksheets and an Inputs / Outputs (I/O) worksheet sheet

- The 11 disciplinary worksheets include:Trajectory – Solar ClipperPropulsion – Solar ClipperPower – Solar ClipperMass – Solar ClipperCost – Solar ClipperTrajectory – MREVWeights – MREVCost – MREVMass – HabCost – HabCost - Missions

ATIES Method and Implementation

Technology Prioritization

Ubiquitous Space Transportation Systems

Revolutionary Improvements

Technology Maturation

Limited Public and Private Outlays (Cumulative and Annual)

Knowledge Inherent in Engineering Models

Future?

Need?

Mechanisms?

Resources?

Techniques?



Technology evaluation:

Probabilistic Space Engineering



Frequency Chart

lbs

Mean = 2,507.93.000

.009

.018

.026

.035

0

8.75

17.5

26.25

35

2,300.00 2,425.00 2,550.00 2,675.00 2,800.00

1,000 Trials

Forecast: Payload Capability

80% Confidence

Pro

bab

ility F

requ

ency

Cumulative Chart

lbs

Mean = 67,878.5.000

.250

.500

.750

1.000

0

5000

63,488.5 65,768.5 68,048.6 70,328.7 72,608.7

1,000 Trials

Forecast: Dry Weight

Pro

bab

ility

Freq

uen

cy

80% Confidence

“Risk" is not the same as "reliability" or "safety“. Risk can be seen in payload variation, $/lb price variation, LCC variation, weight variation, and even safety variation. Immature technologies and incomplete knowledge of the conceptual design are sources of uncertainty leading to program risk.



A Robust Approach Applied to Prioritize Technologies

Mechanism to evaluate concepts (ROSETTA model): create an analysis module for assessing programmatic (i.e. cost and business case), safety, and performance

- Combines approach of meta-model with capability Monte Carlo simulations to generate cumulative distribution functions (CDFs)

Robust Design to probabilistically quantify impact of technologies on output metrics - Concerned with mean and variance of objective’s probability density function (PDFs)- Prudent decision maker uses PDFs to calculate 80% or 90% certainty values for program metrics to assure that vehicle will meet /

exceed desired metric 80% or 90% of the time

Prioritize technologies based upon output metrics and funding levels to determine optimum portfolios of future technologies on which to pour investment dollars

Abbreviated Technology Identification, Evaluation, and Selection (ATIES) methodology is used to leap this gulf of evaluation through:

- Systematic aggregation of decision-making methods (i.e. Morphological Matrices, Pugh Evaluation Matrices, Multi-Attribute Decision Making, etc.)

- Probabilistic methods (Response Surface Methodology, Monte Carlo Simulation, Fast Probability Integration, etc.)

1

2

3



Robust Design Using ROSETTA Analysis Module

Programmatic (PIFs) and Vehicle (VIFs)

[Deterministic or Probabilistic]

0% 1% 3% 4% 6%

J.8

Cumulative Chart

lb

.000

.250

.500

.750

1.000

0

250

500

750

1000

42,500 46,875 51,250 55,625 60,000

1,000 Trials 0 Outliers


Frequency Chart

lb

.000

.008

.016

.024

.032

0

8

16

24

32

42,500 46,875 51,250 55,625 60,000



Frequency and CumulativeProbability Distributions

0% 1% 3% 4% 6%

J.8

ROSETTA I/O (Inputs and Outputs)

DSM Detailed Meta-Model:ROSETTA Model

RDS I/O

Weights

Operations

Cost

Economics

Safety

A B C D E

I

L

N

O

F G H

K

M

J

RDS I/O

Weights

Operations

Cost

Economics

Safety

A B C D E

I

L

N

O

F G H

K

M

J

Influence Factors

Outputs

Outputs that Measure Progress toward Customer Goals

[Deterministic or Probabilistic]

Higher Fidelity Computational Models / Codes

• Update spreadsheet based meta-model to create most accurate representation of full design process

Translation of knowledge to ROSETTA modelthrough:• Direct simulation of high fidelity models• Semi-replication of models / codes• Response surface representations

Standard space architecture design methods:High fidelity tools

Long computation timeLack of integration with other tools

Standard space architecture design methods:High fidelity tools

Long computation timeLack of integration with other tools



ATIES Technology Prioritization Method

Baseline Concept DeterminationRequirements = Objectives + Constraints

(i.e. New RLV)

A

Technology Alternatives

Technology Identification

Technology Evaluation

Physics-based Modeling and Simulation Environment:Potential Environment: Reduced Order Simulation for

Evaluation of Technologies and Transportation Architectures (ROSETTA MODEL)

Physics-based Modeling and Simulation Environment:Potential Environment: Reduced Order Simulation for

Evaluation of Technologies and Transportation Architectures (ROSETTA MODEL)

B

E

Technology Mixes Deterministic or StochasticImpact Factors

Technology Selection

F

Analytic Hierarchic Process (AHP)and / or

Pugh Evaluation Matrix (PEM)

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS): Best Alternatives Ranked for

Desired Weightings

Individual Technology Comparison for

Resource Allocation

Technology Compatibility Matrix (TCM)

Technology Compatibility

C

Compatibility Matrix (1: compatible, 0: incompatible)

Com

posi

te W

ing

Com

posi

te F

usel

age

Circ

ulat

ion

Con

trol

HL

FC

Envi

ronm

enta

l Eng

ines

Flig

ht D

eck

Syst

ems

Prop

ulsi

on M

ater

ials

Inte

gral

ly, S

tiffe

ned

Alu

min

um

Airf

ram

e St

ruct

ures

(win

g)

Smar

t Win

g St

ruct

ures

(Act

ive

Aer

oela

stic

Con

trol)

Act

ive

Flow

Con

trol

Aco

ustic

Con

trol

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11

Composite Wing 1 1 1 0 1 1 1 0 0 0 0

Composite Fuselage 1 1 1 1 1 1 1 1 1 1

Circulation Control 1 1 1 1 1 1 1 1 1

HLFC 1 1 1 1 0 0 0 1

Environmental Engines 1 1 1 1 1 1 0

Flight Deck Systems 1 1 1 0 1 1

Propulsion Materials 1 0 1 1 1

Integrally, Stiffened Aluminum Airframe Structures (wing)

1 0 1 1

Smart Wing Structures (Active Aeroelastic Control)

1 1 1

Active Flow Control 1 1

Acoustic Control 1

Aircraft Morphing

Airc

raft

Mor

phin

g

Symmetric Matrix

Technology Impact Matrix (TIM)

Technology Impact

D

Com

posi

te W

ing

Com

posi

te F

usel

age

Circ

ulat

ion

Con

trol

HL

FC

Env

iron

men

tal E

ngin

es

Flig

ht D

eck

Syst

ems

Prop

ulsi

on M

ater

ials

Inte

gral

ly, S

tiffe

ned

Alu

min

um

Airf

ram

e St

ruct

ures

(win

g)

Smar

t Win

g St

ruct

ures

(Act

ive

Aer

oela

stic

Con

trol)

Act

ive

Flow

Con

trol

Aco

ustic

Con

trol

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11

Wing Weight -20% +5% -10% -5% +2%Fuselage Weight -25% -15%Engine Weight +1% +40% -10% +5%Electrical Weight +5% +1% +2% +5% +5% +2% +2%Avionics Weight +5% +2% +5% +2% +5% +2%Surface Controls Weight -5% +5% +5%Hydraulics Weight -5% +5%Noise Suppression -10% -1% -10%Subsonic Drag -2% -2% -10% -5%Supersonic Drag -2% -2% -15% -5%Subsonic Fuel Flow +1% +1% -2% -4% +1%Supersonic Fuel Flow +1% -2% -4%Maximum Lift Coefficient +15%O&S +2% +2% +2% +2% +2% +2% -2% +2% +2% +1%RDT&E +4% +4% +2% +2% +4% +2% +4% +5% +5% +5%Production costs +8% +8% +3% +5% +2% +1% +3% -3% -3% -3% -3%

Aircraft Morphing

Technical K_Factor Vector

1 -1 1-1-1 1

1 -1 1-1-1 1

1 -1 1-1-1 1

1 -1 1-1-1 1

1 -1 1-1-1 1

1 -1 1-1-1 1

1 -1 1-1-1 1

1 -1 1-1-1 1

+-+-++++

+-+-++++

+-+-++++

+-+-++++

+-+-++++

+-+-++++

+-+-++++

+-+-++++

Frequency Chart

lb

.000

.008

.016

.024

.032

0

8

16

24

32

42,500 46,875 51,250 55,625 60,000



0% 1% 3% 4% 6%

J.8

Vehicle Influence Factors

(VIF)

TechnologiesSymmetric Matrix impact factors

Technologies

Technologies

Note: Based upon work performed at the Aerospace Systems Design Laboratory (ASDL) at the Georgia Institute of Technology

Alternatives

1 2 3

Main Cruise Stage Propulsion Solar Electric Chemical rocket Solar Thermal Main Communications X band Orbiter link S band Main Power Solar Nuclear Chemical Batteries C

hara

cter

istic

s

Main Landing System Airbags Rocket thrusters Glider

0.91548

0.91534

0.91485

0.91461

0.91421

0.91391

0.91301

0.91262

0.91109

0.91060

0.910 0.915

Tech. Port. A

Tech. Port. B

Tech. Port. C

Tech. Port. D

Tech. Port. E

Tech. Port. F

Tech. Port. G

Tech. Port. H

Tech. Port. I

Tech. Port. J

Tec

hnol

ogy

Com

bina

tion

(Cas

e)

TOPSIS OEC

Probabilistic Output Data



1000 Monte Carlo Simulations

Through Crystal Ball®

Technology Evaluation Using ROSETTA Model and Monte Carlo Implementation

Mean = 30.3%

25.0% 28.6% 32.3% 35.9% 39.5%

E ngine T/W

ROSETTA I/O (Inputs and Outputs)

DSM Detailed Meta-Model

RDS I/O

Weights

Operations

Cost

Economics

Safety

A B C D E

I

L

N

O

F G H

K

M

J

RDS I/O

Weights

Operations

Cost

Economics

Safety

A B C D E

I

L

N

O

F G H

K

M

J

ROSETTA Model

Triangular distributions placed on5 N-factors (noise variables)

in ROSETTA model

Weibull distributions placed onROSETTA k-factors based upon

technology impact and TRL

Frequency Chart

lbs

Mean = 67,878.5.000

.007

.014

.021

.028

0

34.5

69

103.5

138

63,488.5 65,768.5 68,048.6 70,328.7 72,608.7



Cumulative Chart

lbs

Mean = 67,878.5.000

.250

.500

.750

1.000

0

5000

63,488.5 65,768.5 68,048.6 70,328.7 72,608.7



Frequency and Cumulative Distributions of Output Metrics

Mean = 5%

-5% 1% 8% 14% 20%

N_Factor: Propulsion Integrating S tructu

Weibull distribution parameter values used to mimic total uncertainty of technology impact as TRL is variedWeibull distribution chosen since it is family of distributions that assumes properties of other distributionsWeibull distribution normally defined by three parameters: apex location (L), shape (b), and scale (a)With some assumptions, reduce to two parameters through previously studied sensitivity investigations

- TRL: Defines scale of distribution- Maximum impact of a technology (“k factor”): location of distribution



Decision Making

Data

Metrics of Importance

DeterministicDeterministic

Concept metrics from design

processes for various technology

combinations

ProbabilisticProbabilistic

1

OEC

Develop Overall Evaluation Criteria: both qualitative and

quantitative measures of fitness

Attributes of the design

Attributes of the design

2

“Voices” of the Customer

Weighting Scenarios

Develop different weighting of the

components of the OEC (safety focused, cost

focused)

Ranking of MetricsRanking of Metrics

3

Shape the Decision by Ranking the

Alternatives

MADM

Multi-Attribute Decision Making;

Technique For Order Preference By

Similarity To Ideal Solution (TOPSIS)

Maximize OECMaximize OEC

4

Robust Design Process

A Holistic Prioritization Process

ROSETTA within ATIES Process [Knowledge Imbedded in Codes]

Monte Carlo Uncertainty Simulations [Knowledge of Experts]

Multiple Technology Combinations [Enabling + Enhancing]

Multiple Metrics [Multiple Weighting Scenarios]

Incorporation of Funding Constraints [Direct and Temporal]

Method

Future

Portfolios

Goals

Viability



Technology Identification



N (noise) Factor Uncertainty Distributions

+5%0%-20%Solar Flux Input [kW / m2]

+2%0%-20%Power Cabling Efficiency [%]

+20%0%-5%Propulsion integrating structure (% of total prop.) [%]

+20%0%-5%Bus Structure [%]

+20%0%-10%Total Transit Hab Mass [kg]

+20%0%-10%Total Surface Hab Mass [kg]

N-factor [units] MaximumMost likelyMinimum

Mean = -5%

-20% -14% -8% -1% 5%

N_Factor: Solar Flux Input

Mean = -6%

-20% -15% -9% -4% 2%

N_Factor: Power Cabling Efficiency

Mean = 5%

-5% 1% 8% 14% 20%

N_Factor: Propulsion Integrating Structure

Mean = 5%

-5% 1% 8% 14% 20%

N_Factor: Bus Structure

Mean = 3%

-10% -3% 5% 13% 20%

N_Factor: Total Transit Hab Mass

Mean = 3%

-10% -3% 5% 13% 20%

N_Factor: Total Surface Hab Mass



Enhancing Technology Portfolios

1008

0107

1106

0015

1014

0113

1112

0001 (Baseline)

Technology C:Super-conducting PMAD

Technology B:Triple Junction PV Arrays

Technology A:Carbon Nano-tube Structures

Portfolio

All portfolios (including baseline with no applied enhancing technologies) have N-factors (noise variables) in simulationAll portfolios (including baseline with no applied enhancing technologies) have N-factors (noise variables) in simulation

Full factorial combination of all possible technologies: 23 = 81 = Indicates technology is on and in portfolio

0 = Indicates technology is off and not in portfolio

Full factorial combination of all possible technologies: 23 = 81 = Indicates technology is on and in portfolio

0 = Indicates technology is off and not in portfolio



Enhancing Technology Impact and Funding Levels

Electric Propulsion PMAD Specific Mass: -20%Solar Clipper Integrated TC Specific Mass: -5%Solar Clipper Non-Recurring Cost: +7%Solar Clipper Recurring Cost: +3%In-Space Operations Facilities Cost: +5%

PV Cell Mass: -5%Array Specific Power: +30%Array Power Density: +30%Solar Clipper Non-Recurring Cost: +5%Solar Clipper Recurring Cost: +2%

PV Cell Mass: -20%Array Power Density: +20%Ancillary Mass (as % of Collector and Array Mass): -40%Wing Weight: -20%Fuselage Weight: -20%Propellant Tank Weight: -20%Subsystem Weight: -20%Undercarriage Weight: -20%Electric Propulsion PPU Specific Mass: -20%Electric Propulsion TC Specific Mass: -20%Electric Propulsion Thruster Specific Mass: -20%Electric Propulsion PMAD Specific Mass: -20%Solar Clipper Integrated TC Specific Mass: -20%Solar Clipper Non-Recurring Cost: +40%Solar Clipper Recurring Cost: +20%MREV Airframe DDT&E Cost: +20%MREV Airframe Procurement Cost (Manufacturing): +20%Transit Hab DDT&E Cost: +20%Transit Hab TFU Cost: +10%Surface Hab DDT&E Cost: +20%Surface Hab TFU Cost: +10%

Effect on Vehicle Influence Factors (VIFs)(k factor effects)

2

2

4

TRL

$450 M$90 M (x5)Super-conducting PMADC

$375 M$75 M (x5)Triple Junction PV ArraysB

$1,000 M$200 M (x5)Carbon Nano-tube StructuresA

Total Cumulative Funding

Annual Funding to TRL 6

(years required)

TechnologyNo.



Viable Technology Combinations

Yes$450 M$90 M8

Yes$375 M$75 M7

Yes$825 M$165 M6

Yes$1,000 M$200 M5

Yes$1,450 M$290 M4

Yes$1,375 M$275 M3

No$1,825 M$365 M2

Yes$0 M$0 M1 (Baseline)

Viable:Subject to Funding Constraints

Cumulative Funding Requirement[<$1,500M]

Annual Funding Requirement[<$300M]

Portfolio

Assume total annual funding allowance available for technology development to TRL 6 = $300 MAssume total cumulative funding allowance available for technology development to TRL 6 = $1,500 M

Assume total annual funding allowance available for technology development to TRL 6 = $300 MAssume total cumulative funding allowance available for technology development to TRL 6 = $1,500 M

Technology Evaluation and Selection



Portfolio 1:No Enhancing Technologies

Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]

Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]

80% Confidence 80% Confidence

80% Confidence80% Confidence

Frequency Chart

MT

Mean = 3,042.99

.000

.009

.019

.028

.037

0

9.25

18.5

27.75

37

2,800.00 2,975.00 3,150.00 3,325.00 3,500.00

1,000 Trials 0 Outliers Frequency Chart

kW

Mean = 73,431

.000

.010

.021

.031

.041

0

10.25

20.5

30.75

41

60,000 70,000 80,000 90,000 100,000


Frequency Chart

$B

Mean = 74.25

.000

.009

.017

.026

.034

0

8.5

17

25.5

34

62.50 69.38 76.25 83.13 90.00

1,000 Trials 0 OutliersFrequency Chart

$B

Mean = 52.18

.000

.011

.021

.032

.042

0

10.5

21

31.5

42

45.00 49.38 53.75 58.13 62.50


Freq

uen

cyPro

bab

ility

Freq

uen

cyPro

bab

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Freq

uen

cyPro

bab

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Freq

uen

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bab

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Portfolio 2:Carbon Nano-tube Structures, Triple Junction PV Arrays, Super-conducting PMAD





Frequency Chart

MT

Mean = 2,373.54

.000

.009

.018

.027

.036

0

9

18

27

36

2,200.00 2,300.00 2,400.00 2,500.00 2,600.00


kW

Mean = 57,241

.000

.011

.022

.033

.044

0

11

22

33

44

47,500 54,375 61,250 68,125 75,000


Frequency Chart

$B

Mean = 42.12

.000

.009

.018

.027

.036

0

9

18

27

36

38.00 40.25 42.50 44.75 47.00


$B

Mean = 53.47

.000

.009

.019

.028

.037

0

9.25

18.5

27.75

37

45.00 49.38 53.75 58.13 62.50


Freq

uen

cyPro

bab

ility

Freq

uen

cyPro

bab

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Freq

uen

cyPro

bab

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Freq

uen

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bab

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Portfolio 3:Carbon Nano-tube Structures, Triple Junction PV Arrays





Frequency Chart

MT

Mean = 2,386.30

.000

.010

.020

.030

.040

0

10

20

30

40

2,200.00 2,312.50 2,425.00 2,537.50 2,650.00


kW

Mean = 57,695

.000

.007

.015

.022

.029

0

7.25

14.5

21.75

29

50,000 55,625 61,250 66,875 72,500


Frequency Chart

$B

Mean = 42.95

.000

.009

.017

.026

.034

0

8.5

17

25.5

34

39.00 41.25 43.50 45.75 48.00


$B

Mean = 54.77

.000

.008

.016

.024

.032

0

8

16

24

32

47.50 51.25 55.00 58.75 62.50


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Portfolio 4:Carbon Nano-tube Structures, Super-conducting PMAD





Frequency Chart

MT

Mean = 2,814.33

.000

.008

.016

.024

.032

0

8

16

24

32

2,650.00 2,787.50 2,925.00 3,062.50 3,200.00


kW

Mean = 67,889

.000

.010

.021

.031

.041

0

10.25

20.5

30.75

41

55,000 63,750 72,500 81,250 90,000


Frequency Chart

$B

Mean = 48.70

.000

.008

.017

.025

.033

0

8.25

16.5

24.75

33

44.00 47.00 50.00 53.00 56.00


$B

Mean = 65.81

.000

.009

.017

.026

.034

0

8.5

17

25.5

34

57.50 63.13 68.75 74.38 80.00


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Portfolio 5:Carbon Nano-tube Structures





Frequency Chart

MT

Mean = 2,826.65

.000

.011

.022

.032

.043

0

10.75

21.5

32.25

43

2,600.00 2,775.00 2,950.00 3,125.00 3,300.00


kW

Mean = 68,207

.000

.009

.019

.028

.037

0

9.25

18.5

27.75

37

55,000 63,750 72,500 81,250 90,000


Frequency Chart

$B

Mean = 49.47

.000

.009

.018

.027

.036

0

9

18

27

36

44.00 47.50 51.00 54.50 58.00


$B

Mean = 66.94

.000

.008

.015

.023

.030

0

7.5

15

22.5

30

57.50 63.13 68.75 74.38 80.00


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Portfolio 6:Triple Junction PV Arrays, Super-conducting PMAD





Frequency Chart

MT

Mean = 2,507.93

.000

.009

.018

.026

.035

0

8.75

17.5

26.25

35

2,300.00 2,425.00 2,550.00 2,675.00 2,800.00


kW

Mean = 60,620

.000

.009

.018

.027

.036

0

9

18

27

36

50,000 56,875 63,750 70,625 77,500


Frequency Chart

$B

Mean = 43.32

.000

.010

.020

.030

.040

0

10

20

30

40

39.00 41.50 44.00 46.50 49.00


$B

Mean = 58.02

.000

.010

.019

.029

.038

0

9.5

19

28.5

38

50.00 54.38 58.75 63.13 67.50


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Portfolio 7:Triple Junction PV Arrays





Frequency Chart

MT

Mean = 2,513.33

.000

.009

.017

.026

.034

0

8.5

17

25.5

34

2,350.00 2,462.50 2,575.00 2,687.50 2,800.00


kW

Mean = 60,642

.000

.008

.016

.024

.032

0

8

16

24

32

52,500 59,375 66,250 73,125 80,000


Frequency Chart

$B

Mean = 44.06

.000

.008

.016

.024

.032

0

8

16

24

32

40.00 42.50 45.00 47.50 50.00


$B

Mean = 59.14

.000

.008

.016

.024

.032

0

8

16

24

32

50.00 55.00 60.00 65.00 70.00


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Portfolio 8:Super-conducting PMAD





Frequency Chart

MT

Mean = 3,026.44

.000

.009

.018

.026

.035

0

8.75

17.5

26.25

35

2,800.00 2,975.00 3,150.00 3,325.00 3,500.00


kW

Mean = 72,924

.000

.010

.019

.029

.038

0

9.5

19

28.5

38

60,000 70,000 80,000 90,000 100,000


Frequency Chart

$B

Mean = 51.19

.000

.010

.020

.030

.040

0

10

20

30

40

45.00 48.75 52.50 56.25 60.00


$B

Mean = 72.70

.000

.010

.020

.029

.039

0

9.75

19.5

29.25

39

62.50 69.38 76.25 83.13 90.00


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Probabilistic ROSETTA Output Metric Data:Pugh Evaluation Matrix (PEM)

51.19 (2.33)

53.13

44.06 (1.57)

45.41

43.22 (1.49)

44.54

49.47 (2.17)

51.07

48.70 (1.94)

50.28

42.95 (1.38)

44.12

42.12 (1.38)

43.24

52.18 (2.44)

54.09

Cost for First Mission [FY2001$B]Mean (Std. Deviation)80% Confidence <=

72.70 (4.42)

76.4272,924 (7,137)

78,4853,026.44 (131.71)

3,128.91

8

59.14 (2.98)

61.6860,642 (5,033)

65,0012,513.33 (75.75)

2,577.34

7

58.02 (2.84)

60.3960,620 (5,033)

65,0142,507.93 (75.17)

2,570.10

6

66.94 (4.10)

70.0168,207 (6,708)

74,1132,826.55 (116.88)

2,927.95

5

65.81 (3.68)

68.8667,889 (6,207)

73,4942,814.33 (107.14)

2,910.00

4

54.77 (2.62)

57.0457,695 (4,748)

61,8332,386.30 (66.29)

2,445.65

3

53.47 (2.61)

55.4857,241 (4,581)

61,4082,373.54 (63.80)

2,425.78

2

73.53 (4.61)

77.8173,431 (7,443)

79,8693,042.99 (137.95)

3,163.21

1 (Baseline)

Cost for Sub. Missions [FY2001$B]Mean (Std. Deviation)80% Confidence <=

Solar Clipper Required Power [kW]Mean (Std. Deviation)80% Confidence <=

Solar Clipper Mass IMLEO [MT]Mean (Std. Deviation)80% Confidence <=

Portfolio



TOPSIS Weighting Factors

Weighting Scenario

0%20%60%25%Cost for Sub. Missions [FY2001$B]

0%60%20%25%Cost for First Mission [FY2001$B]

0%10%10%25%Solar Clipper Required Power [kW]

100%10%10%25%Solar Clipper Mass IMLEO [MT]

Launch OperatorShort TermLong TermEven Components of OEC

The OEC consists of a combination of each type of output metric from the PEMVarious relative weighting scenarios result in different OECs and optimum technological solutions for each type of OECThe TOPSIS method includes the following sequence of activities:

Formation of a decision matrix from the PEMNon-dimensionalization by the Euclidean norm of the metric vector (metric columns of PEM)Establishment of positive (maximum metric value of benefit and minimum value of cost) and negative ideal solutions (compliment of positive)Determination of distance of each alternative from positive and negative idealFinal ranking of alternatives ranked from best to worst with optional evaluation of the robustness of the best alternatives

Overall Evaluation Criterion (OEC) serves as proxy for the needs of the customer, OEC can be decomposed into both qualitative and quantitative measures of fitness, a formulation of Multi-Attribute Decision Making (MADM) known as Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) can be used to order the alternatives in the Pugh Evaluation Matrix (PEM) in terms of those that maximize the OEC

Overall Evaluation Criterion (OEC) serves as proxy for the needs of the customer, OEC can be decomposed into both qualitative and quantitative measures of fitness, a formulation of Multi-Attribute Decision Making (MADM) known as Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) can be used to order the alternatives in the Pugh Evaluation Matrix (PEM) in terms of those that maximize the OEC



TOPSIS Relative Ranking For Each Weighting Scenario

Ranking of Overall Evaluation Criteria (OEC) For Each Weighting Scenario (1= Best)

55555

22226

44444

33337

66668

11113

--------------------2

77771 (Baseline)

Launch OperatorShort TermLong TermEven Portfolio

For all envisioned weighting scenarios above, portfolio 3 (Carbon Nano-tube Structures, Triple Junction PV Arrays) is best funding-viable combination of technologies

For all envisioned weighting scenarios above, portfolio 6 (Triple Junction PV Arrays, Super-conducting PMAD) is second best funding-viable combination of technologies

For all envisioned weighting scenarios above, portfolio 3 (Carbon Nano-tube Structures, Triple Junction PV Arrays) is best funding-viable combination of technologies

For all envisioned weighting scenarios above, portfolio 6 (Triple Junction PV Arrays, Super-conducting PMAD) is second best funding-viable combination of technologies

Based upon 80% confidence level values of ROSETTA model output metrics that form the Overall Evaluation Criteria (OEC)Based upon 80% confidence level values of ROSETTA model output metrics that form the Overall Evaluation Criteria (OEC)

Conclusions and References



Conclusions

For this case study:- Same rankings of technology combinations regardless of weighting scenarios- Portfolio 3 is always best because each and every component of OEC has best value in portfolio 3

(exclusive of non-viable portfolio 2)- Portfolio 6 is always second but is cheaper than portfolio 3

Total cumulative funding for portfolio 6 is $825 M vs. $1,375 M for portfolio 3Triple Junction PV Arrays are in both portfolio 3 and 6

“Risk" is any type of uncertainty associated with the program metrics and goals“Risk" is not the same as "reliability" or "safety" Risk can be seen in payload variation, $/lb price variation, LCC variation, weight variation, and even safety variationImmature technologies and incomplete knowledge of the conceptual design are sources of uncertainty leading to program risk

The ROSETTA model in the ATIES framework is an attempt to holistically examine robust output metrics to prioritize technologies based upon:

The knowledge inherent in legacy, high fidelity codesThe lack of knowledge about the future (and specifically the impact of technology)Funding constraints on an organization



References

“An Advanced Concept for Affordable Human Exploration Beyond Earth Orbit Using Megawatt-Class Solar Electric Propulsion, Reusable Systems and Orbital Basing,” prepared by the Advanced Projects Office, Office of Space Flight, NASA Headquarters, John Mankins.“An Independent Assessment of A Low Cost Human Mars Mission Using a Solar Clipper Architecture,” prepared by the Space Systems Design Laboratory (SSDL) Georgia Institute of Technology, Atlanta, GA, 9/17/99.“Prioritization of Advanced Space Transportation Technologies Utilizing the Abbreviated Technology, Identification, Evaluation, and Selection (ATIES) Methodology for a Reusable Launch Vehicle (RLV)” by A.C. Charania, Master’s Degree Special Problem, School of Aerospace Engineering, Georgia Institute of Technology, July 2000.“Robust Design Simulation: A Probabilistic Approach to Multidisciplinary Design” by D.N. Mavris, O. Brandte, and D.A. DeLaurentis, Journal of Aircraft, Volume 36, Number 1, pp. 298-307.“Forecasting Technology Uncertainty in Preliminary Aircraft Design” by Michelle R. Kirby and Dimitri N. Mavris, Presented at the 1999 World Aviation Conference, October 19-21, 1999, San Francisco, CA, SAE Paper 1999-01-5631.



SpaceWorks Engineering, Inc. (SEI)

Business Address:SpaceWorks Engineering, Inc. (SEI)1200 Ashwood ParkwaySuite 506Atlanta, GA 30338 U.S.A.

Phone: 770-379-8000Fax: 770-379-8001

Internet:WWW: www.sei.aeroE-mail: [email protected]

President / CEO: Dr. John R. OldsPhone: 770-379-8002E-mail: [email protected]

Director of Hypersonics: Dr. John E. BradfordPhone: 770-379-8007E-mail: [email protected]

Director of Concept Development: Mr. Matthew GrahamPhone: 770-379-8009E-mail: [email protected]

Project Engineer: Mr. Jon WallacePhone: 770-379-8008E-mail: [email protected]

Senior Futurist: Mr. A.C. CharaniaPhone: 770-379-8006E-mail: [email protected]

Contact Information

IAC-02-U.5.01: Application of the Abbreviated Technology … · (ATIES) Methodology to a Mars Orbit...

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