USING MODELCENTER TO PRIORITIZE TECHNOLOGY … · - Systematic aggregation of decision-making methods (i.e. Multi-Attribute Decision Making, etc.) - Probabilistic methods (Response

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

1

USING MODELCENTER TO PRIORITIZE TECHNOLOGY INVESTMENTS FOR LUNAR EXPLORATIONPhoenix Integration Fall Workshop: Decision Tools for Complex System of Systems (SoS) Engineering13-14 November 2006, Pasadena, California

Revision A14 November 2006

John E. Bradford, Ph.D.PresidentSpaceWorks Engineering, Inc. (SEI)[email protected]+770.379.8007


2

About SpaceWorks Engineering, Inc. (SEI)

Overview:- Engineering services firm based in Atlanta (small business concern)- Founded in 2000 as a spin-off from the Georgia Institute of Technology- Averaged 130% growth in revenue each year since 2001 - 85% of SEI staff members hold degrees in engineering or science

Core Competencies:- Advanced Concept Synthesis for launch and in-space transportation systems- Financial engineering analysis for next-generation aerospace applications and markets- Technology impact analysis and quantitative technology portfolio optimization

Recent Exploration Experience

Including:- NASA Exploration Systems Mission Directorate (ESMD) Concept Exploration and Refinement (CE&R) Study Subcontractor- NASA Exploration Systems Mission Directorate (ESMD) Economic Development of Space (EDS) Project- NASA MSFC exploration architecture trade studies (launch vehicles, in-space stages, lunar landers)- NASA MSFC Prometheus follow-on study: Nuclear Electric Propulsion (NEP) mission to Pluto/Kuiper Belt- NASA LaRC Lunar Lander Preparatory Study Phase 1/2 Concept Design for NASA JSC - Rocketdyne propulsion technology assessment on lunar exploration architectures- Mission Scenario Analysis Tool (MSAT) architecture optimization tool development- Moonraker in-space stage and habitat sizing tool development- In-space trajectory tool development- Lunar exploration economic and life cycle cost analysis

Image sources: NASA


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Overview

IntroductionMethodology and ProcessMission Scenario Analysis Tool (MSAT)Technology Simulator (TechSim)Technology Prioritization ExampleSummary and Conclusions


4


5SpaceWorks Engineering, Inc. (SEI)

www.sei.aero

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Introduction


6

Introduction

Any envisioned future with ubiquitous space transportation systems will rely on revolutionary improvements in the development and integration of technologies

Financial resources of both the government and commercial industry will always be subject to limitations

Strategic decision makers need methods for the prioritization of advanced space transportation technological investment

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


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A Robust Approach Applied to Prioritize Technologies

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

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

Robust Design to probabilistically quantify impact of technologies on output metrics - “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 at the conceptual design stage are sources

of uncertainty leading to program risk- 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(s) 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 target with investment dollars

Technology Simulator (TechSim) is used to leap this gulf of evaluation through:- Systematic aggregation of decision-making methods (i.e. Multi-Attribute Decision Making, etc.) - Probabilistic methods (Response Surface Methodology, Monte Carlo, DPOMD, Fast

Probability Integration, etc.)- Utilization of an advanced, collaborative engineering environment

1

2

3


8

Methodology and Process


9

Baseline Concept DeterminationRequirements = Objectives + Constraints

A

Technology Alternatives

Technology Identification

Technology Evaluation

Physics-based Modeling and Simulation Environment:

Mission Scenario Analysis Tool-MSAT

Physics-based Modeling and Simulation Environment:

Mission Scenario Analysis Tool-MSAT

B

E

Technology Mixes Deterministic or StochasticImpact Factors

Technology SelectionF

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 CompatibilityC

Compatibility Matrix (1: compatible, 0: incompatible)

Com

posi

te W

ing

Com

posi

te F

usel

age

Circ

ulat

ion

Con

trol

HLF

C

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 ImpactD

Com

posi

te W

ing

Com

posi

te F

usel

age

Circ

ulat

ion

Con

trol

HLF

C

Envi

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enta

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ines

Flig

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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

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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 T11Wing 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

1,000 Trials 0 Outliers

Forecast: Dry Weight

0% 1% 3% 4% 6%

J.8

Vehicle Influence Factors

(VIF)

TechnologiesSymmetric Matrix impact factors

Technologies

TechnologiesAlternatives

1 2 3Main Cruise Stage Propulsion Solar Electric Chemical rocket Solar ThermalMain Communications X band Orbiter link S bandMain Power Solar Nuclear Chemical BatteriesC

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

Tech

nolo

gy C

ombi

natio

n (C

ase)

TOPSIS OEC

Probabilistic Output Data

TechSim: SEI’s Technology Prioritization Process


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Technology Evaluation Using MSAT & Monte Carlo Implementation

Monte Carlo Simulations

MSAT 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

MSAT Model

Triangular distributions onMSAT N-factors (noise variables)

Triangular distributions onMSAT k-factors

(technology impact factors)

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


Forecast: Payload Capability

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


Forecast: Payload Capability

Frequency and Cumulative Distributions of Output Metrics

Mean = 5%

-5% 1% 8% 14% 20%

N_Factor: P ropulsion Integrating S tructu

Mean = 5%

-5% 1% 8% 14% 20%

N_Factor: P ropulsion Integrating S tructu

Step in TechSim ProcessE


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Technology Selection Algorithm

Data

Metrics of Importance

DeterministicDeterministic

Concept metrics from design processes

ProbabilisticProbabilistic

1

OEC

Develop Overall Evaluation Criteria: both qualitative and

quantitative measures of fitness

Attributes of the design


2

“Voices” of the Customer

Weightings

Develop different weighting scenarios of the components of

the OEC (safety focused, cost

focused)



3

Shape the Decision by Ranking the Alternatives

MADM

Multi-Attribute Decision Making;

Technique For Order Preference By

Similarity To Ideal Solution (TOPSIS)



4Robust Design

Process

Install Funding Constraints

Step in TechSim ProcessF


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Mission Scenario Analysis Tool (MSAT)


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Fundamental Question: What are the optimum components of an architecture for a required payload and crew demand over multiple missions?

Capability: Determines characteristic metrics of desired mission architecture by examining various sizes of the fundamental components of the architecture

Setup: Architecture defined by various segments and rendezvous points (i.e. Apollo, ESAS)

Metrics: Quantitative Figures of Merit (FOMs) including Initial Mass in Low Earth Orbit (IMLEO), Life Cycle Cost (LCC), Reliability

Breadth: Examine problem over a span of time (multiple mission opportunities)

Demand: Payload and crew demand at various operational nodes over multiple mission opportunities

Supply: Database of vehicle point designs to determine architecture performance (discrete sizes of elements of architecture)

Engine: Genetic algorithm (GA) used to find better assortment of discrete architecture elements

Platform: Operational in ModelCenter© collaborative design environment and MS Excel using Pi Blue Software’s OptWorks GA


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Database

Vehicles available for each segment

Dynamic and StaticDynamic and Static

Database



MSAT Process Flow

Vehicle Selection

Supply

User Selection or Optimizer

User Selection or Optimizer

What types of vehicles for each

segment in order to optimize metrics of

interest

Vehicle Database



Figures of Merit

MSAT Core Model

For a desired campaign

For a desired campaign

Mission Module

Customer DemandCustomer Demand

1

Opportunity 1

Opportunity 2

Opportunity 3

Opportunity n

.

.

.

Segment ij

Crew Required[Persons]

Cargo Required[MT]

Vehicle Selection Module

Selection: Genetic Algorithm (GA)Selection: Genetic Algorithm (GA)

3

Opportunity 1

Opportunity 2

Opportunity 3

Opportunity n

.

.

.

Vehicle 1

No. RequiredNo. Required

Vehicle Database Module

Customer Pre-Defined (ETO and In-Space)Customer Pre-Defined (ETO and In-Space)

2

Vehicle 1

Vehicle 2

Vehicle 3

Vehicle m

.

.

.

Cost

Payload Capability Per

Segment

MSAT Metrics Module

Output Variable CalculationOutput Variable Calculation

4

Vehicle k. . .

0 0 0 01 0 0 4 3 42 0 0 8 5 8

0 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 0

1 0 0 4 1 42 0 0 8 5 8

0 0 0 00 0 0 0

2 0 0 4 5 44 0 0 8 2 0 8

0 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 0

2 0 0 4 5 44 0 0 8 2 0 8

0 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 0

1 2 3 4 5 6 7 8 9 10 11 12 13 140 0 0 0 0 0 0 0 0 0 0 0 0 05 10 10 10 10 0 0 0 4 4 2 4 4 55 10 10 10 10 0 0 0 4 4 5 4 4 100 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0

RELIABILITY

VIN Vehicle Name DDT&E Cost TFU CostRecurring Cost

[Mission]

Number of Quarters for DDT&E

Number of Quarters for Acqusition

Base Learning Curve Effect Base Rate Effect

Derivative Indication

No. of Uses [Mission]

Base Refurbishment Percentage

Maximum Availability Per

Quarter Reliability

----- ----- $M (FY2004) $M (FY2004) $M (FY2004) ----- ----- % % ----- ----- % ----- -----

Equals "0" for already existing

items


items


items


items

Equals "0" for not a derivative, or

VIN of first model1 = Once, 0 = Unlimited


items

1 Olympus 120 3,825 1,017 0 3 1 85% 85% 2 1 0% 3 0.99998002 Olympus 60 2,596 668 0 3 1 85% 85% 0 1 0% 4 0.99998103 Olympus 80 2,946 796 0 3 1 85% 85% 2 1 0% 5 0.99998204 Delta-IV Heavy 0 0 74 3 1 85% 85% 0 0 0% 10 0.99998305 Delta-IV Heavy 0 0 66 0 0 0% 0% 0 0 0% 15 0.99998406 Falcon 5 0 0 6 0 0 0% 0% 0 0 0% 15 0.99998507 Ariane 5 0 0 150 0 0 0% 0% 0 0 0% 15 0.99998608 Proton M 0 0 90 0 0 0% 0% 0 0 0% 15 0.99998709 In-Space1 1,000 100 0 5 2 85% 95% 10 2 95% 10 0.999988010 In-Space2 2,000 200 0 5 2 85% 95% 0 2 95% 10 0.999989011 In-Space3 3,000 300 0 5 2 85% 95% 10 2 95% 10 0.999990012 In-Space4 4,000 400 0 5 2 85% 95% 10 2 95% 10 0.999991013 In-Space5 5,000 500 0 5 2 85% 95% 0 2 95% 10 0.999992014 In-Space6 6,000 600 0 5 2 85% 95% 0 2 95% 10 0.999992015 In-Space7 7,000 700 0 5 2 85% 95% 0 2 95% 10 0.9999920

IDENTIFICATION REUSABILITYCOST

Operations/Assembly Analysis

Cost Analysis

Reliability Analysis

. . .

Data for Vehicles Selected

Desired Payload

Quantity of Vehicles Selected

Pick vehicles for each segment in exploration campaign (i.e. lunar exploration)- Fixed Vehicles: Discrete vehicle choices from static database (ETO and CEV/Hab stages)- Designed Vehicles: Payload capability (MT) to design specific vehicles for segments (TLI/LOI,

Descent, Ascent, TEI)

Vehicle TypesA

B

C


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ModelCenter© ImplementationMSAT Core

Vehicle Databases


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Figures of Merit (FOMs)

Industrial Base and International Cooperation Synergies

Transportation

Figures of Merit

Quantitative

Qualitative

Cost

Safety / Reliability

Expert Abort Options

Evolvability

Technology Maturity

Integration Difficulty

Loss of Mission Reliability (LOM)

Loss of Vehicle Reliability (LOV)

Loss of Crew Reliability (LOC)

Expected number of vehicle losses (by segment)

Value of expected loss over the entire campaign

Total Life Cycle Cost for Campaign

Cost Through First Mission

Peak Annual Cost

Average Cost per Mission

Total Number of ETO Flights

Total Number of LEO Assemblies/ Rendezvous

Demand capture %

Average Packaging Margin Per Mission



www.sei.aero

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Technology Simulator (TechSim)


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TechSim Overview

TechSim is SEI’s quantitative technology prioritization process

Process has evolved from work initiated in academia

TechSim uses ModelCenter© as analysis environment, typical simulation here includes:

- Portfolio Runner- Genetic Algorithm Optimizer (when considering large number of portfolios)- Portfolio Manager (includes TCM and budgetary constraints)- Technology Impact Database (k-Factors and n-Factors distributions)- Monte Carlo Driver (for probabilistic analysis)- MSAT Core (vehicle databases, stage sizers, campaign mission model, etc.)- Metrics Accumulator (FOM collection)

TOPSIS output metric ranking tool can be either integrated in ModelCenter© or performed off-line

SEI’s process is flexible enough to handle:- Deterministic or Probabilistic, one-technology at a time rankings- Deterministic or Probabilistic, combinatorial technology rankings

Execution times vary depending upon analysis tool fidelity, but roughly one hour of CPU time to probabilistically assess a single technology portfolio


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SEI’s TechSim in ModelCenter© Collaborative Design Environment



www.sei.aero

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Technology Prioritization Example


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EARTH

MOON

Earth Orbit

LunarOrbit

Earth To Orbit (ETO) Launch No. 1:Cargo Launch Vehicle (CaLV)Shuttle-Derived Heavy Lift Launch Vehicle (HLLV)Earth Departure Stage (EDS) + Lunar Surface Access Module (LSAM)

Earth To Orbit (ETO) Launch No. 2:Crew Launch Vehicle (CLV)Solid Rocket Booster (SRB) with new Upper StageCrew Exploration Vehicle (CEV) Command Module (CM) +Crew Exploration Vehicle (CEV) Service Module (SM) + Launch Escape System (LES)

LEO Rendezvous

Earth Arrival

Transfer to Moon (TLI + LOI) Return to Earth (TEI)EDS

(Performs TLI)Two-Stage LSAM

(Performs LOI + Descent + Ascent)CEV/SM

(Performs TEI) CEV/CM

Note: Notional representation of lunar exploration architecture. Architecture elements may not be in scale.

Lunar Descent Lunar Ascent

5 x RS-68 [LOX/LH2]2 x 5 segment SRB+

2 x J-2S+ [LOX/LH2] 4 x RL-10+ [LOX/LH2] - Descent1 x New [LOX/CH4] - Ascent

1 x 5 segment SRB+

1 x J-2S [ LOX/LH2] 1 x LES SRM

1 x New [LOX/CH4] – Same as LSAM

Human Lunar Conceptual Mission Architecture

Step in TechSim ProcessA


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Technology Identification

Four notional technologies chosen:- A. Advanced Liquid Oxygen (LOX) / Liquid Hydrogen (LH2) rocket engine - B. Composite propellant tanks fully compatible with cryogenic liquids - C. Enhanced Automated Rendezvous and Docking (AR&D) - D. New lightweight Environmental Control and Life Support System (ECLSS) for

the Crew Exploration Vehicle (CEV)

All four technologies are compatible with each other

Recognizing that advanced technology budgets usually have fixed ceilings, usually best to narrow list of possible portfolios based on fixed annual and/or multi-year budgets before evaluating those portfolios

All possible technology portfolios set up using full-factorial Design of Experiments (DOE) method for the four technologies selected—16 maximum possible runs

Step in TechSim ProcessB


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Funding-Viable Technology Portfolios

Portfolio Technology A:

Advanced LOX/LH2 Engine

Technology B: Cryogenic Propellant

Tanks

Technology C: Enhanced

AR&D Technology D: CEV ECLSS

Annual Funding Requirement

[<1.0]

Cumulative Funding

Requirement [<4.6]

Viable: Subject to Funding

Constraints 1 No Enhancing Technologies 0.00 0.00 Yes 2 + 0.31 1.56 No 3 + 0.44 2.19 Yes 4 + 0.50 2.50 Yes 5 + 0.88 4.38 Yes 6 + + 0.75 3.75 Yes 7 + + 0.81 4.06 Yes 8 + + 0.94 4.69 No 9 + + 1.31 6.56 No

10 + + + 1.25 6.25 No 11 + + 1.19 5.94 No 12 + + + 1.63 8.13 No 13 + + 1.38 6.88 No 14 + + + 1.69 8.44 No 15 + + + 1.81 9.06 No 16 + + + + 2.13 10.63 No

Yes

Step in TechSim ProcessC


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Technology Impact: k-factors

Technology impact assessed through qualitative impact factors known as “k-factors”k-factors mimic discontinuities in benefits and/or penalties associated with the infusion of new technologiesProcess of determining k-factors is subjective and values are obtained from technology-specific experts

k-Factor Min Middle Max EDS Engine Vacuum Isp +3% +5% +10% EDS Engine Vacuum T/W +20% +30% +40% EDS TFU Cost 0% +3% +5% EDS Reliability +5% +10% +35% Descent Stage Engine Vacuum Isp +3% +5% +10% Descent Stage Engine Vacuum T/W +20% +30% +40% Descent Stage TFU Cost 0% +3% +5% Descent Stage Reliability +5% +10% +35%

k-factors for Technology A: Advanced Liquid Rocket Engine

Step in TechSim ProcessD


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Uncertainty: n-factors

Inherent uncertainty in vehicle performance and programmatic variables captured through “n-factors”n-factors are distributions placed on inputs regardless of technologies used

Noise (N) Factor Min. Most Likely Max. Primary Structural Weight/Area -5% 0% +5%

Fuel Tank Weight/Volume -5% 0% +5% Oxidizer Tank Weight/Volume. -5% 0% +5%

Engine Vacuum Specific Impulse (Isp) -3% 0% +2% Engine Vacuum T/W -5% 0% +10%

Battery Specific Energy Density -5% 0% +10% Landing Structure Weight -5% 0% +5%

Development Cost -5% 0% +25% Theoretical First Unit (TFU) Cost -5% 0% +25%

Stage Reliability -5% 0% +10%

n-factors for Lunar Descent Stage


26

Overall Evaluation Criteria (OEC)

Overall evaluation criteria (OEC) represents “voice of the customer”- Technology prioritization conducted for several different OEC weighting

scenariosEight Architecture-Level Figures of Merit (FOMs) contribute to OEC

- Cost Metrics (4)Total LCC, Peak Annual Cost, Cost to First Flight, Avg. Cost per Mission

- Performance Metrics (2)Initial Mass LEO, Excess Capability

- Safety Metrics (2)Reliability Across Campaign, Total Campaign Reliability

safesafeeconomicseconomicsperfperf NWNWNWOEC ×+×+×=

Weighting Scenario (WS)

Components of OEC Uniform Cost Focused

Performance Focused

Safety Focused

Cost Metrics 33% 80% 10% 10% Performance Metrics 33% 10% 80% 10% Safety Metrics 33% 10% 10% 80%


27

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tech A

Tech B & D

Tech D

Tech C & D

Tech B

No Tech

Tech C

Tec

hnol

ogy

Port

folio

s .


TOPSIS Results for Different OEC Weighting Scenarios (1)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tech B & D

Tech A

Tech D

Tech C & D

Tech B

No Tech

Tech C

Tec

hnol

ogy

Port

folio

s .

Overall Evaluation Criteria (OEC)0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tech D

Tech B & D

No Tech

Tech B

Tech A

Tech C & D

Tech C

Tec

hnol

ogy

Port

folio

s .


Uniform Weighting Scenario Cost Focused Weighting Scenario

Performance Focused Weighting Scenario Safety Focused Weighting Scenario

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tech C & D

Tech C

Tech A

Tech B & D

Tech D

Tech B

No Tech

Tec

hnol

ogy

Port

folio

s .




28

TOPSIS Results for Different OEC Weighting Scenarios (2)

For notional technologies assumed, appears two portfolios should be considered for funding

- Portfolio 5 – Advanced LOX/LH2 Rocket Engine- Portfolio 7 – Lightweight CEV ECLSS & Cryogenic Composite Propellant Tanks


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

Portfolio Tech A:

Advanced LOX/LH2 Engine

Tech B: Cryogenic Propellant

Tanks

Tech C: Enhanced

AR&D

Tech D: CEV

ECLSS Uniform Cost

Focused Performance

Focused Safety

Focused

1 No Enhancing Technologies 6 3 6 7 2 + 3 1 3 5 3 + 7 7 7 2 4 + 5 4 5 6 5 + 2 5 1 3 6 + + 4 6 4 1 7 + + 1 2 2 4


29

Summary and Conclusions


30

Summary and Conclusions (1)

The TechSim technology prioritization process has been demonstrated and can be used on multiple problems relevant to technology managers

Example technology prioritization shown here represents limited example of the capabilities of this method

- Process provides a method to quantitatively prioritize which future exploration technologies deserve consideration for near-term funding

- Various technology assumptions chosen to show effects of different technology types and do not necessarily represent the true effect of the example technologies

- A. Advanced Engine – Expensive technology with large performance impact- B. Composite Tanks – Relatively inexpensive technology with broad applicability but minor

performance benefit- C. Enhanced AR&D – Technology which greatly increases values of some metrics with possible

negative impact on others- D. CEV ECLSS – Provides large benefit to one portion of system

For actual technology prioritization process individual “k-factors”associated with each candidate technology would be provided by technologists


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Summary and Conclusions (2)

While this example only featured a limited and easily manageablenumber of technology portfolios, an actual simulation would contain 20-30 technologies and potentially millions of feasible technology portfolios

- Require injection of Genetic Algorithm (GA) optimizer or other alternative method to full-factorial approach used here

Use of ModelCenter© is advantageous on many levels:- Provides easily reproducible results- Central repository for input/output parameters of all models- Easy access and integration of additional tools like Genetic Algorithm (GA)- Permits quick changes in analysis fidelity level

e.g. introduction of high-fidelity propulsion codes, etc.- When evaluating larger numbers of technology portfolios, extending problem to use of

applications like Phoenix Integration’s CenterLink© becomes solution-enabling


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Documents

USING MODELCENTER TO PRIORITIZE TECHNOLOGY … · - Systematic aggregation of decision-making methods (i.e. Multi-Attribute Decision Making, etc.) - Probabilistic methods (Response