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World Space Congress – 200210-19 Oct 2002/Houston, Texas
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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
World Space Congress – 200210-19 Oct 2002/Houston, Texas
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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
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From Vision to Concept
Including:- Engineering design and analysis- New concept design- Independent concept assessment- Full, life cycle analysis- Programmatic and technical analysis
Engineering Today, Enabling Tomorrow
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
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Engineering Today, Enabling Tomorrow
Motivation
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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
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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
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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
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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
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Transit and Surface Habs
Source: NASA Human Spaceflight [http://spaceflight.nasa.gov/station/assembly/elements/transhab/]
ROSETTA Model
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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.
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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
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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
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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?
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Technology evaluation:
Probabilistic Space Engineering
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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.
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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
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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
Forecast: Dry Weight
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
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
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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
1,000 Trials 0 Outliers
Forecast: Dry Weight
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
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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
5,000 Trials 34 Outliers
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
5,000 Trials 34 Outliers
Forecast: Payload Capability
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
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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
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Technology Identification
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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
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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
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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.
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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 = $1,500 M
Technology Evaluation and Selection
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Portfolio 1:No Enhancing Technologies
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
Forecast: Cost for First Mission [FY2001$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
1,000 Trials 0 Outliers
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
$B
Mean = 52.18
.000
.011
.021
.032
.042
0
10.5
21
31.5
42
45.00 49.38 53.75 58.13 62.50
1,000 Trials 0 Outliers
Freq
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Portfolio 2:Carbon Nano-tube Structures, Triple Junction PV Arrays, Super-conducting PMAD
Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]
80% Confidence80% Confidence
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
80% Confidence 80% Confidence
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
1,000 Trials 0 Outliers Frequency Chart
kW
Mean = 57,241
.000
.011
.022
.033
.044
0
11
22
33
44
47,500 54,375 61,250 68,125 75,000
1,000 Trials 0 Outliers
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
1,000 Trials 0 Outliers Frequency Chart
$B
Mean = 53.47
.000
.009
.019
.028
.037
0
9.25
18.5
27.75
37
45.00 49.38 53.75 58.13 62.50
1,000 Trials 0 Outliers
Freq
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Portfolio 3:Carbon Nano-tube Structures, Triple Junction PV Arrays
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]
80% Confidence 80% Confidence
80% Confidence80% Confidence
Frequency Chart
MT
Mean = 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
1,000 Trials 0 Outliers Frequency Chart
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
1,000 Trials 0 Outliers
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
1,000 Trials 0 Outliers Frequency Chart
$B
Mean = 54.77
.000
.008
.016
.024
.032
0
8
16
24
32
47.50 51.25 55.00 58.75 62.50
1,000 Trials 0 Outliers
Freq
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Freq
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Page 36
Portfolio 4:Carbon Nano-tube Structures, Super-conducting PMAD
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]
80% Confidence 80% Confidence
80% Confidence80% Confidence
Frequency Chart
MT
Mean = 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
1,000 Trials 0 Outliers Frequency Chart
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
1,000 Trials 0 Outliers
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
1,000 Trials 0 Outliers Frequency Chart
$B
Mean = 65.81
.000
.009
.017
.026
.034
0
8.5
17
25.5
34
57.50 63.13 68.75 74.38 80.00
1,000 Trials 0 Outliers
Freq
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Freq
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Portfolio 5:Carbon Nano-tube Structures
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]
80% Confidence 80% Confidence
80% Confidence80% Confidence
Frequency Chart
MT
Mean = 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
1,000 Trials 0 Outliers Frequency Chart
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
1,000 Trials 0 Outliers
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
1,000 Trials 0 Outliers Frequency Chart
$B
Mean = 66.94
.000
.008
.015
.023
.030
0
7.5
15
22.5
30
57.50 63.13 68.75 74.38 80.00
1,000 Trials 0 Outliers
Freq
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Page 38
Portfolio 6:Triple Junction PV Arrays, Super-conducting PMAD
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]
80% Confidence 80% Confidence
80% Confidence80% Confidence
Frequency Chart
MT
Mean = 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 0 Outliers Frequency Chart
kW
Mean = 60,620
.000
.009
.018
.027
.036
0
9
18
27
36
50,000 56,875 63,750 70,625 77,500
1,000 Trials 0 Outliers
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
1,000 Trials 0 Outliers Frequency Chart
$B
Mean = 58.02
.000
.010
.019
.029
.038
0
9.5
19
28.5
38
50.00 54.38 58.75 63.13 67.50
1,000 Trials 0 Outliers
Freq
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Page 39
Portfolio 7:Triple Junction PV Arrays
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]
80% Confidence 80% Confidence
80% Confidence80% Confidence
Frequency Chart
MT
Mean = 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
1,000 Trials 0 Outliers Frequency Chart
kW
Mean = 60,642
.000
.008
.016
.024
.032
0
8
16
24
32
52,500 59,375 66,250 73,125 80,000
1,000 Trials 0 Outliers
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
1,000 Trials 0 Outliers Frequency Chart
$B
Mean = 59.14
.000
.008
.016
.024
.032
0
8
16
24
32
50.00 55.00 60.00 65.00 70.00
1,000 Trials 0 Outliers
Freq
uen
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Freq
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Page 40
Portfolio 8:Super-conducting PMAD
Forecast: Solar Clipper Mass IMLEO [MT] Forecast: Solar Clipper Required Power [kW]
Forecast: Cost for First Mission [FY2001$B] Forecast: Cost for Subsequent Missions [FY2001$B]
80% Confidence 80% Confidence
80% Confidence80% Confidence
Frequency Chart
MT
Mean = 3,026.44
.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
1,000 Trials 0 Outliers Frequency Chart
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
1,000 Trials 0 Outliers
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
1,000 Trials 0 Outliers Frequency Chart
$B
Mean = 72.70
.000
.010
.020
.029
.039
0
9.75
19.5
29.25
39
62.50 69.38 76.25 83.13 90.00
1,000 Trials 0 Outliers
Freq
uen
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Freq
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Probabilistic ROSETTA Output Metric Data:Pugh Evaluation Matrix (PEM)
51.19 (2.33)
53.13
44.06 (1.57)
45.41
43.22 (1.49)
44.54
49.47 (2.17)
51.07
48.70 (1.94)
50.28
42.95 (1.38)
44.12
42.12 (1.38)
43.24
52.18 (2.44)
54.09
Cost for First Mission [FY2001$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$B]Mean (Std. Deviation)80% Confidence <=
Solar Clipper Required Power [kW]Mean (Std. Deviation)80% Confidence <=
Solar Clipper Mass IMLEO [MT]Mean (Std. Deviation)80% Confidence <=
Portfolio
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TOPSIS Weighting Factors
Weighting Scenario
0%20%60%25%Cost for Sub. Missions [FY2001$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
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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
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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
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Page 46
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.
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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