SpaceWorks Engineering, Inc. (SEI)

Transcription

1 A Method For For Advanced Space Transportation Systems October 2001 President / CEO: Dr. John R. Olds Senior Futurist: Mr. A.C. Charania Director of Technical Analysis: Mr. Andrew Crocker Director of Hypersonics: Dr. John Bradford Page 1

2 Introduction to the Firm Motivation Domain of Evaluation: Bimese RLV ATIES Process Analytic Hierarchic Process (AHP) Comparison Agenda

3 Introduction to the Firm

4 About 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: - Advanced Systems Engineering - Technology Prioritization - Financial Engineering - Future Market Assessment - Creative and Policy Services Page 4

5 Sample Past Experience of Personnel Highly Reusable Space Transportation Study Bantam-X TSTO RLV studies RBCC Vision Vehicle studies Future-X Flight Demonstration Concept Development Fast package delivery options for Bimese RLVs Memo-Micron Mars Discovery Mission Proposal Space Solar Power Post Fresh-Look Studies: Space Transportation and Economics Mars Orbit Basing Architecture Analysis Space Transportation Infrastructure Study (STIS) Manned Mars mission studies / NC State Mars Mission Research Center Space Tourism Vehicle / Market Synergies Study Affordable Rapid Response Missile Demonstration (ARRMD) StrutJet RBCC Engine Program NRA8-27 Rocket Engine Cycle Performance, Safety, and Reliability Study Integrated Technology Assessment Center (ITAC) Integrated High Payoff Rocket Propulsion Technology (IHPRPT) Program RD-180 Test Program Hydrogen Peroxide Engine Technology Test Program Advanced Rocket Engine Component Development Page 5

6 Motivation

7 Motivation Any envisioned future with ubiquitous space transportation systems will rely on revolutionary improvements in the development and integration of technologies Limitation of financial resources by both the government and industry Strategic decision makers need methods in the prioritization of advanced space transportation technological investment New methods have to be proactive in forecasting impact of new technologies, even before the maturation of those technologies Page 7

8 Why Probabilistic Analysis? The significant findings from recent studies and reports, involving both successful and unsuccessful missions, mandates sweeping revisions in the way NASA manages the early planning, implementation, and reviews of programs and projects. Therefore, I am requiring the use of formal risk management processes, risk management technologies (e.g. failure modes and effects analysis, fault tree analysis, and probabilistic risk assessments), and design for safety on all NASA programs and projects.improved safety and mission success will result only from your complete, thorough, and across-the-board understanding and management of risk. Daniel S. Goldin, NASA Administrator, May 31, 2000 Page 8

9 A Robust Approach Robust design concerned with mean and variance of objective s probability density function (PDFs) - Program "risk" is generally a measure of standard deviation of some output distribution about a mean value - Prudent decision maker uses PDFs to calculate 80% or 90% confidence values for program metrics to assure that vehicle will meet / exceed desired metric 80% or 90% of the time Evaluation of concepts: create an analysis module for assessing programmatic (i.e. cost and business case), safety, and performance uncertainties associated with future transportation systems - Combines approach of a meta-model with capability to perform Monte Carlo simulations to generate cumulative distribution functions (CDFs) Page 9

10 Applied to Prioritize Technologies The objective is to probabilistically quantify the impact of technologies on the output metrics of interest from the design process Couple design process with 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 a systematic aggregation of Decision-making techniques (i.e. Morphological Matrices, Pugh Evaluation Matrices, Multi-Attribute Decision Making, etc.) - Probabilistic methods (Response Surface Methodology, Monte Carlo Simulation, Fast Probability Integration, etc.) Page 10

11 Domain of Evaluation: Bimese RLV

12 Bimese Item Characteristics Concept Bimese Two-Stage-To-Orbit (TSTO) reusable launch vehicle (RLV) Configuration Reference Mission Flight Performance Programmatic Bimese or twin approach where both stages are identical and interchangeable The vehicle has wing-body configuration Vertical takeoff, un-powered horizontal landing with parallel burn of stages with cross feed or propellants External payload pod for cargo missions and external Crew Transfer Vehicle (CTV) for crewed missions 35klb in a 15 x 55 ft payload bay (reference orbit: 248nm. circular x 51.6 degrees inclination) Cargo delivery and return Booster vehicle docks with ISS Crew rotation missions Crew Transfer Vehicle (CTV) carried by booster to LEO and separates from booster for ISS rendezvous Mission duration of ~10 days + 2 days margin Human rated Crew survivable abort capability Automated rendezvous and docking Two engine out (1 per Bimese element) capability to make mission Cross range capability of several hundred nmi Flight performance reserve: 1% of V Fully commercially venture IOC of 2010 with a technology freeze date of 2005 Source: TSTO Bimese Reference Vehicle by Roger Lepsch (NASA LaRC), Presentation made to ISAT Integrated Technology Assessment, September 12, Page 12

13 Bimese Concept Configuration Single element Fuel/thrustaugmented Fuelaugmented Thrustaugmented Mated Note: Particular schematics show internal payload compartment, different from actual analyzed architecture Page 13

14 ATIES Process

15 0% 1% 3% 4% 6% Compatibility Matrix (1: compatible, 0: incompatible) Aircraft Morphing T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 Composite Wing Composite Fuselage Circulation Control HLFC Environmental Engines Flight Deck Systems Propulsion Materials Symmetric Matrix Integrally, Stiffened Aluminum Airframe Structures (wing) Smart Wing Structures (Active Aeroelastic Control) Active Flow Control 1 1 Acoustic Control 1 1,000 Trials Frequency Chart 0 Outliers Forecast: Dry Weight 42,500 46,875 51,250 55,625 60,000 lb ATIES Technology Prioritization Method A Baseline Concept Determination Requirements = Objectives + Constraints C Technology Compatibility D Technology Impact B Technology Identification Characteristics Technology Alternatives Alternatives Main Cruise Stage Propulsion Solar Electric Chemical rocket Solar Thermal Main Communications X band Orbiter link S band Main Power Solar Nuclear Chemical Batteries Main Landing System Airbags Rocket thrusters Glider Technology Compatibility Matrix (TCM) Symmetric Matrix Technologies Aircraft Morphing Technologies Composite Wing Composite Fuselage Circulation Control HLFC Environmental Engines Flight Deck Systems Propulsion Materials Integrally, Stiffened Aluminum Airframe Structures (wing) Smart Wing Structures (Active Aeroelastic Control) Active Flow Control Acoustic Control Technology Impact Matrix (TIM) impact factors Vehicle Influence Factors (VIF) Composite Wing Composite Fuselage Technologies Circulation Control Technical K_Factor Vector 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% HLFC Environmental Engines Flight Deck Systems Propulsion Materials Integrally, Stiffened Aluminum Airframe Structures (wing) Aircraft Morphing Smart Wing Structures (Active Aeroelastic Control) Active Flow Control Acoustic Control E Technology Evaluation Technology Mixes Deterministic or Stochastic Impact Factors J.8 F Technology Selection Pugh Evaluation Matrix (PEM) With programmatic funding constraints Metric 1 Metric 2.. Metric X Alternative 1 # #.. # Alternative 2 # #.. # Alternative3 # #.. # Alternative 2 n # #.. # Physics-based Modeling and Physics-based Simulation Environment Modeling and Simulation Environment (ROSETTA MODEL) (ROSETTA MODEL) TOPSIS: Best Alternatives Rank Alternatives for Desired Weighting Individual Technology Comparison for Resource Allocation Note: Based upon work performed at the Aerospace Systems Design Laboratory (ASDL) at the Georgia Institute of Technology Page 15

16 Typical RLV Design Structure Matrix (DSM) Aerodynamics and Geometry Reference Configuration & Packaging Propulsion Trajectory Aeroheating and TPS Weights and Sizing Operations Safety and Reliability Cost and Economics Page 16

17 ROSETTA Concept Analysis Module Design Structure Matrix (DSM) ROSETTA Inputs Trajectory L M N O P Q A Programmatic Influence Factors (PIFs) and Vehicle Influence Factors (VIFs) From Inputs Changes Disciplines Weights B C D R Operations E F S Cost G T Output Metrics and Design Convergence Criteria I Economics H U K J Safety V ROSETTA Outputs Page 17

18 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 seconds Requirement for uncertainty analysis through Monte-Carlo simulation - Outputs measure progress towards customer goals ($/lb, turn-around-time, safety, etc.) Standard deterministic outputs as well as probabilistic through Monte Carlo ROSETTA ROSETTA models models contain contain representations representations of of the the full full design design process. process. Individual Individual developer developer of of each each ROSETTA ROSETTA model model determines determines depth depth and and breadth breadth of of appropriate appropriate contributing contributing analyses. analyses. More More assumptions, assumptions, fewer fewer DSM DSM links links than than higher higher fidelity fidelity models models due due to to need need for for faster faster calculation calculation speeds. speeds. Page 18

19 % 1% 3% 4% 6% 42,500 46,875 51,250 55,625 60,000 lb % 1% 3% 4% 6% ,500 46,875 51,250 55,625 60,000 lb Integration of Main-Line Tools with ROSETTA Model ROSETTA I/O (Inputs and Outputs) Influence Factors Programmatic (PIFs) and Vehicle (VIFs) [Deterministic or Probabilistic] Higher Fidelity Computational Models / Codes J.8 J.8 Outputs Outputs that Measure Progress toward Customer Goals [Deterministic or Probabilistic] DSM Detailed Meta-Model: ROSETTA Model RDS I/O Weights A B C D E Operations F G H J Cost K M Economics I L N O Safety Update spreadsheet based meta-model to create most accurate representation of full design process Translation of knowledge to ROSETTA model through: Direct simulation of high fidelity models Semi-replication of models / codes Response surface representations Frequency and Cumulative Probability Distributions Forecast: Dry Weight 1,000 Trials Frequency Chart 0 Outliers Forecast: Dry Weight 1,000 Trials Cumulative Chart 0 Outliers Standard space architecture design methods: Standard space architecture design methods: High fidelity tools High fidelity tools Long computation time Long computation time Lack of integration with other tools Lack of integration with other tools Page 19

20 Technology Evaluation Using ROSETTA Model and Monte Carlo Implementation Triangular distributions on ROSETTA N-factors (noise variables) Monte Carlo Simulations Through Crystal Ball Frequency and Cumulative Distributions of Output Metrics N_Factor: Propulsion Integrating Structu ROSETTA Model Forecast: Payload Capability 5,000 Trials Frequency Chart 34 Outliers Mean = 5% -5% 1% 8% 14% 20% ROSETTA I/O (Inputs and Outputs).007 Mean = 67, , , , , , lbs N_Factor: Propulsion Integrating Structu DSM Detailed Meta-Model Forecast: Payload Capability 5,000 Trials Cumulative Chart 34 Outliers RDS I/O A B C D E Weights F G H I.750 Operations J K L.500 Cost M N Mean = 5% Economics O Safety Mean = 67, % 1% 8% 14% 20% 63, , , , ,608.7 lbs Triangular distributions on ROSETTA k-factors (technology impact factors) Page 20

21 Decision Making Chain in ATIES 1 Data Concept metrics from design processes 2 OEC Deterministic Deterministic Probabilistic Probabilistic Robust Design Process Install Funding Constraints Develop Overall Evaluation Criteria: both qualitative and quantitative measures of fitness Attributes of Attributes of the design the design Metrics of Importance 3 Weightings Develop different weighting scenarios of the components of the OEC (safety focused, cost focused) Attributes of Attributes of the design the design 4 MADM Multi-Attribute Decision Making; Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) Voices of the Customer Attributes of Attributes of the design the design Shape the Decision by Ranking the Alternatives Page 21

22 Overall Evaluation Criteria (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 The OEC consists of a combination of each type of output metric from the PEM Various relative weighting scenarios result in different OECs and optimum technological solutions for each type of OEC The TOPSIS method includes the following sequence of activities: Formation of a decision matrix from the PEM Determination of distance of each alternative from positive and negative ideal Final ranking of alternatives ranked from best to worst with optional evaluation of the robustness of the best alternatives Page 22

23 TOPSIS Weighting Factors WEIGHTING SCENARIOS Even Weights Size-Based Weights Economics-Based Weights Operations-Based Weights Safety/Reliability- Based Weights C R I T E R I A Size Economics Operations Safety/Reliability Using Using 80% 80% confidence confidence level level values values of of ROSETTA ROSETTA model model output output metrics metrics forming forming Overall Overall Evaluation Evaluation Criteria Criteria (OEC), (OEC), Ranking Ranking of of Overall Overall Evaluation Evaluation Criteria Criteria (OEC) (OEC) for for each each Weighting Weighting Scenario Scenario above above Page 23

24 Sample Technology Selection Results Using Decision Making Methods in ATIES Technology Portfolios (feasible technology combinations that meet funding constraints) Deterministic 80% Confidence TechC_D_E TechB_D_E TechB_C_E TechB_C_D TechD_E TechC_E TechC_D TechB_E TechB_D TechB_C TechA_E TechA_C TechE TechD TechC TechB TechA notech Overall Evaluation Criterion (OEC), based upon multiple metrics which are aggregated and ranked using decision making methods such as TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for a particular weighting scenario Page 24

25 Analytic Hierarchic Process (AHP)

26 AHP Overview Method that relies on distillation of expert knowledge and quantification into ranking scores Developed at Wharton School of Business by Thomas Saaty to apply human experience and intuition in logical construct to help formulate decisions AHP is a method to help make decisions amongst alternatives based upon three principles: - Decomposition: problem is disassociated hierarchically into various objectives, subobjectives - Comparative judgments: perform pair-wise comparisons between alternatives for each criterion - Synthesis of priorities: form weighted scores for each alternative This examination used 4-member aerospace engineering group from SEI - Advanced degrees and many years industry experience - Portray themselves as a customer whose utility derived from following constituents: 50% commercial, 25% civil space, and 25% military space Page 26

27 AHP Use Case Alternatives Pair-wise Comparisons Normalized Impact Calculations Determine the candidate technologies available to choose Relate technology combinations for each attribute to rank influence Determine the impact of each technology by normalizing effect Attributes Weighting Scenarios Determine OEC Determine the metrics of import, objectives and sub-objectives Rank the attributes in terms of importance to OEC Determine the impact upon the OEC for each technology Page 27

28 Weightings and Sample Pair-wise Comparison Criteria Criteria I: Size Criteria II: Economics Criteria III: Operations Criteria IV: Safety and Reliability Weighting to OEC Pair-wise Comparisons Per Each Criteria A B Technology C D E A B 1/ Technology C 1/9 1/9 1 1/3 1/5 D 1/5 1/ /3 E 1/ Page 28

29 AHP Results Using SEI Employees: Weighted Average of OEC 0.50 Weighted Score of Overall Evaluation Critiera (OEC) Safety / Reliability Operations Economics Size 0.00 Long-Life High T/W Engine Airframe MMC Self-Healing TPS Densifiied Hydrogen Propellants Graphite Epoxy Propellant Tank Technology Page 29

30 Comparison

31 Observations on the Process In essence the computer models and simulations used for the evaluation part of ATIES act as proxy for the experts themselves - In AHP, the expert is all there is - In ATIES the expert submits weightings to the process which then works with the model or simulation Many times in the AHP discussion one is trying to weigh the impact of metrics upon the system simultaneously, a model can perform that activity much more efficiently and accurately than human intuition on the fly Evaluation of all possible combinations of technologies yields longer AHP evaluation times AHP s data management need grows in when using multiple criteria and subcriteria in different weighting scenarios Page 31

32 Risk Mitigation Through ROSETTA Analysis Module and ATIES Process Risk" is any type of uncertainty associated with the RLV program metrics and goals - Risk" is not the same as RLV "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 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 codes - The lack of knowledge about the future (and specifically the impact of technology) - Funding constraints on an organization Page 32

33 Contact Information Business Address: 1200 Ashwood Parkway Suite 506 Atlanta, GA U.S.A. Phone: Fax: Internet: WWW: President / CEO: Dr. John R. Olds Phone: john.olds@spaceworkseng.com Director of Technical Analysis: Mr. Andrew Crocker Phone: andy.crocker@spaceworkseng.com Director of Hypersonics: Dr. John Bradford Phone: john.bradford@spaceworkseng.com Senior Futurist: Mr. A.C. Charania Phone: ac@spaceworkseng.com Page 33