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Incorporating System-level Probabilistic Incorporating System-level Probabilistic Reliability Into the Multi-disciplinary Reliability Into the Multi-disciplinary
Component Design ProcessComponent Design Process
A status reportA status report presented to
Dr. William E. Vesely Manager, Risk Assessment – Code Q, NASA Headquarters
by
N&R Engineering and Management Services, Inc.Parma Heights, Ohio 44130
NRengineering.com
Bill Strack, John Gyekenyesi, and Vinod Nagpal November, 2007
Many Relevant Software Tools Already ExistMany Relevant Software Tools Already ExistN&R Engineering
A user-friendly framework to integrate existing NASA and commercial codes into an overall reliability simulation that accounts for uncertainties and permits data sharing.
Discipline Example code
Constituent-level material properties models CEMCAN
Design modeling codes ANSYS
Finite element modeling structural analysis ANSYS, NESTEM
Manufacturing process models ProCAST
Life prediction methods NASAlife
Probabilistic reliability analysis (PRA) QRAS, SAPHIRE
However:
• These are independent codes that cannot communicate with each other.
• Most of these codes ignore uncertainties – they are deterministic simulations.
What is needed:
Vision for Reliability AnalysesVision for Reliability Analyses
VVision:
Most future reliability analyses will account for uncertainties – both aleatory and epistemic.
Design processes will consider system reliability as a constraint at the component level.
GGoal:
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Develop a physics-based, multi-disciplinary future design tool (PRODAF) that enables this vision -- utilizing existing deterministic codes.
Review of PRODAF Development Status
NASA GRC Physics-based Probabilistic Reliability RoadmapNASA GRC Physics-based Probabilistic Reliability RoadmapN&R Engineering
Capability
Year2001 2003 2005 2007 2009
PRODAF
SUA Code AE
1999
PRODAF SBIR Phase I
NESTEM
Applications to real problems
- SBIR Phase II - Code Q
PRODAF: Probabilistic Design and Analysis Framework
Code Q funding
Other funding
Complementary funding sources
Example SUA Problem: Probabilistic NASAlifeN&R Engineering
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Example Help Message
Develop a robust online help system to guide users -- select codes and inputs, detect input errors, suggest error recovery strategies.
Example: Provide guidance on how to select a distribution type for new users.
Example Architecture to Implement the PRODAF Vision
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Main PRODAF User Interface
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PRODAF Design Space Options
1. Execute the design process for a single design point
2. Perform a parametric design space exploration
3. Optimize a set of design variables – deterministically
4. Optimize a set of design variables – probabilistically
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Example MDO Problem
Turbine engine performance code - fan pressure ratio - compressor pressure ratio
Airplane performance, weight, economics code - wing loading - wing aspect ratio - wing LE sweep angle
Data interface
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PRODAF Optimization Manager
Generalized Reliability Analysis Procedure
Convergence to MPP?
Limit State Approximation Adaptive Response Surface
FORM, SORM, Monte Carlo based on Response Surface
Failure Probability
Initial Focus Region(Centered at Mean Values)
No
Focus Region Update
Minimum Distance in Focus Region Sequential Quadratic Programming
Adaptive Importance Sampling
Design of ExperimentsOptimal Symmetric Latin Hypercube
No
FORM, SORM, MCresults close enough?
Yes
Yes
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OSLH Design: Example
(a) 9X2 Random LH Design
0
0.125
0.25
0.375
0.5
0.625
0.75
0.875
1
0 0.1250.250.3750.50.6250.750.875 1X1
X2
(b) 9X2 Symmetric LH Design
0
0.125
0.25
0.375
0.5
0.625
0.75
0.875
1
0 0.130.250.38 0.5 0.630.750.88 1X1
X2
(c) 9X2 OSLH Design
0
0.125
0.25
0.375
0.5
0.625
0.75
0.875
1
0 0.130.250.38 0.5 0.630.750.88 1X1
X2
9x2 Latin Hypercube Designs
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Iteration history of searching for the MPPfor x1^3+x2^3-18=0 using quadratic local approximation
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
-2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0
X1'
X2
'
Limit StateTrust region 1Trust region 2Trust region 3Trust region 4Approx1Approx2Approx3Approx4Iteration history
MPP Search : Numerical ExampleN&R Engineering
X1’
X2’
Limit state
Focus region 1
Focus region 2
Focus region 3
Focus region 4
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PRODAF FY2007 Tasks
Provide confidence bounds that account for uncertainties in the uncertainty parameters.
Surrogate adaptive response surface approach for optimization using a 2-phase global/local MPP search method to handle non-linear, implicit limit state functions.
Facilitate physics-based progressive failure modeling of complex systems.
Provide a mechanism for distributed computing capability.
Component Mean failure probability
Shaft 0.000000001
Disk 0.03511
Fan blade 0.00143
System (shaft, disk, 24 fan blades) 0.06818
Application of PRODAF to Shaft/Disk/24-Blade System 1800 RPM, Tshaft = Tdisk = 600 °F, Tblade = 400-700 °F, Time = 50,000 sec.
System failure probability
CDF
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Current PRODAF Development Tasks
PRODAF Development Task SBIR II Code Q Status
Task 1 – Develop confidence intervals with uncertain uncertainties 75%
Task 2 – Develop rapid adaptive response surface methodology 100%
Task 3 – Develop progressive failure modeling of complex systems 60%
Task 4 – Develop executive code (GUI, distributed computing, help) 80%
Task 5 – Provide automatic code/data interfacing during design process 100%
Task 6 – Interface process-based cost modules such as P-BEAT 2%
Task 7 – Validate code and usefulness with commercial customer (GE) 2%
Task 8 – Provide example applications to relevant NASA problems 25%
Task 9 – Author comprehensive methodology/theory and users manual 55%
Task 10 – Develop robust reliability-based design space optimization 85%
Task 11 – Provide robust online user help system 60%
Task 12 – Establish a code and data management system 90%
SBIR II: “Physics-based Probabilistic Design Tool with System-Level Reliability ConstraintPhysics-based Probabilistic Design Tool with System-Level Reliability Constraint ””
Code Q:Code Q: ““Physics-based Multi-disciplinary PRA Physics-based Multi-disciplinary PRA DDesign esign SSystem for ystem for RReliabilityeliability””
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PRODAF Milestones and Deliverables as Defined in Original Proposal
Methodologies
Software
Deliverables
FY07 FY08
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1
Methodology Milestones Software Milestones .
1. Response surface methodology and coding complete
2. System-level progressive failure methodology complete
3. Confidence interval methodology complete
4. Code interfaces, cost module, 1st example problem complete
5. Robust design space optimization methodology complete
Deliverables (in addition to quarterly/annual progress reports)
A PRODAF software version 1.0 (includes confidence intervals and response surface features)
B PRODAF software version 2.0 (includes all methodologies/features)
C Final Theoretical Report and Users Manual
3
4
2
B C
4
6. Code/database management system
7. Online help system completed
8. Distributed computing implemented
9. External evaluation/validation complete
10. Ares application examples completed
7
5
1 2 3
A
56 8 9 10
Dr. Satchi VenkataramanSan Diego State University
Dr. Sankaran MahadevanVanderbilt University
PRODAF Project Organization
Confidence interval methodology Optimization with uncertainty
Response surface methodology
System-level progressive failure
Robust design space optimization
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N&R Project Manager
NASA COTRs
Software development
On-line help system
Distributed computing
Mr. William StrackN&R Engineering
Dr. Shantaram S. Pai & Ed Zampino
Dr. John Z. GyekenyesiN&R Engineering
Software testingGeneral Electric
Dr. Vinod K. NagpalN&R Engineering
Life prediction methodology Manufacturing processes Example applications
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FY07 PRODAF Code Q Funding
Proposed Tasks FY07
Validate code and usefulness with commercial customer (GE) 77K
Provide example applications to relevant NASA problems 21
Develop robust reliability-based design space optimization 55
Provide robust online user help system, theory, users manuals 57
Establish a code and data management system 43
Total $253K
Contractual effort: $253K
Funding actually provided in FY07: $110K
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Proposed FY08 PRODAF Code Q Funding
Proposed Tasks FY08
Validate code and usefulness with commercial customer (GE) 90K
Provide example applications to relevant NASA problems 140
Add cost estimating modules to PRODAF 60
Provide theory and users manuals 30
Total $320K
Contractual effort: $320K
PRODAF will provide engineers with user-friendly tools to conduct a broad spectrum of probabilistic reliability analyses using existing deterministic modeling codes. It will provide a practical reliability-based design framework that captures the impact of uncertainties.
SummaryN&R Engineering
Backup Charts
Multiple runs
FortranC/C++Excel spreadsheet
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The Systems Uncertainty Analysis (SUA) Tool
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PRODAF Code Database Manager
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PRODAF Probabilistic Manager
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PRODAF Uncertainty Definition Dialog
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Uncertainty in Statistical Parameters
• Parameters estimated from data with measurement errors– Random and bias errors
• Parameters estimated from limited number of samples – Scenario 1: Actual test data available –small sample size O(10)– Scenario 2: Population distribution and sample size known– Scenario 3: Population distribution known, sample size unknown– Scenario 4: Population distribution unknown, sample size known– Scenario 5: Population distribution and sample size unknown
• Parameters specified by experts (no test data) – Scenario 6: Bounds specified but distribution is not– Scenario 7: Bounds specified with most likely value
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““Physics-based Multi-disciplinary PRA Design System for ReliabilityPhysics-based Multi-disciplinary PRA Design System for Reliability”” Tasks
Enable automatic code/data interfacing during the design process
Integrate cost module into PRODAF (e.g., NAFCOM) including optimum sparing strategy
Develop NASA-relevant application example
Comprehensive theory and users manuals
Option A Option B
Adv. Development $cx1 $cy1
DDT&E cx2 cy2
Production cx3 cy3
Launch cx4 cy4
LLC $cx $cy
CEV-SM
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Acronyms
CALCE Center for Advanced Life Cycle Engineering’s code to estimate failure probability of printed circuit cards
CEMCAN Ceramic Matrix Composites Analyzer
NASAlife NASA life prediction code
NESTEM Hybrid of NESSUS and CSTEM codes
PRODAF Probabilistic Design and Analysis Framework code
QRAS Quantitative Risk Assessment System (PRA code)
SAPHIRE Systems Analysis Programs for Hands-on Integrated Reliability (PRA code)
SUA Systems Uncertainty Analysis code
Example Probabilistic Analysis Using SUA
SSME Fuel Turbopump Temperature
Critical Space Shuttle reliability component is the SSME high-pressure fuel turbopumps
N&R Engineering
Fuel turbopump temperature Probabilistic sensitivity factors
SSME system model
Power Output of the ISS EPS Probabilistic Sensitivities
ISS EPS Uncertainties
Probabilistic Analysis of ISS Electrical Power System (EPS)N&R Engineering
Application of Probabilistic Methods to Honeywell Blade MistuningN&R Engineering
DoD SBIR
QRAS
NESTEM or CALCE
Fetch probabilistic data
Process data to get Pfail
Call Delphi code to modify QRAS DB
Display results to user
Create Delphi input file
Phase I C++ interface code
No provision in QRAS to import component data – uses multiple Paradox binary relational database tables to store internally generated input data.
files
Fetch probabilistic data
Process data to get Pfail
Display results to user
Phase II C++ interface code
Modify QRAS DB
QRAS
NESTEM or CALCE
files
PRODAF NESTEM to QRAS InterfaceN&R Engineering
Example QRAS Interface using NESTEM and CALCEN&R Engineering
Example Architecture for Physics-based Probabilistic Probabilistic Design with System-Level Reliability ConstraintDesign with System-Level Reliability Constraint
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PRODAF
SUA
Code Q
Manufacturing Process Simulation InterfaceN&R Engineering
Development of Probabilistic Structural Analysis Integrated with Deformation Resistance Annular Welding Simulation: Dr. Anantanarayanan, Delphi Energy & Chassis Systems
Residual stress for SS316
ProbDRAW pre-processor
- Import IGES files to DEFORM files
- Convert cdb ANSYS files to DEFORM files
- Setup and run DEFORM
- Integrate with NESTEM
- Integrate with other mfg. simulation codes
Completed
Unfunded
DEFORM Simulation
Underway
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