Reliability-based Design, Development and Sustainment...Reliability-based Design, Development and...
Transcript of Reliability-based Design, Development and Sustainment...Reliability-based Design, Development and...
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S1SP ARA0127-1
ReliabilityReliability--based Design, Development based Design, Development and and SustainmentSustainment
NDIA’s 2007 T&E Conference: T&E in Support of Operational Suitability, T&E in Support of Operational Suitability,
Effectiveness and Effectiveness and SustainmentSustainment of Deployed of Deployed SystemsSystems
Dr. Anne Hillegas and Dr. Justin Wu, ARA15 March 2007
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Briefing OverviewBriefing Overview
Description of reliability-based methods
Applications and results
Implications of reliability-based methods for T&E
Challenges
Path ahead
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ARA BUSINESS AREASARA BUSINESS AREASNational DefenseTransportationSecurity Risk & Disaster ManagementGeotechnical & Environmental TechnologiesComputer Software & Supporting Technologies
Probabilistic Function Evaluation System
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IntroductionIntroductionReliability-based methods are those that
Use the probability of failure as a criterion in the design process
These methods contribute to suitability, effectiveness and sustainability by
Improving system performanceIncreasing operational readinessReducing unnecessary intervention or maintenanceManaging spare parts inventoriesReducing technical and operational risk
Other benefits of these methodsProvide predicted performance across a range of metricsSupport decision-makers by highlighting trade-offs in performance and RAM
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The “Magic” behind the MethodsThe “Magic” behind the Methods
These methods involveApplying probability distributions to uncertaintiesUsing physics-based modeling to assess the impact of these uncertain factors on system performanceBalancing system design features and inspection intervals against risk
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Apply State-of-the-Art probabilistic methodsApply State-of-the-Art probabilistic methods
PhysicsPhysics--based Probabilistic Analysisbased Probabilistic Analysis
Develop High Fidelity Computational Models
Geometry
Environment
Material
Optimize:- Risk and Reliability- Maintenance Plan- Test Plan
Optimize:- Risk and Reliability- Maintenance Plan- Test Plan
Uncertainty analysis based on fast probability integration andadvanced random simulations
Other Uncertainties
Compute:- Risk- Reliability- Reliability sensitivities- Response probability
distributions- Failure samples for
maintenance simulation
Compute:- Risk- Reliability- Reliability sensitivities- Response probability
distributions- Failure samples for
maintenance simulation
Model Uncertainties and Variability
Failure Sampling
PRObabilistic Function Evaluation System
© Applied Research Associates, Inc. All Rights Reserved
Most Likely Failure PointJoint probability density
Focus on “Weakest Links” and Most Likely Causes for FailuresFocus on “Weakest Links” and Most Likely Causes for Failures
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ReliabilityReliability--based Methods that support based Methods that support Suitability, Effectiveness and SustainabilitySuitability, Effectiveness and Sustainability
Reliability-based multidisciplinary optimization (RBMDO)Reliability-based damage tolerance (RBDT)
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ReliabilityReliability--based Multibased Multi--disciplinary disciplinary Optimization (RBMDO)Optimization (RBMDO)
Optimizes performance subject to multiple reliability-based constraintsIncorporates multi-disciplinary objectives/models (e.g., payload, aerodynamics, shape parameters, weight,…)Accomplishes higher performance over independent optimization of each discipline
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RBMDO Application RBMDO Application –– Aircraft Wing DesignAircraft Wing Design
NASTRAN model of Advanced Composite Technology (ACT) wingBaseline aircraft: proposed 190-passenger, two-class, transport aircraftCritical Design conditions derived from DC-10-10 and MD-90-30
3804 finite element nodes3770 finite elements (2222 shells + 1548 beams)22 material properties47 shell element properties
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PerformancePerformance--based objective and reliabilitybased objective and reliability--based constraintsbased constraints
Weight is reduced while reliability is improvedWeight is reduced while reliability is improved
Nor
mal
ized
Wei
ght
Normalized Safety (1/Risk)
0.964
0.9750.987Original
0.90
0.92
0.94
0.96
0.98
1.00
1 10 100
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Highway Inspections
Another Application Another Application –– Radiation Detector Radiation Detector SustainmentSustainment
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ReliabilityReliability--Based Damage Tolerance (RBDT) FrameworkBased Damage Tolerance (RBDT) Framework
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Crack Size
PODInitial
Critical Size
At Insp.
Cum
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babi
lity
Non-Destruc. Inspection Planning
Principal StructuralElement Model
- stress model- life model
Principal StructuralElement Model
- stress model- life model
ProFES
Crack Size
Initial Flaw
Time (Flight hours)
1st Insp.
Fracture Mechanics Damage Tolerance
Update distributionafter maintenance
2nd Insp.
Flaw or FOD Stress
Material Modeling Error
UsageLoad Spectra
Inspection time
Time
Fully Integrated Finite Element stress, Fracture Mechanics life and ProFES analyses
Failure Sampling
Joint probability density
Most Likely Failure Point
Failure Samples
Flight Hours
Prob
. of F
ract
ure
Without InspectionWith Inspection
Max. risk reduction
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Project sponsored by FAACritical structures must maintain very small probability of failureSupplement current “safe-life” design approach (which tends to be too conservative)Systematically treat variability/uncertainty in:
usage, load, flaw, material, geometry, modeling error, defect detection capability
Maintenance planning for:Non-Destructive Inspection, inspection frequencies, repair/replacement
Has wide applicability to structures with material or manufacturing flaws
Select appropriate NDI intervalOptimize sustainment strategies
ReliabilityReliability--Based Damage Tolerance (RBDT) Methodology Based Damage Tolerance (RBDT) Methodology for Rotorcraft Structuresfor Rotorcraft Structures
DistributionsBounds or Safety Factors
Other Variables
ReliabilitySafety marginSafety Measure
Optimized schedules for max. risk reduction
Life/No. of inspections
Inspection Schedule
0 < Probability < 1 Certain (Safe-Life)Flaw Existence
Probabilistic distributionA given crack sizeFlaw/Defect size
Probability & confidenceBounds or Safety Factors
Underlying Principles
ProbabilisticDeterministic
Flight Hours
Prob
. of F
ract
ure Without Inspection
With Inspection Max. risk reduction
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RBDT Application RBDT Application ---- Rotorcraft Spindle Lug ModelRotorcraft Spindle Lug Model
Goal Goal –– Optimize inspection schedule to minimize riskOptimize inspection schedule to minimize risk
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RBDT Yields Optimal Inspection ScheduleRBDT Yields Optimal Inspection Schedule
Service Insp Crit. parts
Reducible Risk
[ ( ) ( )] [ ( )]C D
o of f
p p
p t p t E POD a
= ⋅
= − ⋅
Systematic approach for probabilistic fracture mechanics damage tolerance analysis with maintenance planning under various uncertainties
Stage 1: compute risk without inspectionStage 2: compute risk with inspection by simulating inspection and maintenance effects using the samples generated from the Stage 1 failure domain
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ReliabilityReliability--based Damage Tolerance Applicationbased Damage Tolerance ApplicationPipeline Maintenance OptimizationPipeline Maintenance Optimization
Corrosive environments cause metal lossesTwo major failure modes with uncertainties
Burst can cause a high consequence but is less likely to occurLeak has a low consequence but is more likely to occur
In-line inspection devices can detect significant defects Options to maintain integrity at different risk/cost:
Defect monitoring using high resolution ILI devices (cost issue)Repair/replace sections (cost issue)Reduce operating pressure (production loss)Corrosion mitigation (cost issue)
ObjectiveDevelop maintenance optimization software using ILI data and probabilistic failure and cost models
In Line InspectionMagnetic Flux Leakage and other ILI
devices can travel through pipelines to detect metal losses
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1Pf at 20 Yrs vs. Inspection Time
Pf a
t 20
Yrs
Next Inspection Time (yrs)
Optimal Inspection Time at 3 Yrs
Prob. Defect Model
Prob. Defect Model
ILI DataILI DataProb.
CorrosionModel
Prob. Corrosion
ModelRisk/Reli-Based Decision Model
Risk/Reli-Based Decision Model
Maintenance Options
Maintenance Options
Opt. Maintenance• Repair/No Repair• Repair/Replace• Repair Methods• Future InspectionSchedules
Opt. Maintenance• Repair/No Repair• Repair/Replace• Repair Methods• Future InspectionSchedules
ILI Meas. Error
ILI Meas. Error
Prob. Burst/Leak
Models
Prob. Burst/Leak
Models
Prior Operating Conditions
Prior Operating Conditions
Projected Operating Conditions
Projected Operating Conditions
Cost ModelCost
Model
Risk is minimized if the next inspection and repair time is 3 years from the last inspection
Pipeline Maintenance Example Pipeline Maintenance Example
Pipeline Maintenance Pipeline Maintenance Optimization MethodologyOptimization Methodology
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Another Application Another Application –– Robotic/Unmanned Systems Robotic/Unmanned Systems DesignDesign
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Implications for T&EImplications for T&E
Material Constitutive Modeling
Component Analyses
Subassembly Analyses
Global Analyses
Material Testing
Observables
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LON
GIT
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INA
L S
TRE
SS
(G
Pa)
0.030.020.010.00-0.01-0.02TRANSVERSE STRAIN LONGITUDINA
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15o
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22.5o
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30o
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45o
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60o
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Experimentaldata
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ExperimentCalculation
StrainS
tress
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Validation Tests
Building block approach Building block approach requires testing at all levelsrequires testing at all levels
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WrapWrap--upup
AccomplishmentsTools for taking a reliability-based approach to design and sustainment that can result in cost savings and risk reductionTraceable predictions of reliability and performance changesComputationally fast and efficient methods
ChallengesScalability of approach Understanding material failure propertiesCascade of variable and interrelationships
Non-unitized structures“User friendliness”
Enable use by decision-makers
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Path AheadPath Ahead
Mature/prove methods for specifying probability distributions
ARA’s Klein Associates Division (cognitive scientists)
Refine the understanding of the impact and interaction of the uncontrollable random variablesAccelerate collection of data to inform physics-based modelsContinue to evolve cost- and time-effective testing approaches for verifying reliability