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Robustness And Reliability in Simulation Based Design
Process
Gaetan Van den BerghSander de Bruijn
Outline
• Introduction
• Research Projects
• Examples & Applications
Introduction
LMS International & Noesis Solutions
NumericalOptimization
ProcessAutomation
Design ofExperiments
RobustDesign
Simulation Process Management Solutions
NumericalOptimization
Design ofExperiments
RobustDesign
Simulation Process Management Solutions
SaveProcess
Time
No moremanual steps
No moremanual steps
FormalizeSimulationProcess
FormalizeSimulationProcess
Exploit Power ofParallelization
Exploit Power ofParallelization
Build Integrated Processes
Build Integrated Processes
NumericalOptimization
ProcessAutomation
RobustDesign
Simulation Process Management Solutions
MoreDesignInsight
ParameterScreeningParameterScreening
UnderstandParameterInteractions
UnderstandParameterInteractions
VisualizeDesignSpace
VisualizeDesignSpace
IntelligentDesign Space
Sampling
IntelligentDesign Space
Sampling
MoreDesignInsight
ProcessAutomation
RobustDesign
Simulation Process Management Solutions
OptimalProduct
Performance
Reach Pre-DefinedTargets
Reach Pre-DefinedTargets
ParameterIdentification &Test Correlation
ParameterIdentification &Test Correlation
ObjectiveTrade-offAnalysis
ObjectiveTrade-offAnalysis
Hunt for“Extreme”
Performance
Hunt for“Extreme”
Performance
MoreDesignInsight
Less Scrap,Warranty
Risks
Simulation Process Management Solutions
ProcessAutomation
OptimalProduct
Performance
FindReliableOptimum
FindReliableOptimum
AssessRobustnessof Design
AssessRobustnessof Design
MinimizeImpact ofVariability
MinimizeImpact ofVariability
CalculateProbability of
Failure
CalculateProbability of
Failure
Challenges for Product Innovation
• Improve competitiveness through Product and Process Innovation– Improve functional performances– Shorten market introduction and reduce development costs– Design for customer expectation
• Current answer: Systematic use of Virtual Prototyping– Applied more and more upfront– Ever larger models, mature techniques, faster computers
• But: What is the Validity and Relevance of these analysis results?– Production and material tolerances – Environmental condition influences – Structural degradation
Non-deterministic Behavior• Designers must consider this Non-Deterministic Behavior
– Understand potential envelope on performances– Understand main sources of response scatter– Understand which are sensitive parameters to control– Take design measures to minimize response variability (Robust Design)– Take design measures to guarantee specified performance (RBDO)
Some Definitions
• Variability– Variations inherent to modeled physical system or environment under
consideration (scatter, tolerances, … with known distributions)– Probabilistic inputs à Probabilistic responses – Reliability analysis: Assure confidence in response limits
– Design For Six Sigma: Guarantee very high reliability• Robust Design:
– Reduce sensitivity to critical parameters which are subject to U & V
– Reliability analysis, RBDO: Update the design to satisfy reliability targets
Reliability and Robustness
Failure Domain
Initial Design
ISO-Objective Function Contours
Search for Optimal point
Safe Domain
Robust & Reliable
Optimum
DeterministicOptimum
Improving Objective
Constraint Boundary
Variable 1
Varia
ble
2
Obj
ectiv
eAssess and Minimize Influence of Design Parameter Variability
Reliability Analysis Methods
• Limit State Approximations– FOSM (First Order Second Moment)– FORM / SORM (First / Second Order Reliability Method)
• Sampling Methods– Monte Carlo Simulation– Importance sampling, Directional Sampling
Research Projects
Some Research & Development by LMS/Noesis in framework of RTD projects
• EC Marie Curie “MADUSE” (2004-2008) on Uncertainty & Variability– EC Marie Curie Research & Mobility Network, 9 partners (Renault, Fiat, …)
across Europe• IWT “Analysis Leads Design” (2004-2007)
– RBDO framework, vehicle crashworthiness, sensitivity, durability, assembly, …
• EC NoE “InMAR” (2004-2008) – Intelligent Materials for Active Noise Reduction (ANC)– Optimization of ANC in presence of variability
• …
Examples & Applications
• AIM: Safer structural design by modifying material properties
• Use of Tsai-Hill criterion
Reliability Application: Composite Wing
+
+−
−=
2
12
12
2
2
22
1
21
2
1
11FFFF
G σσσσσ {{ G > 0 G > 0 SAFESAFE
G G << 0 0 FAILUREFAILURE
Reliability Application: Vehicle Knuckle
-10
-5
0
5
10
-10 -8 -6 -4-2 0 2
4 6 8 10
-2.5
-2
-1.5
-1
-0.5
0
0.5
E
Tensile Strength
Max
imum
Dam
age
-10 -8 -6 -4 -2 0 2 4 6 8 10-10
-8
-6
-4
-2
0
2
4
6
8
10
TS,
Ten
sile
Stre
ngth
E , E last ic Modulus
STARTING POINT
OPTIMIZED POINT
… and much faster Reliability-Based Design Optimization, with comparable accuracy as on full FE model RS RBDO Time
(seconds) FE refinement RBDO Time
(seconds) 26 80920
• Vehicle Knuckle with variability in material propertiesAim: Guarantee that fatigue life is sufficiently long
• DOE+RSM approach has been used, which enabled fast Reliability Assessment …
Robustness and Reliability Application: Slat Track Variability in Geometrical Properties
• Aim: Guarantee Robust and Reliable Fatigue Life Assessment• Measured test data to quantify Variability in geometrical properties• Mesh morphing to include geometrical tolerances
Robustness and Reliability Application: Slat Track Variability in Geometrical Properties
• DOE+RSM approach: fast Robustness & Reliability Assessment• Much faster Reliability-Based Design Optimization, with comparable
accuracy as on full FE model
Robustness and Reliability Application: A-Pillar Trim Impact
• Aim: Guarantee trim panel prevents head injury• Variability in Ribs Thickness, Impact velocity, Stress-Strain curve• Minimize Head Injury Criterion (HIC)• Analysis Procedure:
– Design Space Exploration– Refined Design of Experiments – Response Model using all DOE points
Rib Thickness most important parameter
800
1000
1200
1400
1600
1800
0,5 0,7 0,9 1,1 1,3 1,5
Ribs_thickness (mm)
HIC
(d)
Robustness and Reliability Application: A-Pillar Trim Impact
• Reliability analysis of initial configuration: pf = p(HIC > 1000)
• Deterministic Optimization on the HIC
• RBDO: Reliable optimum practically coincident with deterministic optimum
0
50
100
150
200
250
300
350
400
0,000 0,005 0,010 0,015
Time (s)
Acc
eler
atio
n (G
's)
0,5 mm0,85 mmRib
ThicknessOptimized
Conclusion
Conclusion• Reliability Analysis are key to robust design
• Assess the response scatter & failure probability• Understand root causes • Reliability Based Design Optimization
• Industrial awareness is increasing• Technology is getting to stage where application
to industrial problems becomes feasible• Automotive and Aerospace applications
• Tools (even first product releases in OPTIMUS) are available• LMS and Noesis are active in research & technology development
• On analysis methods• On enabling technologies for fast re-analysis
More information• Visit us at our booth
• OPTIMUS & LMS Virtual.Lab Product Information• Uncertainty, Robustness and Reliability Papers• OPTIMUS & LMS Virtual.Lab Industrial Application Cases
• Register for OPTIMUS Webseminar• Tuesday 2 Oct 2007, 10 am• Send an email to gaetan.vandenbergh@noesis.be
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