reliability-based design optimization using response
TRANSCRIPT
Structural & Multidisciplinary Optimization Group
EGM 6934 Experimental Engineering Optimum Design
Reliability-based Design Optimization Using Response Surface Approximations
Xueyong QuRaphael T. Haftka
Nov 13, 2002
Structural & Multidisciplinary Optimization Group
O tliOutline• Background and MotivationBackground and Motivation
– Deterministic design Vs. Reliability-based design
M C l Si l i (MCS)• Monte Carlo Simulation (MCS)
• Reliability-based Design Optimization (RBDO) UsingReliability based Design Optimization (RBDO) Using Response Surface Approximations
C l di R k• Concluding Remarks
Structural & Multidisciplinary Optimization Group
B k dBackground
• Deterministically optimized laminates are sensitive toDeterministically optimized laminates are sensitive to uncertainties– Deterministic design push design to the boundary of the constraints.
ib li d l h di i f idi i l l di– Fibers aligned along the direction of unidirectional loading – Poor performance to loading transverse to fiber direction
F
Structural & Multidisciplinary Optimization Group
D t i i ti d i R li bilit b d d iDeterministic design vs. Reliability-based design
• Deterministic design (Safety factor or worst case)Deterministic design (Safety factor or worst case) Typically use experience-based safety factor to knock down allowables
1.4 for space applications D i t ll t d i i i li ti Design concepts well accepted in engineering application Unknown actual safety level Usually not optimal in terms of weight and safety
• Reliability-based design (Probabilistic design) Computationally expensive
• Computational cost of a single reliability analysis is on par with aComputational cost of a single reliability analysis is on par with a deterministic optimization
• Total cost of RBDO is similar to multi-level optimization Complex to apply
Complex to apply
Structural & Multidisciplinary Optimization Group
D t i i ti D i f C it L i tDeterministic Design of Composite Laminates
• Design of angle-ply laminateDesign of angle-ply laminate– Maximum strain failure criterion
4 tth minimize NAxial 214 tth minimize
such thattc
NAxial
NHoop
y
1 2
1212
222
111
u
tc
x
2
1
1212
005.00.005
tt
Load induced by internal pressure:
2NHoop = 4,800 lb./in., NAxial = 2,400 lb./in.
Structural & Multidisciplinary Optimization Group
Summary of Deterministic DesignSummary of Deterministic Design
• Optimal ply angles are 27 from hoop direction• Optimal ply-angles are 27 from hoop direction
• Laminate thickness is 0.1 inchLaminate thickness is 0.1 inch
• Probability of failure (510-4) is high with safety factor 1 4• Probability of failure (510 ) is high with safety factor 1.4.
Structural & Multidisciplinary Optimization Group
R li bilit b d L i t D iReliability-based Laminate Design
4hi i i i i bl 214 tth minimize
that such
• 4 Design Variables– 1, 2, t1, t2
1005.0 tPP t
• 12 Normal Random Variables
– Tzero (CV = 0.03)– 1, 2 (CV = 0.035)
2005.0 t
Pt = 10-4
1, 2 (CV )– E1, E2, G12, 12 (CV = 0.035)– 1
c, 1t (CV = 0.06)
c t u (CV 0 09)
• First ply failure principle
Pt 10 – 2c, 2
t, 12u (CV = 0.09)
Structural & Multidisciplinary Optimization Group
Probability of Failure and Limit StateProbability of Failure and Limit Statex2 G(x)<0G(x) 0
Unsafe Region
G(x)>0
G(x)=0Limit State
x1
( )Safe Region
• Probability of failure xxx
X dfPG
0)(
)(
• Integral is hard to evaluate: unknown integration domain and
• Integral is hard to evaluate: unknown integration domain and high dimension.
Structural & Multidisciplinary Optimization Group
Probability Calculation• Monte Carlo simulation (MCS)
– Large number of samples for small probability, computationally expensive
– Random errors (noise) due to limited sample size
• Moment-based methods– Computationally efficient for single failure mode problem– Difficult for problems with multiple failure modes
• Response surface approximation (RSA)Response surface approximation (RSA)– Reduce computational cost of MCS– Filter out noise
Structural & Multidisciplinary Optimization Group
M t C l Si l tiMonte Carlo Simulation
P b bilit l l ti insidePP• Advantages
simple robust and accurate with large enough samples
Probability calculation:total
inside
PP
– simple, robust, and accurate with large enough samples.
• Disadvantages– Computational cost is high for small probability.
– Random errors (noise) hinder design optimization.
Structural & Multidisciplinary Optimization Group
Estimating Probability Using MCSg y gSample size Analytical
solution = /400MCS solution Relative errors
1e2 0 007854 0 00 100%1e2 0.007854 0.00 100%
1e3 0.007854 0.004 49%
1 4 0 007854 0 0085 8%1e4 0.007854 0.0085 8%
1e5 0.007854 0.00847 7.8%
1e6 0.007854 0.007851 0.04%
• Results of MCS changes from simulation to simulation• Do not use the same seed for random number generators,
unlessDebug program
– Debug program– Sensitivity study
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Response Surface Approximation (RSA)Response Surface Approximation (RSA)
A i l i hi b d
y
• Approximate relationship between y and x– Assume basis function z(x), least square estimate
to obtain b– Low order polynomials as basis
x1
x2bxzx T)()(y
• Statistical design of experiments (DOE)– select design points, to obtain desired accuracy at
i i
y=f(x1,x2,…,xn)+e
minimum cost.– Face center central composite design (FCCCD)– Latin Hypercube sampling (LHS)
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Central Composite Design (CCD)Central Composite Design (CCD)• Classical DOE for quadratic RS• FCCCD
– Move axial points to surface of the hypercube
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Response Surface Optionsp p Design response surface approximation (DRS)
Response design ariables: W=W(d)– Response v.s. design variables: W=W(d)– Used in optimization: Venter and Haftka (96, 97), Roux et al. (96), etc.
• Stochastic response surface approximation (SRS)– Response v.s. random variables: G=G(x)– Used in probability calculation: Fox (94, 96), Romero (98), etc.– Need to construct SRS at every point encountered in optimizationy p p
Analysis response surfaces (ARS, Qu et al., 2000)– Response v.s. random variables + design variables: G=G(x, d)
Ad t i ffi i f SRS– Advantage: improve efficiency of SRS– Challenges:
• DOE for both design and random variablesC S i hi h di i l ( 10 i bl )
• Construct RS in high dimensional space ( > 10 variables)
Structural & Multidisciplinary Optimization Group
A l i R S f (ARS)Analysis Response Surfaces (ARS)Strain
• Fit strains in terms of 12 variables• Design of experiments:
L ti H b S li (LHS)– Latin Hypercube Sampling (LHS)
D.V.R.V.
Strain = g(θ1, θ2, t1, t2, E1, E2, G12, 12, 1, 2, Tzero, Tservice)
ARS
• Probabilities calculated by MCS based on fitted polynomials– Reduce computational cost of MCS
p
Structural & Multidisciplinary Optimization Group
Latin Hypercube Sampling (LHS)Latin Hypercube Sampling (LHS)
Uniform
NormalC
• Design of experiments for RSA– Sample the random variable space according to distributions
(Courtesy of Wyss and Jorgensen)
Sample the random variable space according to distributions– Design variables are treated as uniformly distributed within design
bounds– Space-filling feature
– More flexible than orthogonal arrays: arbitrary number of design points
Structural & Multidisciplinary Optimization Group
R li bili b d D i O i i iReliability-based Design Optimization
• Design Response Surface (DRS) ARS• Design Response Surface (DRS)– Fit to Probability in terms of 4 D.V.– Filter out noise generated by MCS
ARS
DOE & MCS– Used in RBDO
ProbabilityDRS
Optimization
i
tiConverge?
Yes
No No
iPOF = p(θ1, θ2, t1, t2) Stop
Structural & Multidisciplinary Optimization Group
ApproximationApproximationDesign variables 1 2 t1 (inch) t2 (inch) Range 20 to 30 20 to 30 0.0125 to 0.03 0.0125 to 0.03
Quadratic ARS based on LHS 182 points
ARS Error Statistics 2 in 1
Rsquare Adj. 0.996
RMSE Predictor (millistrain) 0.060
Mean of Response (millistrain) 8 322
FCCCD 25 points LHS 252 points DRS Error Statistics
th
Mean of Response (millistrain) 8.322
quadratic 5th order
Rsquare Adj. 0.686 0.998
RMSE Predictor (probability) 5.3e-4 0.12e-4
(p y)
Mean of Response (probability) 3.2e-4 0.44e-4
Structural & Multidisciplinary Optimization Group
Optimization
• Deterministic, Reliability-based, and Simplified designs
Pl A l Thi k (i h) Probability of Ply Angles Thickness (inch) Probability of Failure
Deterministic [(27.0)2/(27.0)3]S 0.10 5e-4
Reliability [(24.9)3/(25.2)3]S 0.12 0.55e-4Reliability [(24.9)3/(25.2)3]S 0.12 0.55e 4
Simplified [(25)6]S 0.12 0.57e-4
• The thickness is high for application
Structural & Multidisciplinary Optimization Group
Improving Reliability-based DesignImproving Reliability-based Design• Reliability-based design
– Thickness of 0 12 inchThickness of 0.12 inch– Probability of failure of 10-4 level
Must reduce uncertainties: Quality control (QC)
– Reject small numbers of poor specimenT t di t ib ti f ll bl t l id ( 2 )– Truncate distribution of allowables at lower side (–2 )
Reduce material scatter– Reduce Coefficient of Variation (CV)– Better manufacture process (Better curing process)
Improve allowablesI M V l f ll bl
– Increase Mean Value of allowables– New materials
Structural & Multidisciplinary Optimization Group
Change Distribution of 2 allowable• Reduce scatter (CV) by 10%• Reduce scatter (CV) by 10%
Probability of failure (0.12 inch)
CV = 0 09 CV = 0 081 CV = 0.09 CV = 0.081
[(25)6]S 0.57e-4 0.11e-4
• Increase allowable (Mean value) by 10%
Probability of failure Probability of failure (0.12 inch)
E(ε2u) = 0.0154 E(ε2
u) = 0.01694
[(25)6]S 0.57e-4 0.03e-4
Structural & Multidisciplinary Optimization Group
Quality Control (QC) on 2 allowable• Reduce probability of failure
Probability of failure (0.12 inch) Truncate at 3 Truncate at 2 (0.12 inch)
Normal Truncate at -3 (14 out of 10,000)
Truncate at -2 (23 out of 1,000)
[(25)6]S 0.57e-4 0.001e-4 < 1e-7
• Reduce thickness
0.12 inch 0.10 inch 0.08 inch (POF=1e-4)
Normal Truncate at –2.8 (26 out of 10,000)
Truncate at -1.35 (90 out of 1,000)
[(25)6]S 0.57e-4 0.9e-4 1e-4
[( 5)6]S 0.57e 4 0.9e 4 1e 4
Structural & Multidisciplinary Optimization Group
Tradeoff PlotTradeoff Plot
1.0E-01
1.0E+00NominalQuality control to -2 Sigma10% increase in allo able
1.0E-03
1.0E-02
babi
lity
10% increase in allowable10% reduction in variabilityAllS i 6
1 0E 06
1.0E-05
1.0E-04
Failu
re P
rob
1.0E-08
1.0E-07
1.0E-06F
1.0E 080.06 0.08 0.1 0.12 0.14 0.16
Thickness (inch)
• To be chosen by the cost of implementing these methods
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Concluding RemarksConcluding Remarks
• 2 allowable is the key parameter• Probability of failure (510-4 to 0.610-4) Vs. Thickness (0.1 to
0.12 inch)
• Three methods to improve the design– Quality control, Reducing scatter, and Increase mean value of 2
allowableallowable– Changing other parameters are not as efficient as changing 2 allowable
• Analysis RS combining random and design variables is efficient– DOE: Latin Hypercube sampling (LHS)