Zissimos P. Mourelatos, Associate Prof. Daniel N. Wehrwein, Graduate Student
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Transcript of 1 Probabilistic Re-Analysis Using Monte Carlo Simulation Efstratios Nikolaidis, Sirine Salem,...
![Page 1: 1 Probabilistic Re-Analysis Using Monte Carlo Simulation Efstratios Nikolaidis, Sirine Salem, Farizal, Zissimos Mourelatos April 2008.](https://reader031.fdocuments.net/reader031/viewer/2022032201/56649cfa5503460f949cbe56/html5/thumbnails/1.jpg)
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Probabilistic Re-Analysis Using Monte Carlo Simulation
Efstratios Nikolaidis, Sirine Salem, Farizal, Zissimos Mourelatos
April 2008
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Definition and SignificanceProbabilistic design optimization• Find design variables • To maximize average utility
RBDO • Find design variables• To minimize loss function • s. t. system failure probability does not exceed allowable value
• Often average utility or system failure probability must be calculated by Monte Carlo simulation. – Vibratory response of a dynamic system: failure domain consists of multiple disjoint
regions
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Definition and Significance
Challenge: High computational cost• Optimization requires probabilistic analyses
of many alternative designs• Each probabilistic analysis requires many
deterministic analyses• Expensive to perform deterministic analysis
of a practical model
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Definition and Significance
Vibratory door displacement
Excitation at engine mounts
Reliability analysis
Monte Carlo Simulation (10,000 replications)
Probability of failure
RBDO
Search for optimum (100- 500 Monte Carlo Simulations)
Optimum
Deterministic FEA
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Outline
1. Objectives and Scope2. Probabilistic Re-analysis
– RBDO problem formulation– Method description– Sensitivity analysis
3. Example– Preliminary Design of Internal Combustion
Engine Conclusion
4. Conclusion
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1. Objectives and Scope
• Present probabilistic re-analysis approach (PRA) for RBDO– Estimate reliability of many designs by performing a
single Monte-Carlo simulation– Integrate PRA in a methodology for RBDO– Demonstrate efficacy
• Design variables are random; can control their average values
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2. Probabilistic Re-analysis
RBDO problem formulation:– Find average values of random design
variables – To minimize cost function – So that
Xμ)( Xμl
allfsys pIPp ]1),,([ PXd
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Reducing computational cost by using Probabilistic Re-analysis
1. Select a sampling PDF and perform one Monte Carlo simulation2. Save sample values that caused failure (failure set)3. Estimate failure probability of all alternative designs by using failure
set in step 2
x1
x2
Failure region
Sampling PDF
Alternative designs Failure set
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• Failure probability
• Confidence in failure probability estimate
• Similar equations are available for average value of a function of design variables (for example utility)
• Values xi are calculated from one Monte-Carlo simulation, same values are used to find failure probabilities of all design alternatives
2
1
2)(/
ˆ ˆ)(
)/(
)1(
1sys
n
i iS
iEp pn
f
f
nns
f
sys
x
μx
X
XXX
syspn
sys stp ˆ2
1,1ˆ
)(
)/(1)(ˆ
)(/
1 iS
in
isys
f
f
np
Ef
x
μxμ
X
XX XX
Sampling PDF
PDF when mean values of design variables = µX
Estimation of failure probability
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Sensitivity analysis
)(
1)|(1
1
)(|
iS
n
i X
Xi
jX
sys
f
f
n
p f
j
E
x
μx
X
XX
• Analytical expression• Can be calculated very efficiently because it is easy to
differentiate PDF of a random variable
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RBDO with Probabilistic Re-analysis
• Find X
• To minimize
• s. t.
• Solution requires only n deterministic analyses
)( Xμl
allf
iS
in
isys p
f
f
np
f
)(
)/(1)( /
1 x
μxμ
X
EXX XX
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RBDO with Probabilistic Re-analysis
Iso-cost curves
Feasible Region
Increased Performance
x2
x1
Optimum
Failure subset
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Efficient Probabilistic Re-analysis:Capabilities
• Calculates system failure probabilities of many design alternatives using results of a single Monte-Carlo simulation
• Does not require calculation of the performance function of modified designs – reuses calculated values of performance function from a single simulation. Cost of RBDO cost of a single simulation
• Non intrusive, easy to program• If PDF of design variables is continuous then system
failure probability varies smoothly as function of design variables
• Highly effective when design variables have large variability
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Challenges
• Works only when all design variables are random
• Requires sample that fills the space of design variables
• Cost of single simulation increases with design variables
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3. Example: RBDO of Internal Combustion Engine
• Preliminary design of flat head internal combustion engine from thermodynamic point of view
• Find average bore, inner and outer diameters, compression ratio and RPM
• To maximize specific power • S. t. system failure probability ≤pf
all (0.4% to 0.67%)
• Failure: any violation of nine packaging and functional requirements
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Design variables(all variables normal)
Standard
Deviation Lower Bound
Upper Bound
Cylinder bore, b , mm
0.4 81.7 82.5
Intake valve diameter, Id , mm
0.15 35.7 36
Exhaust valve
diameter, Ed , mm
0.15 30.2 30.5
Compression ratio,
rc
0.05 9.3 9.39
RPM at peak power/1000,
0.25 5.15 5.65
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Sampling PDF
Average Values
Bore 82.13
Intake valve diameter
35.84
Exhaust valve diameter
30.33
Compression ratio
9.34
RPM 5.31
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Effect of average bore on system failure probability (100,000 replications)
81.6 81.8 82 82.2 82.4 82.60.1
0.05
0
0.05
0.1
dPFb b Ecr EdI EdE E
b
.
81.9 81.95 82 82.05 82.1 82.15 82.2 82.250.004
0.006
0.008
0.01
0.012
Monte-CarloEfficient Reanalysis95% Upper Bound95% Lowel Bound
Bore
Fai
lure
Pro
babi
lity
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RBDO Results for the Engine Design Example (maximum failure probability:
0.0067) Design Variables Initial
Design 1
Initial Design
2
Optimum
Bore 82.023 82.5 82.151 Intake valve diameter 35.7 36 35.857
Exhaust valve diameter 30.2 30.5 30.37 Compression ratio 9.3 9.39 9.315
RPM 5.289 5.65 5.373 psys (PRA)
psys(Monte Carlo, one million replications)
0.0067 0.0071
Objective function l (KW/liter) 51.162
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Specific Power and Probability of Failure
3.E-03
4.E-03
5.E-03
6.E-03
7.E-03
8.E-03
50.7 50.8 50.9 51 51.1 51.2
Specific Power
Fa
ilure
Pro
ba
bili
ty
PRA
Monte Carlo
Liang et al.
95% Upper ConfidenceBound PRA95% Lower ConfidenceBound
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Comparison of efficiencies of standard Monte Carlo and PRA (narrower CI means
higher efficiency of the method)
0.E+00
1.E-04
2.E-04
3.E-04
4.E-04
5.E-04
6.E-04
7.E-04
8.E-04
9.E-04
4.45E-03 5.45E-03 6.42E-03 6.92E-03 7.09E-03
Failure Probability
Hal
f-W
idth
95%
CI
MC
PRA
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Observations
• PRA found an optimum design almost identical as RBDO using FORM (Liang 2007).
• PRA converged to same optimum from different initial designs
• PRA underestimated consistently system failure probability by 5% to 11%.
• 95% confidence intervals have half width = 23% to 28% of system failure probability
• Confidence interval from PRA is 50% wider than that of standard Monte Carlo. This means that PRA needs 225,000 replications to yield results with same accuracy as standard Monte Carlo with 100,000 replications.
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4. Conclusion
• Presented efficient methodology for RBDO using Monte Carlo simulation
• Solves RBDO problems using a single Monte Carlo simulation
• Calculates sensitivity derivatives of system failure probability
• Limitation: methodology, in its present form, works only when all design variables are random