Multidisciplinary analysis and optimization under uncertainty Chen Liang Doctoral Dissertation Defense 1 Multidisciplinary Analysis and Optimization under Uncertainty Chen Liang Dissertation Defense Adviser: Sankaran Mahadevan Department of Civil and Environmental Engineering Vanderbilt University, Nashville, TN Aug. 21 st , 2015
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Multidisciplinary analysis and optimization under uncertainty
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Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense1
Multidisciplinary Analysis and Optimization
under Uncertainty
Chen Liang
Dissertation Defense
Adviser: Sankaran Mahadevan
Department of Civil and Environmental Engineering
Vanderbilt University, Nashville, TN
Aug. 21st , 2015
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense2
MDA Overview
Three-objective two-stage-to-orbit launch vehicle
Heatsink
Aircraft wing analysis
Nodal Pressures
Nodal
Displacements
Wing Backsweep Angle,
Speed and Angle of Attack
Lift, drag, stress
FEA
structure
CFD
fluid
Compatibility Fixed-point-iteration
(FPI)
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense3
MDO under uncertainty
Presence of uncertainty sources UQ
Sampling outside FPI SOFPI Repeated MDA
New design input values at each iteration
Computationally unaffordable Need efficient methods for MDA and
MDO under uncertainty
UQ / Reliability Analysis
FEA CFD
MDA
Optimization
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense4
Three types of sources
• Physical variability
• Data uncertainty
(e.g., sparse/interval data)
• Model Uncertainty
Forward problem
• For a given input Uncertainty of output needs to
be evaluated
• Propagation of aleatory uncertainty is well-studied
• Inclusion of epistemic uncertainty becomes
more important
• Little work regarding the propagation of epistemic
uncertainty in feedback coupled MDA
Uncertainty and errors in optimization
Sources of Uncertainty and Errors
Aleatory (Irreducible)
Natural Variability
Epistemic (Reducible)
Data Uncertainty
Sparse Data
Interval Data
Model Uncertainty
Numerical error
Discretization error
Round off error
Truncation error
Surrogate model error
UQ error
Model Form Error
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense5
Overall Research Goal
Efficient UQ techniques for feedback coupled MDA
and MDO
Combine information with both aleatory and
epistemic sources of uncertainty
Particular emphasis on
• Representation of epistemic sources of uncertainty
• Propagation through feedback coupled analysis
• Inclusion in the design optimization of multidisciplinary
analysis with feedback coupling (high-dimensional)
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense6
Research objectives
MDA with epistemic uncertainty
- Inclusion of data uncertainty and model error
MDA with high-dimensional coupling
- Large number of coupling variables
- Dependence among all variables
- Efficient uncertainty propagation
Multi-objective optimization under uncertainty
- Reliability-based design optimization
- Solution enumeration (Pareto front construction)
Multidisciplinary design optimization
- Concurrent interdisciplinary compatibility enforcement and
objective/constraint functions evaluation
A. Multidisciplinary analysis under uncertainty
B. Multidisciplinary optimization under uncertainty
Bayesian
Framework
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense7
𝑔2
𝑓
𝑢12
𝑔1
𝑢21
Analysis 1
𝑨𝟏(𝒙, 𝑢21)
Analysis 2
𝑨𝟐(𝒙, 𝑢12)
Analysis 3
𝑨𝟑(𝑔1, 𝑔2)
𝑥1𝑥𝑠 𝑥2
Uncertainty propagation under
the compatibility condition
No need for full convergence
analysis
Multi-disciplinary multi-level system
Review of MDA under uncertainty methods
Sankararaman & Mahadevan,
J. Mechanical Design, 2012
Approximation Method
• First-order Second Moment
(FOSM) approximations• Linear approximations of disciplinary
analyses
• PDF based on mean & variance
• Du & Chen, Mahadevan & Smith
• Fully Decoupled Approach• Calculate PDFs of u12 & u21
• Cut-off feedback both directions
• Ignores dependence between u12 &
u21
• Lack of one-to—one correspondence
between 𝑔1 and 𝑔2 in calculating f
Likelihood-based approach for MDA
(LAMDA) 𝑢21 𝑢12
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense8
𝑢12
𝑔2
𝑓
𝑢12
𝑔1
𝑢21
Analysis 1
𝑨𝟏(𝒙, 𝑢21)
Analysis 2
𝑨𝟐(𝒙, 𝑢12)
Analysis 3
𝑨𝟑(𝑔1, 𝑔2)
𝑥1𝑥𝑠 𝑥2
Multi-disciplinary multi-level system
Likelihood-based approach for MDA
(LAMDA)
Objective 1: MDA with epistemic uncertainty
𝑔2
𝑓
𝑔1
Analysis 1
𝑨𝟏(𝒙, 𝑢21)
Analysis 2
𝑨𝟐(𝒙, 𝑢12)
Analysis 3
𝑨𝟑(𝑔1, 𝑔2)
𝑥1𝑥𝑠𝑥2
𝑢21
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense9
𝑈12
𝐹𝑈12
𝐺Interdisciplinary compatibility:
LAMDA
𝑢12 Analysis 1
𝑨𝟏(𝒙, 𝑢21)
Analysis 2
𝑨𝟐(𝒙, 𝑢12)
𝑢21 𝑈12
Given a value of 𝑢12 what is 𝑃(𝑈12 = 𝑢12|𝑢12) 𝐿(𝑢12)
𝑓 𝑢12 =𝐿(𝑢12)
𝐿(𝑢12)𝑑𝑢12
FORM is used to calculate the CDFs of
the upper and lower bounds: 𝑃𝑢 and 𝑃𝑙
𝐿 𝑢12 ∝ 𝑢12−
𝜀2
𝑢12+𝜀2𝑓𝑈12
𝑈12 𝑢12 𝑑𝑈12
𝐿 𝑢12 ∝ (𝑃𝑢 − 𝑃𝑙) finite difference
𝑷𝒖
𝑷𝒍
𝒖𝟏𝟐 +𝜺
𝟐𝒖𝟏𝟐 −
𝜺
𝟐
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense10
Sources of Uncertainty and Errors
Aleatory (Irreducible)
Natural Variability
Epistemic (Reducible)
Data Uncertainty
Sparse Data
Interval Data
Model Uncertainty
Numerical error
Discretization error
Round off error
Truncation error
Surrogate model error
UQ error
Model Form Error
Uncertainty and errors in LAMDA
Considering epistemic
uncertainty sources
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense11
Data uncertainty (sparse and interval data)
pi pQ
X
fX(x)
θQθ3θ2θ1θi
p1
p2
n
i
b
a
X
m
i
iX dxPxfPxfPLi
i11
)|()|()(
Parametric approach Non-parametric approach
Likelihood
Sparse data Interval data
Convert sparse and interval data into a useable distribution
(for propagation)
Sankararaman &
Mahadevan RESS 2011
Zaman, et. al,
RESS 2011
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense12
Sources of Uncertainty and Errors
Aleatory (Irreducible)
Natural Variability
Epistemic (Reducible)
Data Uncertainty
Sparse Data
Interval Data
Model Uncertainty
Numerical error
Discretization error
Round off error
Truncation error
Surrogate model error
UQ error
Model Form Error
Uncertainty and errors in LAMDA
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense13
Model uncertainty estimation
Training
points
(e.g. FEA)
Prediction
Uncertainty
Discretization error estimation
• GP prediction ~ 𝑁(𝜇, 𝜎) 𝜇 and 𝜎 are input dependent
Rangavajhala, et. al,
AIAA Journal 2010
Richardson
extrapolation
At each input 𝒙
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense14
Auxiliary variable 𝑃ℎ~𝑈[0,1] CDF of GP output
Stochastic model output input random variable
FORM can be used for likelihood evaluation
𝑈12
CDF of GP output
𝐿 𝑢12 ∝ 𝑃(𝑈12 = 𝑢12|𝑢12)
• Equation only calculable when 𝑈12 is deterministic given an 𝒙 and 𝑢12
Inclusion of model uncertainty in LAMDA
𝑢12 𝑢21𝐴2 𝒙, 𝑢12
GP model
(𝒙, 𝑢21)
𝒙 𝑃ℎ
Deterministic
𝑃ℎ
Extra loop of uncertainty propagation
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense15
Electrical
Parameters
Component Heat
Total Power
Dissipation
Heatsink
Temperature
Electrical
Analysis
Thermal
Analysis
Power Density: Total Power DissipationVolume of the Heatsink
Heatsink Size
Parameters
Numerical example: electronic packaging
Model error in thermal Analysis
• 2D steady state heat transfer equation (PDE)
𝛻𝑇𝑠𝑖𝑛𝑘 𝑥, 𝑦 +𝑞(𝑥, 𝑦)
𝑘= 0
• Solved by Finite Difference method
• Limited computational resources
discretization error
MDO test suite: Heatsink
Data uncertainty
Temperature Coefficient of resistance (𝜶)
Data points Data intervals
0.0055
0.0057
[0.004,0.009]
[0.0043,0.0085]
[0.0045, 0.0088]4 5 6 7 8
x 10-3
0
200
400
600
800
1000
1200 Non-parametric PDF of 𝜶
PDF
𝜶
• Uncertainty estimated auxiliary
variable
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense16
Temperature
Thermal
Analysis
Electrical
Analysis
Power density
𝒙𝟐𝒙𝟏
Heat
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense17
Results
Uncertainty of the coupling variables
Comparison between FPI and LAMDA
FPI with stochastic model errors is difficult
to converge
Only a few FPI realizations is affordable
LAMDA agrees well with the available data
Liang & Mahadevan
ASME JMD, 2015
Temerature(℃ )
PD
F
Component heat(Joule)
PD
F
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense18
Likelihood Approach for Multidisciplinary Analysis
• Data uncertainty and model error in feedback coupled
analysis.
• Auxiliary variable stochastic model error.
Features of methodology
• Likelihood-based approach for MDA (LAMDA)
• FORM
• GP estimation of model error
• Auxiliary variable
Objective-1 summary
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense19
High Dimensional Coupling
CFD FEA
UQ/Reliability Analysis
Nodal Pressures
Nodal Displacements
Multiple coupling variables in one direction
Joint distribution of the coupling variables in the same direction
• Solved by Finite Difference method• Limited computational resources
discretization error
At each realization of input 𝒙:
Training
pointsGP
Prediction
Auxiliary variable 𝑃ℎ representation of the stochastic model output
Sample 𝑃ℎ from 𝑈(0,1) inverse CDF deterministic output
• GP prediction ~ 𝑁(𝜇, 𝜎)
𝜇 and 𝜎 are input dependent
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense52
Representation of Model Error in BN
Design
variables
Uncertainty
sources
Coupling
variables
System
output
Compatibility
condition
(including 𝑷𝒉)
𝑥1_𝑠𝑡 𝑥2_𝑠𝑡 𝑥3_𝑠𝑡 𝑥4_𝑠𝑡 𝑥5 𝑥6 𝑃ℎ
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense53
Results
Design
Variables
RBDO using
FPI
Design
Variables
𝑥1 0.077
𝑥2 0.149
𝑥3 0.021
𝑥4 0.010
Objective 𝝁𝑾𝑫 11170
Constraint Joint Probability 0.99
• Cannot implement SOFPI since the FPI is hard to converge with stochastic
model output.
Iteration
𝝁𝑾𝑫
• Optimizer: Genetic algorithm
• 100 populations, 15 iterations.
• Build BN with 120 samples (240 function evaluations).
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense54
𝑵𝑷𝟑 𝑵𝑷𝟒
Example-2 Aeroelastic wing design
Design variables:
• Backsweep angle 𝜃: [0 , 0.5]
• Input variability: 𝜎𝜃 ~ 𝑁(0, 0.03)
• FPI takes 10 iterations to converge
Coupling variables: nodal pressure
• 𝑁𝑃3: nodal pressure after 3rd iteration
• 𝑁𝑃4: nodal pressure after 4th iteration
𝑵𝑷𝟑 after PCA 𝑵𝑷𝟒 after PCA
Difference = 0
I/O
BN with 30 principal components
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense55
Optimization problem and solution
Optimal Value
Design
variable𝝁𝒃𝒘 0.405
Objective 𝝁𝒍𝒊𝒇𝒕 1707.5
Constraint 𝑃(𝑠𝑡𝑟𝑒𝑠𝑠) 0.998
Optimizer: DIRECT
67 calls of BN
547 seconds
max𝝁𝒃𝒘
𝐸 𝐿
s.t
𝑃 𝑆 ≥ 3 ∗ 105𝑃𝑎 ≤ 10−3
0.05 ≤ 𝜇𝑏𝑤 ≤ 0.45
Optimization formulation
Optimal solution Optimization history
0 5 101695
1700
1705
1710
No. of iterations
Lift
BN is trained with samples without full convergence analysis
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense56
Objective-4 summary
BNC-MDO under uncertainty
• Efficiently integrates of MDA and optimization under
uncertainty
• Simultaneously enforces the interdisciplinary
compatibility and evaluates objectives and constraints
Features of methodology
• BNC
• PCA
• Optimization algorithm (DIRECT/GA)
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense57
Future Work
(1)Scalability of the proposed BNC-MDA approach needs to be
investigated by solving larger problems.
(2)Extension in multi-level analyses, and multi-disciplinary feedback
coupled analyses (for more than two disciplines).
(3)Extension to robustness-based design optimization under both aleatory
and epistemic uncertainty.
(4)Analytical multi-normal integration of the Gaussian copula instead of
the sampling-based strategy for reliability assessment.
(5) Improve the efficiency for non-Gaussian copulas.
(6)Extension to time-dependent problems.
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense58
List of journal manuscripts
1. Liang, C. and Mahadevan, S., Stochastic Multi-Disciplinary Analysis under Epistemic Uncertainty, Journal of Mechanical Design, Vol. 137, Issue 2, 2015.
2. Liang, C. and Mahadevan, S., Bayesian Sensitivity Analysis and Uncertainty Integration for Robust Optimization, Journal of Aerospace Information Systems, Vol 12, Issue 1, 2015.
3. Rangavajhala, S., Liang, C., Mahadevan, S. and Hombal, V., Concurrent optimization of mesh refinement and design parameters in multidisciplinary design, Journal of Aircraft, Vol. 49, No. 6, 2012.
4. Liang, C. and Mahadevan, S., Stochastic Multidisciplinary Analysis with High-Dimensional Coupling, AIAA Journal, under review.
5. Liang, C. and Mahadevan, S., Pareto Surface Construction for Multi-objective Optimization under Uncertainty, ready for submission.
6. Liang, C. and Mahadevan, S., Probabilistic Graphical Modeling for Multidisciplinary Optimization, ready for submission.
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense59
List of conference proceedings
1. Liang, C. and Mahadevan, S., Reliability-based Multi-objective Optimization under Uncertainty,
16th AIAA/ISSMO Multidisciplinary Analysis and Optmization Conference, Dallas, Texas, 2015.
2. Liang, C. and Mahadevan, S., Bayesian Framework for Multidisciplinary Uncertainty
Quantification and Optimization, 16th AIAA Non-Deterministic Approaches Conference, National
Harbor, Maryland, 2014.
3. Liang, C. and Mahadevan, S., Multidisciplinary Analysis and Optimization under Uncertainty,
11th International Conference on Structural Safety and Reliability, New York, New York, 2013.
4. Liang, C. and Mahadevan, S., Multidisciplinary Analysis under Uncertainty, 10th World
Congress of Structural and Multidisciplinary Optimization, Orlando, Florida, 2013.
5. Liang, C. and Mahadevan, S., Design Optimization under Aleatory and Epistemic Uncertainty,
10th World Congress of Structural and Multidisciplinary Optimization, Orlando, Florida, 2013.
6. Liang, C. and Mahadevan, S., Inclusion of Data Uncertainty and Model Error in Multi-
disciplinary Analysis and Optimization, 54th Structures, Structural Dynamics, and Materials
Conference, Boston, Massachusetts, 2013.
7. Liang, C. and Mahadevan, S., Design Optimization under Aleatory and Epistemic Uncertainties,
14th Multidisciplinary Analysis and Optimization Conference, Indianapolis, Indiana, 2012.
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense60
Acknowledgement
Committee members:
Dr. Prodyot Basu (CEE)
Dr. Mark N. Ellingham (Math)
Dr. Mark P. McDonald (Lipscomb)
Dr. Dimitri Mavris (GT)
Dr. Roger M. Cooke (TU Delft)
University of Melbourne:
Dr. Anca Hanea
Dan Ababei
Vanderbilt University:Dr. Sirisha Rangvajhala
Dr. Shankar Sankararaman
Dr. You Ling
Dr. Vadiraj Hombal
Adviser:
Dr. Sankaran Mahadevan
Ghina Nakad Absi, Dr. Bethany Burkhart, Beverly Piatt
Defense preparation:
Great friends at Vanderbilt University !
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense61
Acknowledgement
Funding support:
(1) NASA Langley National Laboratory
(2) Sandia National Laboratory
(3) Vanderbilt University, Department of Civil and Environmental
Engineering
Software Licenses:
(1) UNINET by LightTwist Inc. (Dan Ababei)
(2) VisualDOC by Vanderplaats R&D Inc. (Garret Vanderplaats,
Juan-Pablo Leiva)
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense62
Multidisciplinary analysis and optimization
under uncertainty
Chen Liang
Doctoral Dissertation Defense63
Fit a Gaussian process model to 𝒇 with 𝑥𝑇 and 𝑦𝑇 as inputs, and
predict the function value at desired points 𝑥𝑃:
GP Model the underlying covariance in the data instead of the functional