Design Optimization of Flow Path with ANSYS-Workbench and ...
Introduction to parametric optimization and robustness … · 2015-12-14 · optimization....
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1 Introduction to parametric optimization and robustness evaluation with optiSLang Dynardo GmbH
Transcript of Introduction to parametric optimization and robustness … · 2015-12-14 · optimization....
1
Introduction to parametric
optimization and robustness
evaluation with optiSLang
Dynardo GmbH
2Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
1. Introduction to optiSLang
2. Process integration
3. Sensitivity analysis
5. Robustness analysis
6. Further training
4. Parametric Optimization
3Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
1. Introduction to optiSLang
2. Process integration
3. Sensitivity analysis
5. Robustness analysis
6. Further training
4. Parametric Optimization
4Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Excellence of optiSLang
• optiSLang is an algorithmic toolbox for
• sensitivity analysis,
• optimization,
• robustness evaluation,
• reliability analysis
• robust design optimization (RDO)
• functionality of stochastic analysis to
run real world industrial applications
• advantages:
• predefined workflows,
• algorithmic wizards and
• robust default settings
5Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Robust Design Optimization (RDO)
in virtual product development
optiSLang enables you to:
• Identify optimization potentials
• Improve product performance
• Secure resource efficiency
• Adjust safety margins without limitation of input parameters
• Quantify risks
• Save time to market
6Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Methods for Robust Design Optimization (RDO) with
optiSLang
7Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
1. Introduction to optiSLang
2. Process integration
3. Sensitivity analysis
5. Robustness analysis
6. Further training
4. Parametric Optimization
8Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Process Integration
Parametric model as base for
• User defined optimization (design) space
• Naturally given robustness (random) space
Design variablesEntities that define the design space
Response variablesOutputs from the system
The CAE processGenerates the results according to the inputs
Scattering variablesEntities that define the robustness space
9Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Start
CAE process (FEM, CFD, Excel, Matlab, etc.)
Robust Design Optimization
Optimization Robust Design
10Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
optiSLang Integrations
Direct integrations Matlab Excel Python SimulationX Ansys Workbench
Supported connections Ansys Abaqus Adams …
Arbitrary connection ofASCII file based solvers
11Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Full Integration of optiSLang in Ansys Workbench
• optiSLang modules Sensitivity, Optimization and
Robustness are directly available in ANSYS Workbench
12Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
• Process integration: Ansys classic (APDL) and Ansys Workbench
• Optimization task: How to change a tuning fork so that
• Eigen-modes 1, 2 and 3 are 440 Hz, 880 Hz and 1230 Hz each
• Mass is max. 80 g
Optimization of a tuning fork with optiSLang
Final Design
13Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Rod_Length (40-60 mm)
Radius (7-10 mm)
Grip_Width (4-5 mm)
Depth (5-10 mm)
Optimization of a tuning fork with optiSLang
Design parameters (here: at DesignModeler)
Rod_Width (5-10 mm)
Grip_Length (20-30 mm)
14Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Process Integration with optiSLang: tuning fork
Initial Design
Final Design
15Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
1. Introduction to optiSLang
2. Process integration
3. Sensitivity analysis
5. Robustness analysis
6. Further training
4. Parametric Optimization
16Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Flowchart of optiSLang Sensitivity Analysis
• Full design variable space X for sensitivity analysis
• Scanning the design space with DoE by direct solver calls
• Generating MOP on DoE samples
• Sensitivity analysis gives reduced design variable space Xred
• MOP may be used as approximation model for optimization
• Best design from DoE as start point may accelerate local optimization
DoE
Solver
Sensitivity analysis
MOP
17Introduction to the parametric optimization and robustness evaluation with
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© Dynardo GmbH
Scanning the Design Space
Inputs Design of Experiments Solver evaluation Outputs
• Distributions of inputs are represented by Latin Hypercube Sampling
• Minimum number of samples should represent statistical properties, cover the input space optimally and avoid clustering
• For each design all responses are calculated
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optiSLang
© Dynardo GmbH
Metamodel of Optimal Prognosis (MOP)
• Approximation of solver output by fast surrogate model
• Reduction of input space to get best compromise between available
information (samples) and model representation (number of inputs)
• Advanced filter technology to obtain candidates of optimal subspace
• Determination of optimal approximation model (polynomials, MLS, …)
• Assessment of approximation quality (Coefficient of Prognosis, CoP)
MOP algorithm solves 3 important tasks:
• Best variable subspace
• Best meta-model
• Estimation of prediction quality
19Introduction to the parametric optimization and robustness evaluation with
optiSLang
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• Optimization task: Frequency 1 = 440 Hzobjectives: Frequency 2 = 880 Hz
Frequency 3 = 1320 Hz
• Constraints: mass < 80 g
Sensitivity Analysis with optiSLang: tuning fork
Initial Design
Final Design
20Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
1. Introduction to optiSLang
2. Process integration
3. Sensitivity analysis
5. Robustness analysis
6. Further training
4. Parametric Optimization
21Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Optimization with MOP pre-search
• Full optimization is performed on MOP by approximating the solver response
• Optimal design on MOP can be used as
– final design (verification with solver is required!)
– as start value for second optimization step with direct solver
• Good approximation quality of MOP is necessary for objective and constraints (CoP ≥ 90%)
DOE
Solver
Optimizer• Gradient• ARSM• EA/GA
Sensitivity analysis
Optimization
Solver
MOP
SolverMOP
Optimizer• Gradient• ARSM• EA/GA
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optiSLang
© Dynardo GmbH© Dynardo GmbH
optiSLang Optimization Algorithms
Gradient-based Methods
• Most efficient method if gradients are accurate enough
• Consider its restrictions like local optima, only continuous variablesand noise
Adaptive Response Surface Method
• Attractive method for a small set of continuous variables (<20)
• Adaptive RSM with default settings is the method of choice
Nature inspired Optimization
• GA/EA/PSO imitate mechanisms of nature to improve individuals
• Method of choice if gradient or ARSM fails
• Very robust against numerical noise, non-linearity, number of variables,…
Start
23Introduction to the parametric optimization and robustness evaluation with
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© Dynardo GmbH
Decision Tree for Optimizer Selection
• An optimizer is automatically suggested depending on the parameter
properties, the defined criteria as well as user specified settings
• Preoptimized reference without failed
or noisy solver responses -> NLPQL
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• Optimization task: Frequency 1 = 440 Hzobjectives: Frequency 2 = 880 Hz
Frequency 3 = 1320 Hz
• Constraints: mass < 80 g
Optimization with optiSLang: tuning fork
Initial Design
Final Design
25Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Initial vs. Optimal Design
Target Design Initial Design Optimal Design
Mode 1 [Hz] 440 323 440
Mode 2 [Hz] 880 602 880
Mode 3 [Hz] 1320 1096 1320
Mass [g] < 80 89 54
Initial Design Optimal Design
26Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
1. Introduction to optiSLang
2. Process integration
3. Sensitivity analysis
5. Robustness analysis
6. Further training
4. Parametric Optimization
27Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Optimization + Robustness evaluation
• Full optimization variable space X for sensitivity analysis
• Sensitivity analysis gives reduced optimization variable space Xred
• Optimizer determines optimal design xopt by direct solver calls
• Robustness evaluation (varianced-based or reliability-based) in the random variable space Xrob at optimal design xopt
DOE
Solver
Optimizer• Gradient• ARSM• EA/GA
Sensitivity analysis
Optimization
Solver
MOP
Robustness• Variance• Sigma-level• Reliability
Robustness
Solver
28Introduction to the parametric optimization and robustness evaluation with
optiSLang
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Robustness Analysis
1) Define the robustness space using scatter range, distribution and correlation
2) Scan the robustness space by producing and evaluating ndesigns
3) Check the variation 4) Check the
explainability of the model
5) Identify the most important scattering variables
29Introduction to the parametric optimization and robustness evaluation with
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© Dynardo GmbH
Robustness Analysis with optiSLang: tuning fork
Initial Design
Final Design
30Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Workflow in optiSLang: tuning fork
Initial Design
Final Design
31Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Optimization and Robustness evaluation withoptiSLang inside Ansys Workbench
32Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
1. Introduction to optiSLang
2. Process integration
3. Sensitivity analysis
5. Robustness analysis
6. Further training
4. Parametric Optimization
33Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH© Dynardo GmbH
Further Training
optiSLang 4 Basics 3 day introduction to process integration, sensitivity,
optimization, calibration and robustness analysis
optiSLang inside ANSYS Workbench 2 day introduction seminar to
parameterization in ANSYS Workbench, sensitivity analysis and
optimization
optiSLang 4 and ANSYS Workbench 1 day introduction to the integration
of ANSYS Workbench projects in a optiSLang 4 solver chain,
parameterization of signals via APDL output
Parameter Identification 1 day seminar on basics of model calibration,
application of sensitivity analysis and optimization to calibration problems
Robust Design and Reliability Analysis 1 day seminar on basics of
probability, robustness and reliability analysis, robust design optimization
See our website: http://www.dynardo.de/en/trainings.html
34Introduction to the parametric optimization and robustness evaluation with
optiSLang
© Dynardo GmbH
Thanks for your attention!
