DECEMBER 2019 Process Integration and Design Optimization

• DECEMBER 2019

Johannes Will

Senior director optiSLang product line

Process Integration and Design Optimization (PIDO) - the glue and the driver of virtual prototyping

Challenges in Virtual Prototyping

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• Virtual prototyping is key for cost efficiency • Test cycles are reduced and placed late in the

product development• CAE-based optimization and CAE-based robustness

evaluation becomes key technology in virtual prototyping

• combining ANSYS leading edge technology of parametric modelling in multiphysics

• with optiSLang leading edge technology for simulation workflow building and design exploration

enables customer to perform cost efficient virtual prototyping

Source: ANSYS Inc.

PIDO -Process Integration and Design Optimization

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Process Integration for simulation workflow building• optiSLang integrations provides flexibility for process chaining• Nested loops, parallel paths and conditional execution are possible• orchestration of simultaneous runs, HPC and cloud usage• Publish as Web-App to pave the road for democratization

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optiSLang - software neutral integration platform

Adams

TurboOpt

• Process automation: combining a parametric Pre-Processing tool (TurboOpt) with productivity check (VaneAdviser) and CFD simulation (ANSYS)

• Workflows: run a Sensitivity, create an Metamodel and use for Optimization

Process Integration, Automation & Workflow Generation

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ANSYS optiSLang Power of variation analysis

• Fit/calibrate simulation and measurement

• Understand your design via optiSLang sensitivity module

- Which parameters influence what?

- Which constraints and goal conflicts I need to address?

- Can I calibrate to measurements

• Powerful metamodeling module

• Powerful Robustness/Reliability module

• Enables customer to address Robust Design Optimization (RDO), Uncertainty Quantification (UQ), Design for Six Sigma (DfSS)

Design UnderstandingInvestigate parameter sensitivities,

reduce complexity and generate best possible metamodels

Design UnderstandingInvestigate parameter sensitivities,

reduce complexity and generate best possible metamodels

Model CalibrationIdentify important model parameter for the best fit between simulation

and measurement

Model CalibrationIdentify important model

parameter for the best fit between simulation and measurement

Design ImprovementOptimize design performance

Design QualityEnsure design robustness and

reliability

Design QualityEnsure design robustness and

reliability

CAE-Data

MeasurementData

Robust Design

Design ImprovementOptimize design performance

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Sensitivity AnalysisUnderstand your possibilities:

• Take a deep look at the space of opportunities

• Learn which design parameter is important and how to define the goals and the limitations to find the right way

1 Latin Hypercube Sampling

2 Metamodel ofOptimal Prognosis

3 Parameter importance

Automatic workflow with a minimum of solver runs to:Identify the important parameters for each response

Generate best possible metamodel (MOP) for each responseUnderstand and reduce the optimization task

Check solver and extraction noise

Sensitivity AnalysisUnderstand the most important input variables

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Design Improvement• How to define your objective and constraints?

• Use the MOP from Sensitivity to compare different optimization strategies in minutes (no additional simulation run)

• One goal or maybe multi disciplinary optimization?

1 Sensitivity analysisfor pre-optimization

2 Optimization using MOP

3 Direct optimizationwithalgorithms

Work with the reduced subset of only important parameters Pre-optimization on meta model (one additional solver run)

Optimization with leading edge optimization algorithms Decision tree for optimization algorithms

OptimizationOptimize your product design

Design Exploration easy and safe to be used

Engineers and Designers should not have to choose detailed settings and algorithmic choicesoptiSLang’s functionality is compressed to three wizards (sensitivity, optimization, robustness) with minimal user input

Simply drag and drop to add optiSLang, use the wizards and follow the suggestions, and push to solve

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Design Optimization of a Radial Compressor Impeller

Problem Description• Investigate optimization potential• Input: 49 Geometry Parameter to optimize

Result/Benefit• With sensitivity analysis the most important parameter

with successful ranges could be identified• With ARSM and Evolutionary Optimization algorithms

the mechanical objective was significantly improved • Mass of the impeller was reduced by 45%• Aerodynamic performance near surge line was

improved.

Wanzek T.: Design Optimization of a radial compressor impeller; Proceedings WOST 2015, Weimar, Germany, www.dynardo.de

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Design Quality/Uncertainty Quantification

Definition of design and stochastic variables

Sensitivity analysis

Design failure

Update constraints

Deterministic optimization

Variance-based

robustness evaluation

Optimal and robust design

1 Latin Hypercube Sampling

2 Output parametervariation

3 Parameter Importance

Powerful procedure to check design quality:Robustness evaluation with optimized Latin Hypercube Sampling

Proof of Reliability with leading edge algorithmsCheck variation interval limits and probabilities of overstepping

Identify the most important scattering variables Decision tree for robustness algorithms

Robustness EvaluationEnsure your product quality

• Quantify Robustness and Reliability

• Optimize safety distances/margins to target for robust/reliable and cost effective designs at the same time

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Robust Design OptimizationExample: Robust Design Optimization of a centrifugal compressor performance

Parameterization

Parametric geometry definition using ANSYS BladeModeler(17 geometric parameter)

Model completion and meshing using ANSYS Workbench

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Fluid Structure Interaction (FSI) coupling

Parametric fluid simulation setup using ANSYS CFX

Parametric mechanical setup using ANSYS Workbench

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Optimization goal: increase efficiencyConstraints: 2 pressure ratio’s, 66 frequency constraints, Robustness

Tolerance limit1.34<ΠT<1.36~13% outside

Input Parameter 21Output Parameter 43Constraints 68

Initial SA ARSM I EA I ARSM II ARSM III

Total Pressure Ratio 1.3456 1.3497 1.3479 1.3485 1.356 1.351

Efficiency [%] 86.72 89.15 90.62 90.67 90.76 90.73

#Designs - 100 105 84 62 40

Customer Success

Statements of benefit• Part of many processes – standard tool for optimization and robust design• Many success stories like:

- M. Brück – “faster than competitors – starting in procurement phase”- A. Fuchs – “Optimized supplier management – reduce cost of existing product”- D. Krätschmer – “33-50% time savings in complete development process”

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Key Technology: MOP – Metamodel of Optimal Prognosis

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Key Technology: MOP – Metamodel of Optimal PrognosisValue of the workflow to the customer:

• Automatic workflow

• Automatic identification of relevant parameter subspace

• Automatic identification of optimal approximation model

• Do not allow overfitting! Objective measure of prognosis quality = CoP (global and local approximation)

• Use Case 1: Evaluation of variable sensitivities

• Use Case 2: run optimization/robustness on metamodels

• Use Case 3: use MOP as data-based ROM

• optiSLang is a programmable environment: Include your own algos, include industry standard AI (machine learning algorithms)

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Example scalar MOP: Unconventional Gas ProductionForecast of Oil Production of Wells based on historical data

• 16 input variables, partly highly correlated• 130 measurement data points for production were available

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Example scalar MOP: Unconventional Gas ProductionForecast of Oil Production of Wells based on historical dataStep 2: build best possible metamodels (MOP)

• Automatic approach detects four inputs with minimal input correlations and maximum prognosis quality of oil production variation

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Next Generation Metamodelling: Machine Learning

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optiSLang‘s MOP – a machine learning framework

Feature extraction

Classification

Testing

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optiSLang‘s metamodels: MOP – Metamodel of Optimal PrognosisIntegration of Keras & Tensorflow Libraries

Implementation in custom surrogate interface of optiSLang: Automatic configuration of neurons and layers Cross validation to estimate Coefficient of Prognosis Available as external python environment Neural networks are treated as one of a library of approximation models Competition is done in the MOP framework based on the CoP

MOP machine lerning framework

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optiSLang‘s metamodels: MOP – Metamodel of Optimal PrognosisTurbine Data

• Mass, stiffness, pressure loss and life time are given with respect to geometry parameters• 13 parameters are considered, 176 data sets were available

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optiSLang‘s metamodels: MOP – Metamodel of Optimal PrognosisTurbine Data – Rotational Stiffness

• Anisotropic kriging (Gaussian process) is optimal approximation model type

Kriging6 parameters

CoP = 99%

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13 parameters (full space)3 layer/ 7 neurons

CoD = 98 %CoP = 79 %

Significant larger error in validation data indicates overfitting

Deep Learning only

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6 parameters (optimal subspace)3 layer/ 7 neurons

CoD = 99 %CoP = 98 %

Reduction of parameter number significantly improves accuracy

Deep Learning within machine learning framework

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optiSLang‘s metamodels: MOP – Metamodel of Optimal PrognosisApproximate Load Bearing Capacity• 6000 design evaluations on MOP• 9 input parameters with linear

and nonlinear dependencies

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optiSLang‘s metamodels: MOP – Metamodel of Optimal PrognosisInvestigate Load Bearing Capacity

PolynomialCoD = 81 %CoP = 81 %RMSE = 214

ANNCoD = 99,7 %CoP = 99,5 %

RMSE = 36

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Field-MOP for “2D” areas:

• Shows relevant parameter variationon the surface and their sensitivities

• Approximation of performance maps

• Generation of imperfect surfaces

Field-MOP for 3D volume data:

• Shows influence of all relevant parameter in 3D Simulation/ 3D measurement

• Ability to predict the complete FEM solution for new support points

• Generation of imperfect geometries

Comparison of different brake pads surfaces Comparison of temp. distribution of a FSI Simulation

in 15 h in 1 s

Next Generation Metamodelling: Approximate Field Data

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Key Technology: Random Field DecompositionRandom Field DecompositionExample: Auto parameterization of geometric variations

With courtesy of UK Aachen (Source: S. Raith, WOST 2015)

• Given: 60 measured mandibles

• Task: Find an “optimal” parameterization of variations

• Constraints: Fine meshes, large deformations

• Solution: - Transfer geometries onto reference mesh - Empirical random field model with cross-correlations

of x,y,z-components of the node coordinates

Customer Example Field ROM in time

Parametric Signal represent possible electric loading of ECU unit

Excellent approximation quality for transient temperature and contact stresses!

Field ROM ready for virtual prototyping, system simulation or digital twinsFaster than real time: 9 seconds of loading run in 6 seconds using Field ROM.

A DOE of relevant loading is created and is calculated with high fidelity ANSYS based non-linear electric-thermal-mechanic simulation.9 seconds of loading runs 1 day at HPC cluster.

Riester, K.: Creation of a field meta model on the basis of electro-thermal-mechanical FEM simulations; WOST, 2018, Weimar, Germany, www.dynardo.de

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Customer Example Field ROM for predictive maintenance

DOE Generation: Based on available flight schedules a representative set of flight is selected

The sample set is calculated with high fidelity ANSYS CAE for temperature, stress and life effects on blades.1 selected DOE sample runs 15 hours at HPC cluster.

The forecast quality is quantified to be very good!

Field ROM ready for system simulation and predictive maintenance using digital twins

One flight forecast evaluation runs in seconds

Schulze Spüntrup, H.: Real-time processing with 3D meta models for predictive maintenance of aircraft engines; WOST, 2018, Weimar, Germany, www.dynardo.de

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For more information please visit our homepage:www.ansys.comwww.dynardo.com

Thank you for your attention!

Q&A

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DECEMBER 2019 Process Integration and Design Optimization