Workflows zur Systemanalyse und Optimierung in ANSYS

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Workflows zur Systemanalyse und Optimierung in ANSYS anhand der Auslegung eines Elektromotors

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Workflows for analysis and optimization of an electric

motor with ANSYS and optiSLang

M. Schimmelpfennig, Dynardo GmbH ACUM 2016 Linz

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Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang

M. Schimmelpfennig - Dynardo GmbH Linz 2016

Dynardo • Founded: 2001 • More than 60 employees,

offices at Weimar and Vienna • Leading technology companies

Daimler, Bosch, E.ON, Nokia, Siemens, BMW are supported

Software Development

Dynardo is engineering specialist for CAE-based sensitivity analysis, optimization, robustness evaluation and robust design optimization

• Mechanical engineering • Civil engineering &

Geomechanics • Automotive industry • Consumer goods industry • Power generation

CAE-Consulting

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Master of Design Robust Design Optimization (RDO) in virtual product development

Our customized FE-consulting and software products enable you to: • Quantify risks • Identify optimization potentials • Perform variant studies • Secure resource efficiency • Ensure product quality • Improve product performance • Save time to market

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optiSLang • is an general purpose tool for variation analysis using CAE-based design sets (and/or data sets) for the purpose of • sensitivity analysis • design/data exploration • calibration of virtual models to tests • optimization of product performance • quantification of product robustness and product reliability • Robust Design Optimization (RDO)

and Design for Six Sigma (DFSS) serves arbitrary CAX tools with support of process integration, process automation and workflow generation

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Model Calibrations Identify important model parameter for the best fit between simulation

and measurement

Model Calibrations Identify important model parameter for the best fit between simulation

and measurement

Design Improvement Optimize design performance

Design Quality Ensure design robustness

and reliability

Design Quality Ensure design robustness

and reliability

Design Understanding Investigate parameter sensitivities,

reduce complexity and generate best possible meta models

Design Understanding Investigate parameter sensitivities,

reduce complexity and generate best possible meta models

CAE-Data

Measurement Data

Robust Design

Design Improvement Optimize design performance

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SPDM

Input 1

Input 2

Input n

Output 1

with Process Integration

and for Automatization

Workflow-Management

ANSYS optiSLang

Postprocessing

Excel Add-In other Solver

Output 2

Output m

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optiSLang as an ANSYS Workbench plugin • optiSLang modules

Sensitivity + MOP, Optimization and Robustness are directly available in ANSYS Workbench

Signal Processing module to work with curves

inside ANSYS Workbench

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CAX-Interfaces – the ANSYS Workbench Node • optiSLang Integrations provides the flexibility to extend the process chain

• ANSYS Workbench can be coupled with different other solvers like MATLAB, SimulationX or Abaqus

• External geometry or mesh generators can work together with the ANSYS Workbench node

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Optimization of an electric motor

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The motor simulation • ANSYS Maxwell 2D model • commutator principle

Sensitivity analysis with optiSLang • problem understanding • identification of influential parameters • identification of tradeoffs

Optimization with optiSLang • minimization of torque ripples • maximization of the efficiency “eta” η = Pout/Pin

• suitable in this case: ARSM – adaptive response surface method


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Commutator motor: working principle What creates the driving torque?

https://commons.wikimedia.org/wiki/File:Kommutator_animiert.gif

B-field from magnets

B-field from coils

motor characteristics • commutator principle • 12 lamellae & coils • one current branch

U0 = 12 V • fixed outer diameter

OD = 78 mm


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The model: 2D commutator motor FE-simulation

simulation details • time: 16.67 ms in 180 steps Δt = 92.6 μs

• time integration: Backward Euler

• ensure that stationary state is reached (not all designs will become stationary at the same time)

data extraction: • key properties extracted by

analyzing only the last cycle • access to output variables via Ansys Workbench ParameterSet • access to signals via Ansys Workbench or ASCII files


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Model parametrization

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Model parametrisation

magnet_coverage: magnet coverage in percent

rotor_borehole: diameter of motor axis

wall_thickness

magnet_voffset: for widening of air gap

HS0

magnet_rounding: as fraction of magnet thickness

airgap gapwidth

magnet_thickness

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example A: • set rotor diameter • set magnet thickness motor size dependent

example B (used): • set motor size • set magnet thickness rotor size dependent

magnet takes away space available for

rotor and vice versa

the bigger the better (in terms of torque & power)

real-world goal conflict well represented

lost chance to learn about a relevant

tradeoff

Parametrization needs careful decisions


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Calling Maxwell from optiSLang

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Optimization of an electric motor Method A: using Ansys Workbench in Maxwell • export scalar output variables

to Optimetrics • parallel design computation with

Optimetrics Parametric

in the Workbench • optiSLang and Maxwell communicate

through the ParameterSet

parallel/distributed computation: • RSM, PBS, LSF, HPC pack • use “optiSLang inside Ansys”

alternative: • optiSLang full version and a

“Workbench node”

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represented inside the Workbench node

Method A: using Ansys Workbench in Maxwell • export scalar output variables

to Optimetrics • parallel design computation with

Optimetrics Parametric

in the Workbench • optiSLang and Maxwell communicate

through the ParameterSet

parallel/distributed computation: • RSM, PBS, LSF, HPC pack • use “optiSLang inside Ansys”

alternative: • optiSLang full version and a

“Workbench node”


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Method B: scripting and ASCII files direct coupling Maxwell and oSL

Maxwell • run batch job • run Python script • write transient reports into files

signal data accessible

optiSLang • text-based batch job node • extract signal data with ETK • signal data free mathematical

computations inside optiSLang

parallel/distributed computation: • optiSLang spawns Maxwell batch jobs

text file for transporting input

parameters


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the batch script

Python

Optimization of an electric motor Method B: scripting and ASCII files

direct coupling Maxwell and oSL Maxwell • run batch job • run Python script • write transient reports into files





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reading generated stored data







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reading generated stored data







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• extract signal data with ETK green area for data analysis • FFT amplitudes of the reference signal picture for postprocessing


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Sensitivity analysis

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Initial sensitivity analysis (100 DPs)

airgap magnet thickness

magnet edge radius gapwidth

magnet v. offset rotor borehole

magnet coverage wall thickness

hs0 mech. power

losses torque ripple amplitude

restrict some parameters by 20%-30% =

reduction of whole search space by 80%

• Good Meta models for responses but bad for the torque ripples parallel coordinates plot: • select designs of interest • restrict search space

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2nd sensitivity analysis (limited space – 200 DPs) • Very good metamodels for responses • Medium metamodels for the torque ripples • Analyze of the optimization potential • Multi-objective approach • Correlation analysis

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2nd sensitivity analysis (narrowed space) Correlations

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2nd sensitivity analysis (narrowed space) Correlations

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2nd sensitivity analysis (narrowed space) Correlations no linear correlation for torque ripples Are there nonlinear dependencies?

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2nd sensitivity analysis (narrowed space) Getting more information with coloring

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2nd sensitivity analysis (narrowed space) designs with low torque ripples are scattered

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Optimization

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for or the tradeoff is already well captured in the random sampling optimization = picking

eta torque

eta P_mech

Optimization problem definition

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Optimization problem definition for or the tradeoff is already well captured in the random sampling optimization = picking

but for the nonlinear interactions complicate the situation

eta torque

torque_cv any other goal

eta P_mech

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Optimization: starting point

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Objective function minimize:

(1-eta) + 0.4*torque_cv

Constraint:

torque ≥ 0.5


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ARSM (adaptive response surface method) • objective function and constraint

functions treated separately by ARSM • good convergence for the objective

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Reference design

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parallel coordinates plot • select designs of interest • restrict search space

Best design of the sensitivity

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parallel coordinates plot • select designs of interest • restrict search space

Best design of optimization (ARSM)

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reference design

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sensitivity: best design

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optimization: final design

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Next steps: • Take final design as start

for Maxwell 3D analysis • Ad some new parameters • Pre-analysis in 2D saves a

lot of time because the design space in 3D is now smaller

Last step: • Make a robustness analysis to check the influence of tolerances


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Summary

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Summary - Optimization of an electric motor Coupling Maxwell with optiSLang • via Workbench (node) easy use but only scalar parameters • via ASCII files powerful signal processing

(incl. Large Scale-DSO) Sensitivity analysis • identification of important parameters and correlations • exploring tradeoffs and optimization potentials • meta models (MOPs):

can be used for optimization visualization gain knowledge about nonlinear interactions

Optimization • ARSM: efficient & robust algorithm for optimization directly on simulation • torque ripples reduced by 73%, efficiency increased by 36% • play with parametrization and goals

fast gain of engineering intuition

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

Thank you for your attention!

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Workflows zur Systemanalyse und Optimierung in ANSYS