Altair HTC 2012 Hyper Study Training

Model Calibration using

Altair HyperStudy

Innovation Intelligence® Fatma Koçer

Altair Engineering

May, 2012

Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.

HyperStudy is:

• Solver Neutral Design of Experiment,

Multi-Disciplinary Optimization and

Stochastic Simulation Engine .

• Automates processes for parametric

study, optimization and robustnessstudy, optimization and robustness

assessment

• Integrated with HyperWorks thru

HyperMesh, MotionView and direct solver

interfaces


HyperStudy: Business Benefits

Design high-performance products

Reduce cost and development cycle

Increase the return on CAE investments

Cost effective and innovative licensing model


HyperStudy: User Benefits

• Streamlined design exploration, study and optimization process

• Solver-neutral , multi-disciplinary

• Advanced data-mining capabilities

• State-of-the-art optimization engine

• HyperWorks integration: Morphing, Direct parametrization, Results Readers


Unilever Corp. (UK)Optimal Comfort Softener Bottle Design

Challenge: Increase collapse load and stiffness of a softener bottle while minimizing the mass

Solution: • DOE to screen design variables:

• Fractional Factorial Method

• 7 design variables are selected

• DOE to create Approximate Model:

• Box Behnken Method

• Optimization using the Approximate Model:

• ARSM

Results:• Buckling capacity increased over 20%

• Mass reduced over 5%

“HyperStudy provides potential for reducing design cycle times, through facilitating definition of strong design concepts early in the design process, which require fewer down-stream modifications.”

– Richard McNabb, Design Analysis and Technology Manager, Lever Fabergé, Unilever Corporation


Capabilities Overview


Next Generation

User InterfaceModel Calilbration


HyperStudy: Architecture Schema

StudyEngine:

Model

VariantVariantVariantVariantVariant

SimulationSimulation

Creation

JobManagementEngine:

DOEFitOptimization Stochastics

ResultsResultsResultsResultsResults

SimulationSimulationSimulationSimulationSimulation

Study ResultsOptimal parameters

SensitivitiesModel Robustness

…

Management

Extraction


Parameters Screening

HyperStudy: Study Types

DOE Approximation Optimization Stochastic

Parameters Screening

System Performance Study

Response Surface Evaluation

Optimum Design

Variation Analysis

Robust Design

Reliability Design


HyperStudy: Key Differentiators

Technology

State-of-the-art

exploration,

approximation and

optimization methods

Direct Results Access

Direct result access to

most Solvers: Abaqus,

Ansys, Madymo, etc.

DataMining

Correlations,

SnakeView, PCA, RDA,

etc.

Direct Parameterization

Automatic transfer of

modal parameters from

HyperMesh, MotionView,

HyperForm

Shape Optimization

Seamless integration

with HyperMorph


Next Generation HyperStudy


Next Generation

User InterfaceModel Calilbration


Next Generation HyperStudy

• Differentiators of HyperStudy are kept• tree-based process • navigation in the study pages

• Changes in user interface• data in tables• extended edition features• dedicated wizards

• Enhanced Task Management• orchestration• live monitoring and control

• Improved Post-Processing• multiple plots• richer charting

•Reporting• messaging• study report


Model Calibration using HyperStudy


Next Generation

User InterfaceModel Calibration


Background

• We need to model 6063 T7 Aluminum material in Radioss for the first time.

• 6063 T7 Aluminum has an isotropic elastic-plastic behavior which can be reproduced by a Johnson-

Cook model without damage as:

• In Radioss Johnson-Cook model can be defined using the Law2 material card as:


Background

• In this card, we do not know the values for five material properties: Young’s modulus, yield stress

(a), hardening modulus (b), hardening exponent (n) , and maximum stress.

• We have strain-stress curve from tensile testing of a a 6063 T7 Aluminum sample• We have strain-stress curve from tensile testing of a a 6063 T7 Aluminum sample

• Our objective is to find the five material property values of Radioss Law2 card such that Radioss

simulation of the tensile test gives the same curve as the test. Then we can be confident in our

material model for further simulations.


Background

• We can model the tensile testing in Radioss as a quarter of a standard tensile test and

using symmetry conditions. A traction is applied to the specimen via an imposed velocity

at the left-end.

Thickness = 2.0 mm

• We can then calculate the engineering strains are by dividing the node 1 displacement by

the reference length (75 mm), and engineering stresses by dividing the section 1 force

by its initial surface (12 mm2).

Node 1

(displacement) Section 1

(force)


Results from the Initial Radioss Simulation

• Radioss simulation with initial guesses of Young’s modulus, yield stress (a), hardening

modulus (b), hardening exponent (n) , and maximum stress values of 60400 MPa, 110

MPa, 120 MPa, 0.15, 280 MPa leads to significant differences between the test and

simulation results as seen below


Objective

The objective is to find the values for the five material properties so that the simulationresults match to tensile test results. We can achieve this if we minimized (ideally zero):1. difference between Radioss and experimental stress (141MPa) at Strain equal 0.02

2. difference between Radioss and experimental stress (148MPa) at Necking point

3. difference between Radioss and experimental strain (0.08) at Necking point


Method

• We will use optimization to achieve this objective.

• We will use a special optimization problem formulation called “System Identification”.

• System identification minimizes the sum of normalized error-squared. Error is the

difference between the target values and simulation results.

2

where fi(x) is the ith response obtained from analysis,

Ti are the target value for the ith response.

• Note that, in HyperStudy we do not need to enter this equation manually. We can simply

enter the target values for each response and use the “System Identification” objective.

∑

−2

mini

ii

T

Tf


Problem Formulation

where


DemonstrationDemonstration


DOE Results

• 32 Design Full Factorial

• Young’s Modulus and SigMax are not significant so we will continue our study with three design

variables.


First Optimization Results

• Adaptive Response Surface Method (ARSM) is used for this case.

• In 5 iterations, we minimized the system identification objective function value from

0.158 to 0.06.

• In the optimum design, the DV values are: 99, 132, 0.165

• The response values are: 140, 146, 0.06 (note that the targets were 141, 148 and 0.08;

initial design values were 147, 150, 0.05)

• We observe that all three design variables are at their lower or upper bounds.

• If we can relax those bounds; we may be able to get closer to the target values.

• We started a new optimization from the best result of the first optimization and with

relaxed bounds.


Second Optimization Results

• ARSM is used for this case.

• In 10 iterations, we minimized the system identification objective function value from

0.06 to 0.0.

• In the optimum design, the DV values are: 93, 157, 0.2.

• The response values are: 140, 149, 0.08 (note that the targets were 141, 148 and 0.08)

First two objectives are off by 1.0 from the target and last one is on target


Results

Initial Opt 1 Opt 2

Variables

E 60400 60400 60400

a 110 99(99-121)

93(50-150)

b 120 132 157(108-132) (100-200)

n 0.15 0.165(0.135-0.165)

0.19(0.1-0.3)

Sigma 280 280 280

Responses

Obj1 147 (Target 141) 140 140

Obj2 150 (Target 148) 146 149

Obj3 0.05 (Target 0.08) 0.06 0.08


Results

• Radioss results for the

Initial Design vs. Test

Results: There are

significant differences

between the two curves.

• Radioss results for the

Optimum Design vs. Test

Results: The two curves are

almost identical.


Conclusions

• HyperStudy provides a user friendly GUI to easily set up design studiesincluding system identification.

• Design Study methods in HyperStudy are efficient and effective in meetingdesign targets.

• HyperStudy is solver independent and can also work with applicationsrunning other solvers such as LS-Dyna, Abaqus, Ansys, Adams, etc.


Altair HyperStudy

Altair HyperStudy is a

• user-level,

• solver neutral,

• multi-disciplinary,

• exploration, study and optimization tool,

helping engineers to

“HyperStudy enabled us to efficiently implement DOE and optimization methods. The new automatedprocess is able to cover different types of applications and can be used in various projects. Besides thetechnical advantages and the saved development time, Magna benefits from being an HyperWorks PartnerAlliance member and therefore can use the needed software at no additional costs.”

– Werner Reinalter, Teamleader, MBS Simulation, Magna Steyr

helping engineers to

• design high-performance products,

• reduce cost and development cycle,

• increase the return on CAE investments

with advanced optimization and data mining

capabilities.