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 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 Interface
Model Calilbration


HyperStudy: Architecture Schema

Model

Variant
Variant
Creation Variant
Variant
Variant

Study Job
Engine: Management
Simulation
Simulation
Simulation
Simulation
DOE Simulation
Fit
Optimization
Stochastics
Results
Results
Extraction Results
Results
Results

Study Results
Optimal parameters
Sensitivities
Model Robustness
…


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

Shape Direct Direct Results Data Technology
Optimization Parameterization Access Mining
State-of-the-art
Seamless integration Automatic transfer of Direct result access to Correlations, exploration,
with HyperMorph modal parameters from most Solvers: Abaqus, SnakeView, PCA, RDA, approximation and
HyperMesh, MotionView, Ansys, Madymo, etc. etc. optimization methods
HyperForm


Next Generation HyperStudy


Next Generation

User Interface
Model 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 Interface
Model 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

• 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 simulation
results 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
 f i − Ti 
min ∑ 
 T  
 i 
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.


Problem Formulation

where


Demonstration


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 93
(99-121) (50-150)
b 120 132 157
(108-132) (100-200)
n 0.15 0.165 0.19
(0.135-0.165) (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 studies
including system identification.
• Design Study methods in HyperStudy are efficient and effective in meeting
design targets.
• HyperStudy is solver independent and can also work with applications
running 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
• design high-performance products,
• reduce cost and development cycle,
• increase the return on CAE investments
with advanced optimization and data mining
capabilities.

“HyperStudy enabled us to efficiently implement DOE and optimization methods. The new automated
process is able to cover different types of applications and can be used in various projects. Besides the
technical advantages and the saved development time, Magna benefits from being an HyperWorks Partner
Alliance member and therefore can use the needed software at no additional costs.”
– Werner Reinalter, Teamleader, MBS Simulation, Magna Steyr

Altair HTC 2012 Hyper Study Training

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