Date post: | 12-Sep-2014 |
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Technology |
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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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
HyperStudy: Business Benefits
Design high-performance products
Reduce cost and development cycle
Increase the return on CAE investments
Cost effective and innovative licensing model
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
Capabilities Overview
Capabilities Overview
Next Generation
User InterfaceModel Calilbration
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
HyperStudy: Architecture Schema
StudyEngine:
Model
VariantVariantVariantVariantVariant
SimulationSimulation
Creation
JobManagementEngine:
DOEFitOptimization Stochastics
ResultsResultsResultsResultsResults
SimulationSimulationSimulationSimulationSimulation
Study ResultsOptimal parameters
SensitivitiesModel Robustness
…
Management
Extraction
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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
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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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
Next Generation HyperStudy
Capabilities Overview
Next Generation
User InterfaceModel Calilbration
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
Model Calibration using HyperStudy
Capabilities Overview
Next Generation
User InterfaceModel Calibration
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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:
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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.
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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)
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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
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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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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
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Problem Formulation
where
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DemonstrationDemonstration
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DOE Results
• 32 Design Full Factorial
• Young’s Modulus and SigMax are not significant so we will continue our study with three design
variables.
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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.
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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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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.
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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.
Copyright © 2012 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
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.