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3DS
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Das
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3DS
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Integration – The I in ICME (*)
Dr. Alex Van der Velden
DS/SIMULIA
Technology Director, CTO office
(*) title from Integrated Computational Materials Engineering: A
Transformational Discipline for Improved Competitiveness and National
Security Committee on Integrated Computational Materials Engineering,
National Research Council ISBN: 0-309-12000-4, page 92 (2008)
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Spectrum of Multiscale Simulation
Full system
Sub system
Smeared Composite
Bulk Scale Constituents
Micro-
structure
Molecules
Electrons
System-of-systems
Agent-based
simulation
Co-
simulation
3D FEA,
homogenous
materials
3D FEA,
composite
materials
3D FEA,
multiple
materials
Phase-field
simulation
Molecular
Dynamics
Chemical
reaction
simulation
SIMULIA
CATIA
BIOVIA
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End-to-end Modeling
SIMULIA/simpoe
BIOVIA/Materials studio
SIMULIA/Abaqus
SIMULIA/FE-safe
SIMULIA/Isight
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SIMULIA/simpoe
BIOVIA/Materials studio
SIMULIA/Abaqus
SIMULIA/FE-safe
SIMULIA/Isight
Process integration
End-to-end Modeling
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Isight
Eclipse IDE for development of custom component/plugins
Modern graphical interface
Integrates 30+ applications using components (Abaqus, Excel and other 3rd party) into a simulation process flow
50+ edge DOE, optimization, approximation and quality methods
Execution on desktop, distributed stations and connectivity to commercial grid engines.
Postprocessing of multi-run jobs
Desktop Process Integration and Design Optimization Environment
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SEE Execution Engine
Centralized simulation process flow and component library (shares with Isight) with model and results sharing
Dashboard monitoring & job hot restart
Connected to commercial grid engines (LSF, PBSpro… )
Build on Websphere/Weblogic WAS & Webtop deployment
OPEN, service oriented architecture certified
B2B IP protected.
Process Flow Job Execution, Management and Sharing
SEE
Isight/SEE based on The Federated Intelligent Product EnviRonment (FIPER)
http://www.atp.nist.gov/gems/oai-99-01-3079.htm (Stanford, Engineous, GE,
Goodrich, Parker. NIST 20M$ ATP funding 1999-2003)
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Scales to Easy-to-Use Large Systems Halliburton/Landmark AssetConnectTM
Szatny, Lochman, Integrating Business and Technical Workflows to Achieve
Asset-Level Production Optimization, Halliburton Landmark 2010
Custom UI
Functional Flow
Process Simulation
Integration scale (IN A SINGLE MODEL) achieved by several customers
250 disciplinary simulation tools, 100 excel spreadsheets, 20K
parameters/arrays, ~1K model files, and ~10 M database records
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1st Example: Nitenol Materials Model
Abaqus UMAT for Superelasticity
Upper Plateau
Lower Plateau
mismatch
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1st Example: Nitenol Materials Model
Abaqus UMAT for Superelasticity
Upper Plateau
Lower Plateau
mismatch
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1st Example: Nitenol Materials Model
Abaqus UMAT for Superelasticity
Upper Plateau
Lower Plateau
mismatch
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1st Example: Nitenol Materials Model
Abaqus UMAT for Superelasticity
Upper Plateau
Lower Plateau
mismatch
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1st Example: Nitenol Materials Model
Abaqus UMAT for Superelasticity
Upper Plateau
Lower Plateau
mismatch
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Model Calibration with Data-Matching
Simulation
Uncertainty
DOE
Geometry, Materials
Geometry
(known)
Vary Material m,s (unknown)
Min S( Dm, Ds )
i=1
n
Approximation
Uncertainty
experiment
model
Sampling
Uncertainty
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Assessment: Model vs Experiment
Approximated model
experiment
Geometry Variation Measured
Free OD, s=0.6%
Crown Height, s=0.8%
Wire Diameter, s=0.8%
12 Actual Experimental Samples
12 Random Model Samples with Data-
Matched Distributions
Distribution mean passes
Chi-Square test, except in extremes (Not enough samples for distribution test)
1
7
2
3
4
5
6
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Precision and Accuracy of Approximation 7 predictions of Hoop Force
4th order Response Surface
Method with 32 term reduction
200 sample DOE: 0.8< Upper Material Plateau<1
…
Precision s< ~ 0.002 lbfs cross validation, no calibration required
worst
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Uncertainty Quantification of Nintenol Material
Upper Plateau
Lower Plateau
m s
EA Multiplier 1.07
0.05
EM Multiplier 1.10
0.07
eL Multiplier 0.92
0.06
Lower Plateau Multiplier
0.92
0.06
Upper Plateau Multiplier
0.88
0.03
Based on 12 samples so precision is low ~ 0.01 0.8 correlation
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Batch-manufacturing: Optimization for Minimum Quality-Loss*
Quality optimum
Spec
quality
units
Elongation Piercing Induction Furnace Equalization
Billet Heating Multiple Stand Rolling Mill
Rotary Sizing Cool
Vary tool set points (recipes)
Minimize deviation from spec with constraints on equipment operation
Key issue is uncertainty – manufacturing conditions are not known precisely but vary according to certain probability distributions
Goal is an optimum manufacturing setup that is insensitive to this uncertainty and meets spec.
* “Controlled Thermo-mechanical processing of tubes and pipes for enhanced manufacturing
performance” Timken Co DOE DE-FC36-99D13819
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TOM Automatic Robust Recipe. Custom Front End
(…DS technology) ..had been incorporated into TOM.
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TOM saves double digits in energy and cost
• …complete process recipes for bearing and
automotive grades were optimized using the
“.. (now DS technology) “ feature within
TOM.. and made available as recipies.
• .. The REML* process overall that 25% less
energy intensive. .....
• workflow type of the CMTP and Stent
example are the same: Optimization to
minimize variance and mean, versus
optimization to target variance and mean.
Energy consumption comparison. *REML: Robotic
Enhanced Manufacturing Line Vs Baseline
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Where we are now and what are the challenges…
Existing software Integration Frameworks (like Isight/SEE or Pipeline Pilot) and SLM (simulation lifecycle management platforms) can be used today for automating ICEM Workflows and to support collaboration between researchers. (*)
Multi-scale Multi-scale abstractions (Here, there is much to do)
We need to understand the effect of changes in one scale on the next (hierarchical refinement)
Each lower abstraction needs to have higher accuracy than the lower abstraction
We need to capture the stochastic material properties in operation
We need to apply rigorous V&V processes to verify numerical methods (e.g. ASME V&V10 verification and validation in computational solid mechanics)
(*) from Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and
National Security Committee on Integrated Computational Materials Engineering, National Research Council ISBN: 0-309-12000-4,
page 92 (2008)