1

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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)

4

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

6

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

End-to-end Modeling

SIMULIA/simpoe

BIOVIA/Materials studio

SIMULIA/Abaqus

SIMULIA/FE-safe

SIMULIA/Isight

7

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

SIMULIA/simpoe

BIOVIA/Materials studio

SIMULIA/Abaqus

SIMULIA/FE-safe

SIMULIA/Isight

Process integration

End-to-end Modeling

8

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

9

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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)

10

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

11

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

11

1st Example: Nitenol Materials Model

Abaqus UMAT for Superelasticity

Upper Plateau

Lower Plateau

mismatch

12

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

12

1st Example: Nitenol Materials Model

Abaqus UMAT for Superelasticity

Upper Plateau

Lower Plateau

mismatch

13

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

13

1st Example: Nitenol Materials Model

Abaqus UMAT for Superelasticity

Upper Plateau

Lower Plateau

mismatch

14

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

14

1st Example: Nitenol Materials Model

Abaqus UMAT for Superelasticity

Upper Plateau

Lower Plateau

mismatch

15

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

15

1st Example: Nitenol Materials Model

Abaqus UMAT for Superelasticity

Upper Plateau

Lower Plateau

mismatch

16

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

17

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

18

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

19

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

22

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

22

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

23

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

TOM Automatic Robust Recipe. Custom Front End

(…DS technology) ..had been incorporated into TOM.

24

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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

25

3DS

.CO

M ©

Das

saul

t Sys

tèm

es |

Con

fiden

tial I

nfor

mat

ion

| 7/1

/201

4 | r

ef.:

3DS

_Doc

umen

t_20

12

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)