Thoughts on Modeling and Simulation in the DOE environment

Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia

Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department

of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Thoughts on Modeling and Simulation

in the DOE environment

David Womble

Sandia National Laboratories

2

Three phases in the design cycle have

different modeling requirements

Requirements definition Design Qualification

Le

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l o

f fi

de

lity

3




Le

ve

l o

f fi

de

lity

High fidelity

Multiple scales and physics

Full environment

4




Le

ve

l o

f fi

de

lity

High fidelity


Full environment

Low fidelity

Rapid turnaround design studies

Component level

Often single physics and scales

5




R238

1.5kQ28

MMBT2907

Q6

MMBT2222

V2

3Vdc

M3

MTB30P06V

R239

1K

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0

RF_PWR3_TXPA

Q62

BFS17A

DA_BATTERY3

R207

1k

R236

1k

RL3

10

0

V8TD = 70ms

TF = 3ns

TR = 3ns

V1 = 3

V2 = 0

Q63

MMBT2907

R242

10R251

499

0

D50

MMSZ5236BT1

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R204

4.99K

V1

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FET_BIAS

VDD

R241

200

R240

100

RF_PWR3_ENB_N

R206

1K

C1

5uF

R226

4.99k

R237

1k

R46

1k

V7

TD = 0

TF = 1ms

TR = 50ms

V1 = 0

V2 = 10.8

0

C40

1uf

0

R210

10K

Le

ve

l o

f fi

de

lity

High fidelity


Full environment

Low fidelity

Rapid turnaround design studies

Component level

Often single physics and scales

High fidelity


Rigorous V&V

6

How much computing is required

for different phases?

Crash scenario

(no practical testing)

Combined abnormal environment

(no testing)

Re-entry (admiral’s test)

Re-entry with a radiation event

(no testing)

Carry/release (limited testing)

Laydown (limited testing)

Launch accelerometer

(limited testing)

Timing and fuzing operations

(limited testing)

Exascale

P

eta

scale

Te

rascale

Normal electrical performance

(with testing)

Co

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on

en

ts

Sys

tem

s

En

viro

nm

en

ts

7



Crash scenario



(no testing)



(no testing)




(limited testing)


(limited testing)

Exascale

P

eta

scale

Te

rascale

Re

qu

irem

en

ts b

ou

nd

ing

eve

nts

Pe

rform

an

ce

eve

nts


(with testing)

Co

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on

en

ts

Sys

tem

s

En

viro

nm

en

ts

8



Crash scenario



(no testing)



(no testing)




(limited testing)


(limited testing)

Exascale

P

eta

scale

Te

rascale

Re

qu

irem

en

ts b

ou

nd

ing

eve

nts

Pe

rform

an

ce

eve

nts


(with testing)

Co

mp

on

en

ts

Sys

tem

s

En

viro

nm

en

ts

Desig

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Qu

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9

Is physics-based predictive?

Is predictive physics-based?

Experiments

Empirical models

and table look-ups

Simulations

Results

Verification

and

Validation

Observational M&S

• Models derived from observation

• Not predictive

• Cannot validate

10

Is physics-based predictive?

Is predictive physics-based?

Predictive modeling and simulation must be physics-based and validated

Experiments

Empirical models

and table look-ups

Simulations

Results

Verification

and

Validation

Observational M&S

• Models derived from observation

• Not predictive

• Cannot validate

Experiments

Physics-based

models

Simulations

Results

Verification

and

Validation

“Physics”

Physics-based M&S

• Models based on “science”

• Can be predictive

• Incorporates multiple scales

• Can validate

11

There are many engineering challenges that will

rely on predictive modeling and simulation

• Large Scale/Complex Structural Response: Structural response due to

severe mechanical insult including shock and penetration

• Predictive Flight Test Simulation: Simulated re-entry body flight

environments including structural load propagation to internal component

mechanical response

• Engine design on a laptop: Turbulent reacting flows in combustion,

predictive simulation code for high-speed mixing & fluid jet break-up

• Thermal Mechanical Failure: Abnormal thermal environments with

applicability to ship fire, plane fire, …

• Energetic Material Initiation: Predictive capability for simulating explosives

• Electromagnetic Effects: Lightning with EM coupling

• Aging: Predictive capability for physical effects such as hardening,

embrittlement, degradation

• Manufacturing: Including flows, coating with the ability to predict as-built

performance

There are many research challenges needed to support a predictive engineering capability

Driven by engineering challenges

• Validated, predictive modeling approach(es) for ductile material crack initiation,

propagation and fracture

• Determining pressure and velocity field turbulent aerodynamic flow fluctuations with

structural dynamic load coupling models & testing (e.g. 6-degree of freedom shaker)

• Improved high resolution, time resolved diagnostics: i) imaging fluid break up, ii)

characterizing explosive initiation, iii) measuring spatial and temporal plasma

properties

• Computationally tractable approaches for multi-scale (10 m – 10 mm) mechanics

simulations (e.g. shock/blast to structural dynamics)

• Coupled thermal-mechanical simulation with time/temperature dependent materials

properties

• Determining operative lightning-induced failure mechanisms in weapon systems

• Rapid problem set up for large models with different levels of detail

Further out

• Computational design of engineered materials/structures at the

continuum scale – with materials science

• Inverse analysis capabilities for failure assessments – algorithm

development with computer sciences

• Community geomechanics model that couples

thermal/mechanical/hydraulic/chemical response – with geosciences

CAD

Model Mesh

13

V&V should be an intrinsic part of all elements

of modeling and simulation

• V&V/UQ is a part of every analysis

• Aspects of “intrinsic V&V” include

– SQA standards and rigorous testing methodology

– Automatic code and feature coverage reports in each log file

– Dynamic test suites designed around key applications

– Support for solution convergence for various quantities of interest by users

– Embedded sensitivities and UQ where possible linked to sample-based UQ

– Integrated workflow that includes support for computing margins

– Support for DoE and coupling to validation experiments

– Code expects models and input to include uncertainties and propagates these

uncertainties through simulations

– Integrated post-processing (e.g., visualization) includes techniques for

understanding and presenting uncertainties and margins

• Customers demand it, analysts perform it, scientists, engineers and

developers enable it

14

Sandia’s X-Prize is an attempt to test to predictivity of

fracture and failure modeling and simulation capabilities

4 Modeling Paradigms Were Used • XFEM – diplacement and discontinuities embedded

in elements

• Peridynamics – meshless method

• Tearing Parameter – plastic strain evolution integral

• Localization Elements – focused on element

interfaces

Three challenge problems were defined to

assess failure initiation and crack

propagation methods

Summary • Predicting ductile failure initiation and crack propagation

remains an extremely difficult problem.

• Wide variation in modeling results suggest our methods

are not yet predictive.

• Limits in experimental capabilities were also observed

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3.5mm 3.5mm 3.5mm

2.5 mm

2.5 mm

2.5 mm

A B C

D

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D E F

A B C

Experimentally Observed

Paths

15

Closing Thoughts

• Modeling and simulation can impact all phases of the

design/qualification cycle, but computing needs are very

different.

• “Predictive” simulation is a high bar.

• The goal is really to be able to use modeling and simulation as

the basis for decisions.

Documents

Thoughts on Modeling and Simulation in the DOE environment