Identification of scatter sources and significant...

© Fraunhofer SCAI

Identification of scatter sources and significant

reduction of scatter occurrence with

DIFFCRASH

Marian Bulla (Altair Germany) Dominik Borsotto (Fraunhofer SCAI)

Clemens A. Thole (Fraunhofer SCAI)

Innovation Intelligence®

© Fraunhofer SCAI

Agenda

Introduction

Reasons for scatter

Analysis methods

Basic analysis methods

Correlation based methods

PCA based methods

Example Case

Analysis of a Chrysler NEON model (RADIOSS)

© Fraunhofer SCAI

Introduction: Reasons for scatter

Potential scatter of simulation results is still a challenging issue

For the design and optimization of car models it is very helpful to deal

with a simulation model, which generates similar results even if slight

changes of the model are performed.

Keyword: Predictability

Reasons for scatter are various.

© Fraunhofer SCAI

Introduction: Reasons for scatter due to

physics

Reasons

Contact / no contact 90° contact buckling

Element failures friction

© Fraunhofer SCAI

Analysis methods: Stability analysis with

DIFFCRASH Postprocessing tool: Identification and separation of multiple sources of

scatter: location and time

Statistical Analysis of full simulation models

Basic analysis methods

Correlation based methods

PCA based methods

© by SCAI-FHG 5

© Fraunhofer SCAI

Analysis methods: Basic analysis

methods

Functionals (per Node):

PD3MX (max. scatter in 3D)

PD3AV (avg. scatter in 3D)

PDXMX (max. scatter in X-direction)

PDYMX (max. scatter in Y-direction)

PDZMX (max. scatter in Z-direction)

PD3IJ (Simulation runs with max. distance)

Relies on different positions of the same node in multiple simulations

© Fraunhofer SCAI

Analysis methods: Scatter visualization

© Fraunhofer SCAI

Analysis methods: Correlation based methods

Correlation analysis

Find strong correlation in data -> causal chains (backtracking of

instabilities)

Elimination of a detected source from the set of results

(Orthogonal projection)

© Fraunhofer SCAI

Analysis methods: PCA based methods

Data Reduction for simulation results: 20 – 200 runs

Parameter changes (Material properties, thicknesses, barrier loc.)

2.000.000 nodes/elements

150 states (time steps in the results)

Dimension: 1 Billion 200 Billion values

PCA Analysis

Small number of modes representing the results

Find the dominating components in the result data, which have the

strongest impact on the simulation results

Subspace comparison to identify buckling

Modes consist of a linear combination of all simulation runs

© Fraunhofer SCAI

Analysis methods: PCA based methods

Global PCA

Computation of scatter modes for the whole model

Visualization as an virtual computed simulation result

Local PCA

Computation of scatter modes for single parts or groups of parts out

of the model

Difference PCA

Different origins of scatter can be identified and physically meaningful

components can be determined

© Fraunhofer SCAI

Analysis methods:

Principle Component Analysis (PCA)

Covariance Matrix:

Eigenvalues/Eigenvectors of the Covariance matrix:

Vectors in the space of coefficients

L2 Norm of

Number of determines the upper bound of the essential size of the

solution space

jiji XXXXA,00 ,

ii ,2

i )(0 iXX

i

i

0

10

20

30

40

50

1 11 21 31 41 51 61 71 81 91

λi (Importance measures)

0

0.05

0.1

0.15

0.2

0.25

0 20 40 60 80 100

© Fraunhofer SCAI

Example of the two most important modes

for the Ford Taurus

© Fraunhofer SCAI

Example case: Chrysler Neon

Frontal Impact on Rigid wall

Model Unit: mm, s, Ton

Initial Velocity: 12.3 m/s

Total Mass : 1.219 Ton

Random Noise: 1.0 E-6 mm

Seed variation (0.00 to 0.9)

© Fraunhofer SCAI

T = 00.00 ms T = 80.00 ms

© Fraunhofer SCAI

Target area

T = 00.00 ms T = 80.00 ms

Overall intrusion in dashboard

© Fraunhofer SCAI

MDSplot

Points represent simulation results

X-Axis: Contribution of most important mode

Y-Axis: Contribution of 2nd important mode.

virtual derived simulationresultsd to

visualize the dominating effect.

What is the Source (in time and space) of

this results Dispersion ?

© Fraunhofer SCAI



“Switching OFF” this scatter source (analytically in DiffCrash) indicates

a significant reduction of displacement scatter in Dashboard area.

Issue area has to be analyzed further, by local

investigation e.g. with the help of MultiDomain .

Blue: Original scatter modes

Green: Without scatter of

engine/Subframe

© Fraunhofer SCAI

Summary

DIFFCRASH allows us to identify and quantify major sources of scatter

The methods allow to devise design and modeling suggestions to reduce

scatter of simulation results

Next steps e.g.:

Applying the multidomain - technique to get a modeling of the critical

region in more detail.

Geometrical changes can force a deterministic behavior in a next

step.

First results of an adapted Chrysler NEON model look very promising

regarding the reduction of scatter at the front wall

OUTLOOK:

Postprocessor interface for GNS Animator (1st prototype ) and

others.

Identification of scatter sources and significant...

Documents

Transcript of Identification of scatter sources and significant...