Date post: | 19-Aug-2014 |
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Needs and trends of crash simulations in the next 10 years
Paul Du Bois
June 2014
PDB
Overview
• The big question
• Building larger models
• Building more models
• Modeling the process chain
• Conclusions
predicting failure is far more difficult than predicting ductile deformation
J. Jergeus, 2012
PDB
The big questions
• An increase of a factor of 25 in CPU availability could be achieved in
somewhat less then 10 years by Moore’s law
• How far will the capability to run 100M elements rather then 4M elements
get us with respect to predictive potential of crash simulations ?
• How do we best use that capability in the context of automotive
development ?
• How to define industrial reliability ?
A simulation result is reliable if it
drives the design in a direction that
improves the subsequent test result
This must be achieved in 99% of all cases
PDB Copyright © 2013 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.
6577
3865
1785
1036 909 495 347 355 542
325 267 349
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7000
128 256 512 1024
Engine Performance (DP) on Curie super computer
E5-2680@2,7GHz
Pure MPI 8 OpenMP 16 OpenMP
RADIOSS : PRACE project 15 Melts model
• Pure MPI scales very well up to 1024 domains
• Test Hybrid with 8 (optimal data locality on Sandy Bridge) and 16 OpenMP (max per node)
• Excellent scalability up to 4096 cores ( 512 * 8 and 256 MPI x 16 OpenMP)
• Maximum performance achieved using 8192 cores (512 x 16)
• First time ever run with 16384 cores ! Need bigger model (less than 1000 solids per core in this case )
#domains
Elapsed (s)
16384 cores 4096 cores 8192 cores
63.24267
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Mercedes-Benz Cars, Dr. Markus Feucht (EP/SPB) / Chrysler TC 14-05-2012 5
Crashsimulation History
W220 (1998)
250.000 E. W168 (1996)
130.000 E.
1990
W124 (1988)
25.000 E.
W211 (2000)
600.000E.
1995 2000 2005
Model size Crash/ Mio. El.
1
W210 (1994)
75.000 E.
W221 (2003)
>1.1 Mio. E.
W251 (2006)
1.8 Mio. E.
(EH-Modell)
W212 (2010)
2.8 Mio. E.
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Mercedes-Benz Cars,
Crash simulation 2014 Example: Frontcrash
(Euro NCAP, 64 km/h)
Show model: 6 Mio elements
Element size: 3…5mm
Turn around time: 10-12 hours
(MPP-Cluster 192 CPU,
FEM-Code LSDYNA)
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Crashsimulation History analysis & Prognosis
1
No mesh convergence close to convergence failure
Prony series fit
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State of the art in vehicle component modelling 4PB test
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State of the art in vehicle component modelling
4pb 5mm
4pb 2.5mm
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observations
• Convergence in terms of displacement and force does not necessarily inly
convergence in terms of stress and strain
• Failure models without regularisation cannot work on non-homogeneous
meshes as failure will be biased towards the smaller elements
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11
Crashsimulation History analysis & Prognosis
1
Trendline predicts 70M elements by 2024
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What are our options ?
• Bigger models
• Shells
• solids
• More models
• More load cases
• Stochastic analysis
• Process Chain
• Manufacturing simulations
• Mapping
• Unification
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Bigger models
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Thin shell, thick shell, 3D shell or solid ?
• Assumptions of thin shell theory are fulfilled for curvature radii up to 5t
• Thick shell theory should be invoked for radii<5t
• Although fibers are still straight, thin shell theory will over/under predict
strains in the outer layers due to changes in lamina length
• Reasons to go to solid elements are : T-joints, local necking, through-the-
thickness shear failure ( all induce a 3D state of stress )
12t
Thin shell, inner=outer=middle fiber
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Model size for a car body :
• Estimate of converged meshes for a car body for a small and large car :
• Mesh refinement beyond this point will add limited value with thin shell elements
• Estimate of ‘converged’ meshes using solid elements : multiply by 1000,
corresponding to 10 elements through the thickness
• Studies in ballistics suggest convergence may take 100-1000 elements through the
thickness
• Recently seen the first model > 1 billion elements
Car body 20m**2 2mm sheet 40m**2 1mm sheet
# shell elements 4M 40M
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Speed-up techniques :
• More and more speed-up techniques are developed
• Adaptivity
• Subcycling / multi-scale
• advanced mass scaling
• …
• Usually these techniques work very well for displacement driven problems (
problems where we know the final deformed shape ) but must be very carefully
assessed for bifurcation problems as they tend to favor certain deformation modes
• In brief : bigger crash models will mean more cpu
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More models
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about 30 loadcases are currently investigated
regulators are very inventive
Mercedes-Benz Cars, Dr. Markus Feucht (EP/SPB) / Chrysler TC 14-05-2012 18
PDB
Crash simulation results with mapped data (thickness, plastic strain)
Crack
Local very high damage (90%)
but still no crack initiation
No damage mapping With mapped pre-damage
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20
Validation of forming simulation Analysis of tension tests from B-pillar
B – Säule
Innen
IN 1 IN 2 IN 3 IN 4
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450
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0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 50,0
Dehnung e [%]
Sp
an
nu
ng
s [
MP
a]
•Real component shows
softer behavior than
crash-material card (ca.
10%)
•Reduction of local failure
strain is captured well by
pre-damage
Experiment
Simulation with
strain mapping
Simulation
without strain
mapping
Allowed tolerance band (Rm)
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21
Crash simulation results with mapped data (thickness, plastic strain, damage)
• Crack initiation is very sensitive to small changes in
tensile strength (10%)
• Same failure strain in both simulations
(no change GISSMO card)
Standard material card material strength -10%
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Mercedes-Benz Cars, Dr. Markus Feucht (EP/SPB) / Chrysler TC 14-05-2012 22
Material cards in crash simulation Work practice with tolerances
• Virtual experimental curves must be
created as base for the definition of
„min/max“ cards
• Daily development work in CAE Passive
Safety: Use of „average material cards“ or
individually „min/max“ cards for worst case
scenarios
• Problem: What is worst case?
•=> Robustness investigations on a
stochastic base are neccesary!
„Min“
„Max“
„Mean“
Experiment
Tension test curves
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Modeling the process chain
The failure of a high strength steel part is often preprogrammed in the manufacturing
process
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24
Advanced High Strength Steels Reduced ductility
22MnB5
CP800
TRIP800
ZE340 Aural
TWIP
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Mercedes-Benz Cars, Dr. Markus Feucht (EP/SPB) / Chrysler TC 14-05-2012 25
Local distribution of mechanical properties Variations in yield stress and failure strain
0
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0,00 0,05 0,10 0,15 0,20
etechnisch
ste
chnis
ch
[MPa]
MN1-Rz-S1
MN1-Rz-S2
MN1-Rz-S3
MN1-Rz-S4
MN1-Rz-S5
MN1-Rz-S6
MN1-Rz-S7
MN1-Rz-S8
MN1-Rz-S9
MN1-Rz-S10
MN1-Rz-S11
MN1-Rz-S12
Technische Sigma-Epsilon-Kurve
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0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70
e
ste
ch
nis
ch
[M
Pa]
QH2-1-Fz-S1
QH2-1-Fz-S2
QH2-1-Fz-S3
QH2-1-Fz-S4
QH1-Fz-S1L
QH1-Fz-S1Q
QH1-Fz-S1D
Technische Sigma-Epsilon-Kurve
Press hardened steel 22MnB5 Micro alloyed steel ZStE340
1 1
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Mercedes-Benz Cars, Dr. Markus Feucht (EP/SPB) / Chrysler TC 14-05-2012 26
Influence of the manufacturing process on material properties
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27
Process chain B-pillar
Plast. strain Thickness damage
Forming simulation
Anisotropic plasticity (Barlat)
*MAT_ADD_EROSION
(GISSMO)
Crash simulation:
J2 plasticity (Mises)
*MAT_ADD_EROSION
(GISSMO)
Mapping
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Does the crash group need it‘s own manufacturing team ?
• Metalforming, hotforming and casting simulations are far advanced , however
do not always provide exactly the type of output needed for the crash model
(e.g. initial damage , microporosity…)
• Special-purpose manufacturing simulations may be needed
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Anisotropic plasticity : thin shell vs 3D shell
Thin shell midplane 3D shell midplane
Increase of damage in the midplane due to 3D state of stress
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Anisotropic plasticity : thin shell vs 3D shell
Thin shell maximum 3D shell maximum
decrease of maximum damage due to 3D state of stress
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Isotropic vs anisotropic plasticity
Isotropic midplane anisotropic midplane
Computed damage values differ by almost 50%
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Isotropic vs anisotropic plasticity
Isotropic maximum anisotropic maximum
Computed damage values differ by 25%
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Material law and/or failure law ?
• Too much emphasis has been put on failure laws and too little on material
laws
• Adding a failure law to J2 plasticity will not always do the job
• Higher order anisotropic plasticity with distortional hardening may be needed
• In this case mapping the material directions from the forming results is critical
• Mapping vectors is hard : a sensitivity study will be needed to learn about the
required accuracy
• A common material law for crash and forming would be ideal : currently no
material law that has the required accuracy and robustness under bifurcation
problems (=crash)
• Need to finetune a generalized metals plasticity law with the needed
efficiency, accuracy, robustness and user-friendliness
PDB
Material Data Set
• An data set was provided for material
DBL4919.10, extruded aluminium
• This data set included global tensile
test measurements for three different
angles
• 0°, 45° and 90°
/Presentation/MAT135OPT
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Material Anisotropy
• At first glance, the selected
material does not look
anisotropic based on the yield
stress
• Failure strain varies, however it
can be attributed to
measurement scatter
• R00 was measured using the
Aramis system to be 0.49
indicating strong anisotropic
flow 0
50
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200
250
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0.00 0.05 0.10 0.15 0.20 0.25 0.30
e
s [
MPa]
VP3-Fz-S1L
VP3-Fz-S2L
VP3-Fz-S3L
VP3-Fz-S1Q
VP3-Fz-S2Q
VP3-Fz-S3Q
VP3-Fz-S1D
VP3-Fz-S2D
VP3-Fz-S3D
0°
90°
45°
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Yield curves
Extrusion Direction
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Material data for anisotropic plasticity model :
• Reference material shows R values as:
• R00 = 0.48, R45 = 0.29, R90 = 1.76
• “Bumper Beam Longitudinal System Subjected to Offset Impact Loading” Kokkula
(PhD Thesis)
• AA-6060 T1 Aluminum
• Optimized R values for AW-6060 T66 are:
• R00 = 0.49, R45 = 0.27, R90 = 1.69
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Crashworthiness Application
• This model was tested to improve the response/failure prediction of an
extruded tube profile
• Original model was Material 24 in LSDYNA
• Initial simulations provide excellent force vs. deflection results however the
simulation lacks the necessary plastic strain to create element failure
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Profile Bending Simulation
Three point bending test
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Summary and conclusions
Start your own metalforming department
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Summary and conclusions
• Assuming we achieve a 25 fold increase, we will be as short of CPU in 2024
as we are today (The need for cpu goes up with the square of the availability (
T. Belytschko ))
• The need of predictability with respect to failure will force a unification of
methods between manufacturing and crash
• Unlike manufacturing, crash is a bifurcation problem and therefore may have
stochastic aspects, this is particularly true where failure is concerned
• Failure related research currently likely puts too much emphasis on failure
and damage models and too little on material laws
PDB
Thank you very much