optimisation and robust design for car body development · optimisation and robust design for car...
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Optimisation and Robust Designfor Car Body Development
Dr. habil. Fabian DuddeckReader for Computational Mechanics
Technical University of Munich
May-08 AUTOSIM - Duddeck 2
Objectives of AUTOSIM1
• Integration of simulation into the product development process (PDP)
• Characterization of materials
• Establishing confidence in the use of simulation
1 Taken from AUTOSIM network bulletin issue 3 (April 2008)
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Objectives of AUTOSIM (cont.)
• To facilitate the use of advanced simulation technologies (FEM, CFD, etc).
• To improve the quality and robustness of modelling and simulation.
2. Quality Assurance methods for CAE / car body development
1. Optimisation and robust design for car body development
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AUTOSIM - Main Topics
• Integration:Optimisation strongly depends on it.
• Materials:Optimal material selection
• Confidence:Robustness, Quality Assurance
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Optimisation and Robust Design for Car Body Development
• Solid Mechanics (FEM):Evaluation of methods and algorithms for topology, shape and multi-disciplinary optimisation for crash and NVH;
• Fluid Mechanics (CFD):EU project FLOWHEAD, FP7, QMULFluid Optimisation Workflows for Highly Efficient Automotive Development Processes.
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Optimisation for Solid MechanicsTypesShape
Particular geometry of the structure
Gauge
Fine adjustmentof the structure
• Optimal material selection• Optimal geometry• Robust results
Topology
Principal conceptsfor the structure
• Early and series development phases
• Optimisation under uncertainty / lack of knowledge
Source: Duddeck et al.: 3rd M.I.T. Conf. on ComputationalFluid and Solid Mechanics, Cambridge June 2005.
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Gauge Optimisation
Intrusion
Limit = 100 %
Mass
Initial mass
Δm = -19.3 kg
Dynam ics
Limit = 100 %
91.1 %
Torsion I
Non-effective approach.
Mass
Initial mass
Δm = +9.1 kg
Iterative optimization required:
Frontal im pact
Source: Duddeck et al. (2005)
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Multi-disciplinary Optimisation
Crash disciplines
NVH disciplines
Mass
Initial mass
Δm = -13.5 kg
Multi-disciplinary optimisation:Integrated optimisation approach
0
20
40
60
80
100
Degree of optimizedfunctionality
%
Fron
tLa
tera
lR
ear
Dan
ner R
ear
Dan
ner
Fron
t
Dyn
amic
sSt
atic
s
BMW (2001-2005): Evaluation study (4 test cases) for ~20 different algorithms
Source: Duddeck et al. (2005)
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Multi-disciplinary OptimisationLateral impactIIHS, EuroNCAP,
Pole, etc.
MDO: 14 days300-500 different
designs
Rear impact(high speed)FMVSS 301
StaticsBending, torsion etc.
Frontal impact(high speed)
EuroNCAP, etc.
Frontal impact (low speed)Danner test
DynamicsBending, torsion etc.
Rear impact(low speed)Danner test
Source: Duddeck et al.
(2005)
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Optimisation Algorithms
Non-linear optimisation
Search strategies Gradient-based strategies
Deterministicstrategies
Stochasticstrategies
Quasi Newtonstrategy
Gauss Newtonstrategy
Jacobmethod
Simplexmethod
EvolutionaryStrategies (ES)
GeneticAlgorithms (GA)
Simulated Annealing (SA)
BFGSmethod
Sequentialquadratic
programming
DampedGauss-Newton
Trust regionmethod
LevenbergMarquardt
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Surrogate Models
Different disciplines: Crash, NVH etc.
Evolutionary Strategies Response Surface Methods
Advantages Disadvantages
Appropriate for all disciplines
Self learning strategy
High number of design variables
Global search possible
High numerical effort
Parameter of the algorithm are important
Slow convergence
Advantages DisadvantagesFaster if the response surface (RS) is “good”
Not appropriate for all disciplines(crash!)
Step-wise progression recommendable
Statistical values for quality of RS
High numerical effort for higher number of design variables
Different RSM available (which?)
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Example for Surrogate Models
Ratio between via RSM predicted valuesand real computed values
Discipline PolynomialRSM Kriging
KernelSmoother
104 %
100 %
77 %
Frontal impact 87 % 115%
Lateral impact 97 %
103 %
101 %
Rear impact 98 %
Source: Duddeck et al. (2005)
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Example for Surrogate ModelsFE-Modell
Successive polynomial RSM with ~250 samples
Source: Duddeck et al. (2005)
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Intelligent Method Selection
Definition of the project
Generation of the initial set of variants (DoE)Computation and evaluation of the variants
Automaticdecision rule
SelectionRecombination
Mutation
Termination ?
Evolutionary Strategy
No
Choice of themeta model
Termination ?
Surrogate Models
No
Analysis of robustness
End of project
Statistical analysis of the resultsValidation of the approximation model
Stepwiseoptimization New generation
Fitness
Intelligent switchbetween strategiesdepending on the
discipline
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Shape Optimisation
• Methods without re-meshing:e.g. morphing;Only for small geometrical changes;
• Methods with re-meshing:For larger geometrical changes;Still a challenge;
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Parametric Model
Points and Lines
Cross Sections
Joints & Beams
Welds, Adhesives
FE Meshes
Beads, Stamps, Ribs
Freeform Surfaces
Loading, Etc.© SFE GmbH 2008
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Parametric Model
Max 1
Min 1
Max 2
Min 2
© SFE GmbH 2008
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Parametric Model
© SFE GmbH 2008
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Shape Optimisation Example
K Volz, B Frodl, F Dirschmid, R Stryczek, H Zimmer (2007): Optimizing Topology and Shape for Crashworthiness in Vehicle Product Development. Int. Automotive Body Congress (IABC), 17 – 19, June 2007, Berlin.
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Shape OptimisationDesign Variables
Volz et al. (2007).
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Shape OptimisationInitial and Final Configurations
Initial guesses
Final design Volz et al. (2007).
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1. Topology – Shape Optimisation
Initial model forshape optimisation
Topology optimisation of the frontend
Passenger cabin
Volz et al. (2007).
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2. New Body Concepts
Referencestructure
New bodyconcept
Volz et al. (2007).
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3. Shape Optimisation
Volz et al. (2007).
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4. Validation
Volz et al. (2007).
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5. Robustness
Which criteria is robust?Which criteria should be taken for surrogate models?
M Thiele, M Liebscher, W Graf: Fuzzy analysis as alternative to stochastic methods – a comparison by means of a crash analysis. 4th German LS-DYNA Forum 2005, Bamberg.
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5. Robustness
Source: Renault, Europ. Autom. Safety 2004
Frontalimpact
Rearimpact
Lateralimpact
Loadpaths for
crash
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5. Robustness
T Streilein, J Hillmann (2002): Stochastic Simulation and Optimisation focused on the VW Phaeton, VDI-Conf., Würzburg, Germany
Scatter Experimentn = 4
Intrusion 18.64 %
Scatter Simulationn = 72
Intrusion 11.41 %
May-08 AUTOSIM - Duddeck 29
CFD OptimisationFLOWHEAD (EU FP7)
Fluid Optimization Workflows for Highly Effective Automotive Development Processes
Collaborative ProjectCall: SST.2007-RTD-1 Competitive product development
Coordination: J. Müller / F. Duddeck, Queen Mary, Univ. of London
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FLOWHEAD (EU FP7)Motivation and Approach
1. Derive methods for optimisation and robust design for fluid problems for the automotive industry;
2. Cover the full range of applications;3. Integrate the methods into workflows
adapted to PDP of the automotive industry.
The approach is mainly based on adjoint methods combined with topology, shape and parameter optimisation including robust design techniques.
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FLOWHEAD (EU FP7)Strategic Objectives (cont.)
Improving automotive PDP in the following fields:1. Internal combustion: reduction of emissions / fuel consumption;
2. Aerodynamics: reduction of drag, fuel consumption, CO2-emissions;
3. Interior and exterior acoustics: reduction of noise, improving comfort;
4. Cabin comfort and safety: effectiveness of heating, cooling, defrosting, demisting, etc.
5. Thermal management: effectiveness of engine cooling
6. Exhaust systems: increasing catalytic conversion efficiency, reduction of emissions
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FLOWHEAD (EU FP7)Partner
May-08 AUTOSIM - Duddeck 33
FLOWHEAD (EU FP7)Work Packages
WP 7(Queen Mary)
ManagementDisseminationExploitation
WP 1 (Renault) Integration into the PDP
WP 2 (FE-DESIGN) Optimisation Workflows
WP 3 (Queen Mary) Adjoint Methods
WP 4(TU Denmark) TopologyOptimisation
WP 6(TU Warsaw) Robust Design
WP 5(TU Munich) ShapeOptimisation
Start: Sep 2008
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Quality Assurancefor CAE Car Body Development
• Current PhD project (BMW, TU Berlin, Queen Mary)
• Rising complexity due to international collaborations and outsourcing
• Derive a quality process for CAE (NVH, crash, etc.)
• Please contact me for more discussion
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Future Challenges
1. Topology optimisation for crashworthiness;
2. Shape optimisation with large geometrical changes (re-meshing);
3. Automatic transition between shape and topology optimisation;
4. Combined material and shape optimisation including a stamp-crash coupling;
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Future Challenges (cont.)
5. Optimisation for CFD;
6. Optimisation for fluid-structure interaction;
7. Simplified and surrogate models to reduce numerical effort (current activity: pedestrian protection);
8. Intelligent hybrid optimisation approaches;
9. True MDO algorithms; integration of costs and other disciplines
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Future Challenges (cont.)
10. Combined optimisation and robust design;11. Quality Assurance methods for CAE;12. Database on variation in experiments;13. Integration into current development
processes;14. Higher confidence in simulation results.15. Knowledge on the limits of simulation.16. Design under Lack of Knowledge
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Thank you.
Contact:Fabian DuddeckQueen Mary, Univ. of LondonSchool of Engineering and Materials ScienceMile End Road, London E1 4NS, UK [email protected]: +44-(0)20 7882 3749