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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 79
Edited by: B.H.V. Topping and C.A. Mota Soares
Paper 114

A New Method of Automatic Concept Model Generation for Optimisation and Robust Design of Passenger Cars

J. Hilmann+, M. Paas*, A. Hänschke* and T. Vietor+

+Pre Program and Concepts Engineering
*Body Core Engineering - Safety
Ford Werke AG, Cologne, Germany

Full Bibliographic Reference for this paper
, "A New Method of Automatic Concept Model Generation for Optimisation and Robust Design of Passenger Cars", in B.H.V. Topping, C.A. Mota Soares, (Editors), "Proceedings of the Seventh International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 114, 2004. doi:10.4203/ccp.79.114
Keywords: vehicle engineering, structural optimisation, SFE Concept, genetic algorithms, finite element method, parametric modelling, sensitivity analysis.

A new fully automated method of structural optimisation for the body in white structure is presented. The iterations in the optimisation loop comprise the following steps: fully parameterised design creation, automated meshing and model assembly, parallel computation and evaluation. For this purpose several free and commercially available software applications are combined, including: SFE Concept, Hypermesh, Perl, Matlab, and Radioss. SFE concept delivers high quality meshes based on parameterised models thus providing high flexibility for investigating design alternatives. The optimisation is conducted using Genetic Algorithms (GA), which apply the principle of survival of the fittest to produce optimal solutions to problems. Genetic Algorithms offer distinct benefits like: problem solving for large solution spaces, no limitations on maximum number of design variables, avoidance of premature convergence to local optima, no limitations on continuity or differentiability and numerical efficiency through parallel computing.

The viability of the method is demonstrated for a vehicle component model of a front bumper system utilizing both material and geometry related properties as design variables. The importance of achieving optimal system performance in conjunction with robustness is emphasised. System noise due to statistical variations in the design parameters is addressed. Combined application of optimisation methods and sensitivity analysis based on Monte Carlo simulations enables optimal numerical efficiency by reducing total number of design variables and selecting initial population of fit members. Parallel computing gives the opportunity to extend the size of the models and to include additional components like the engine. A proper set of optimisation strategies and settings are prerequisite to avoid unnecessary calculations. Because of its short run times the bumper/crash can/side rail system is very suited as a demonstrator model for evaluating different optimisation strategies and parameter settings.

Figure 1: Sample of four population members after 60ms for generation 1 (left) and generation 16 (right).

Hänschke, A., Paas, M., Hilmann, J. "The Use of Simplified Geometrical and Mechanical Surrogate Models for Body and Total Vehicle Optimisation", VDI Conference in Hamburg Entwicklungen im Karosseriebau 2004
Hilmann, J., Hänschke, A. "Use of simplified models for the improved vehicle lay out with regards the vehicle Safety", 10. Aachen Colloquium, Automobile and Engine Technology 2001
Paas, M., Ippen, H., Schilling, R. "Structural Component Optimisation and Material Model Identification based on Generic Algorithms", VDI - 11. Internationaler Kongreß "Numerical analysis and simulation in Vehicle engineering", 01.-02. Oktober 2002, Würzburg
Hoppe, A., Zimmer, H., Widmann, U., Papke, L., Arzul, C., Holzheuer, C., Unruh, R. "Multidisziplinäre Optimierung parametrischer Fahrzeugkomponenten", VDI-Bericht 18xx, 2004
Goldberg, D. "Genetic Algorithms in Search, Optimisation, and Machine Learning". Addison-Wesley, 1989

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