Abstract
Crash simulation results show both deterministic and stochastic behavior. For optimization in automotive design it is very important to distinguish between effects caused by variation of simulation parameters and effects triggered, for example, by buckling phenomena. We propose novel methods for the exploration of a simulation database featuring non-linear multidimensional interpolation, tolerance prediction, sensitivity analysis, robust multiobjective optimization as well as reliability and causal analysis. The methods are highly optimized for handling bulky data produced by modern crash simulators. The efficiency of these methods is demonstrated for industrially relevant benchmark cases.
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Clees, T., Nikitin, I., Nikitina, L., Thole, CA. (2013). Analysis of Bulky Crash Simulation Results: Deterministic and Stochastic Aspects. In: Pina, N., Kacprzyk, J., Filipe, J. (eds) Simulation and Modeling Methodologies, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34336-0_15
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DOI: https://doi.org/10.1007/978-3-642-34336-0_15
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