Adaptive Kriging reliability-based design optimization of an automotve body structure under crashworthiness constraints Moustapha, Maliki; Sudret, Bruno; Bourinet, Jean-Marc; Guillaume, Benoît
The increasing use of surrogate models has widened the range of application of classical reliability-based design optimization (RBDO) techniques to industrial problems. In this paper, we consider such an approach to the lightweight design of an automotive body structure. Solving this problem while approximating the complex models (nonlinear, noisy and high-dimensional) with a single metamodel would require a very large and non affordable design of experiments (DOE). We thus investigate and propose a methodology of adaptive Kriging based RBDO where an initial DOE is iteratively updated so as to improve the Kriging models only in regions that actually matter. The nested reliability analysis is expressed in terms of quantiles assessment. Two stages of enrichment are performed. The first one seeks to gradually improve the accuracy of the metamodels where the probabilistic constraints are likely to be violated. The second one is embedded in an evolution strategy optimization scheme where, at each iteration, the accuracy of the quantile estimation is improved if necessary. The methodology is applied on an analytical and crashworthiness design problems showing good performance by enhancing accuracy and efficiency with respect to a traditional approach.
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