Computational strategy for the crash design analysis using an uncertain computational mechanical model

Abstract

International audienceThe framework of this paper is the robust crash analysis of a motor vehicle. The crash analysis is carried out with an uncertain computational model for which uncertainties are taken into account with the parametric probabilistic approach and for which the stochastic solver is the Monte Carlo method. During the design process, different configurations of the motor vehicle are analyzed. Usual interpolation methods cannot be used to predict if the current configuration is similar or not to one of the previous configurations already analyzed and for which a complete stochastic computation has been carried out. In this paper, we propose a new indicator that allows to decide if the current configuration is similar to one of the previous analyzed configurations while the Monte Carlo simulation is not finished and therefore, to stop the Monte Carlo simulation before the end of computation

Uncertainty Quantification

“…For a general approach devoted to the robust design optimization in computational mechanics, we refer the reader to [31], and for an application devoted to a robust crash design analysis using a high-dimensional nonlinear uncertain computational mechanical model, we refer the reader to [67].…”

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

“…When these works were published, design optimization with stochastic computational model was not still really possible for large scale multiscale computational models. The applications that have been published (see for instance [1,5,11,12,16,22,29] and also [35,54,57,68,70,72,80,99,96]) were devoted to optimization problems under uncertainties for which the computational models had a reasonable number of degrees of freedom, for which the optimizers were based on the use of relatively classical optimization algorithms and/or the introduction of approximations such as surface responses and surrogate models.…”