7 research outputs found
Best compromising crashworthiness design of automotive S-rail using TOPSIS and modified NSGAII
No full textMulti-objective cross-sectional shape and size optimization of S-rail using hybrid multi-criteria decision-making method
No full textCrashworthiness optimization of front rail structure using macro element method and evolutionary algorithm
No full textThe role of nuclear medicine in breast cancer detection: A focus on technetium-99 sestamibi scintimammography
No full textComputational strategy for the crash design analysis using an uncertain computational mechanical model
No full textInternational 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
