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Sensitivity analysis of complex engineering systems: Approaches study and their application to vehicle restraint system crash simulation

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  • Qian, Gengjian
  • Massenzio, Michel
  • Brizard, Denis
  • Ichchou, Mohamed

Abstract

Restricted by high calculation cost of single engineering model run and large number of model runs for sampling-based Sensitivity Analysis (SA), qualitative SA are used for parameter study of the Vehicle Restraint System (VRS) and quantitative SA of such models has always been a challenge. Sequential approaches are proposed for SA of complex systems and the SA of a VRS is realized: sampling-based SA methods are discussed; SA of a simple three points dynamic bending test model is realized, the aims are to compare different two-level screening methods and put into practice the sequential SA; crash test FE model of a VRS is created and used for SA; influential uncertain parameters of the VRS are identified qualitatively through screening analyses (SA with Two-level screening and Morris Analysis), and Sobol’ indices are used to quantify the influence of influential parameters with Kriging metamodeling. The uncertain parameters which contribute the most to robustness of the VRS are identified and their influences are quantified by combining screening analyses and Sobol’ Indices.

Suggested Citation

  • Qian, Gengjian & Massenzio, Michel & Brizard, Denis & Ichchou, Mohamed, 2019. "Sensitivity analysis of complex engineering systems: Approaches study and their application to vehicle restraint system crash simulation," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 110-118.
    • Handle: RePEc:eee:reensy:v:187:y:2019:i:c:p:110-118
    DOI: 10.1016/j.ress.2018.07.027
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    1. Marrel, Amandine & Iooss, Bertrand & Laurent, Béatrice & Roustant, Olivier, 2009. "Calculations of Sobol indices for the Gaussian process metamodel," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 742-751.
    2. Ge, Qiao & Ciuffo, Biagio & Menendez, Monica, 2015. "Combining screening and metamodel-based methods: An efficient sequential approach for the sensitivity analysis of model outputs," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 334-344.
    3. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    4. Lamoureux, Benjamin & Mechbal, Nazih & Massé, Jean-Rémi, 2014. "A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 12-26.
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