Adaptive surrogates of crash worthiness models for multi-purpose engineering analyses accounting for uncertainty

Rocas Marc; Garcia-Gonzalez Alberto; Larrayoz XabierDiez Pedro

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Adaptive surrogates of crash worthiness models for multi-purpose engineering analyses accounting for uncertainty

机译：Adaptive surrogates of crash worthiness models for multi-purpose engineering analyses accounting for uncertainty

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摘要

Uncertainty Quantification (UQ) is a booming discipline for complex computational models based on the analysis of robustness, reliability and credibility. UQ analysis for nonlinear crash models with high dimensional outputs presents important challenges. In crashworthiness, nonlinear structural behaviours with multiple hidden modes require expensive models (18 h for a single run). Surrogate models (metamodels) allow substituting the full order model, introducing a response surface for a reduced training set of numerical experiments. Moreover, uncertain input and large number of degrees of freedom result in high dimensional problems, which derives to a bottle neck that blocks the computational efficiency of the metamodels. Kernel Principal Component Analysis (kPCA) is a multidimensionality reduction technique for non-linear problems, with the advantage of capturing the most relevant information from the response and improving the efficiency of the metamodel. Aiming to compute the minimum number of samples with the full order model. The proposed methodology is tested with a practical industrial problem that arises from the automotive industry.

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来源
《Finite elements in analysis & design》 |2022年第6期|103694.1-103694.11|共11页
作者
Rocas Marc; Garcia-Gonzalez Alberto; Larrayoz XabierDiez Pedro;
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作者单位

Univ Politecn Cataluna, Lab Calcul Numer, ETS Ingn Caminos, BarcelonaTech, Barcelona, Spain|SEAT, Barcelona, Spain;

Univ Politecn Cataluna, Lab Calcul Numer, ETS Ingn Caminos, BarcelonaTech, Barcelona, Spain;

SEAT, Barcelona, SpainUniv Politecn Cataluna, Lab Calcul Numer, ETS Ingn Caminos, BarcelonaTech, Barcelona, Spain|CIMNE, Int Ctr Numer Methods Engn, Barcelona, Spain;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
Crashworthiness; Uncertainty Quantification; Adaptive; Noninstrusive; kPCA; Dimensionality reduction; Metamodeling; Sensitivity analysis;

Adaptive surrogates of crash worthiness models for multi-purpose engineering analyses accounting for uncertainty

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