Journal
FINITE ELEMENTS IN ANALYSIS AND DESIGN
Volume 203, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.finel.2021.103694
Keywords
Crashworthiness; Uncertainty Quantification; Adaptive; Noninstrusive; kPCA; Dimensionality reduction; Metamodeling; Sensitivity analysis
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Funding
- Generalitat de Catalunya [1278 SGR 2017-2019]
- Pla de Doctorats Industrials [2017 DI 058]
- Ministerio de Economia y Empresa and Ministerio de Ciencia, Innovacion y Universidades [DPI2017-85139-C2-2-R]
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The article discusses the uncertainty quantification analysis for nonlinear crash models and the challenges it faces, proposing the use of kernel principal component analysis technique to improve the efficiency of metamodels.
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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