Nonintrusive uncertainty quantification for automotive crash problems with VPS/Pamcrash
Visualitza/Obre
10.1016/j.finel.2021.103556
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/356919
Tipus de documentArticle
Data publicació2021-10-01
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex systems, enhancing reliability of engineering simulations. In crashworthiness, having an accurate assessment of the behavior of the model uncertainty allows reducing the number of prototypes and associated costs. Carrying out UQ in this framework is especially challenging because it requires highly expensive simulations. In this context, surrogate models (metamodels) allow drastically reducing the computational cost of Monte Carlo process. Different techniques to describe the metamodel are considered, Ordinary Kriging, Polynomial Response Surfaces and a novel strategy (based on Proper Generalized Decomposition) denoted by Separated Response Surface (SRS). A large number of uncertain input parameters may jeopardize the efficiency of the metamodels. Thus, previous to define a metamodel, kernel Principal Component Analysis (kPCA) is found to be effective to simplify the model outcome description. A benchmark crash test is used to show the efficiency of combining metamodels with kPCA.
Descripció
© 2021 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
CitacióRocas, M. [et al.]. Nonintrusive uncertainty quantification for automotive crash problems with VPS/Pamcrash. "Finite elements in analysis and design", 1 Octubre 2021, vol. 193, p. 103556:1-103556:14.
ISSN0168-874X
Versió de l'editorhttps://www.sciencedirect.com/science/article/abs/pii/S0168874X21000408
Altres identificadorshttps://arxiv.org/pdf/2102.07673.pdf
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2102.07673.pdf | 2,412Mb | Visualitza/Obre |