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- Title
An uncertainty representation based sampling method for metamodeling in auto-motive design applications.
- Authors
Yang, Junqi; Zhan, Zhenfei; Zheng, Kai; Chen, Chong; Hu, Jie; Zheng, Ling
- Abstract
Meta-model is frequently employed as the surrogate of high-fidelity finite element models in computer aided engineering. It helps to achieve the trade-off between computational efficiency and predictive accuracy. To improve the predictive capability of meta-models, we developed an uncertainty representation based sampling method to schedule Design of experiment (DOE) for meta-modeling. Several datasets were first generated through a modified Bootstrap method for datasets acquisition, then the influence of the input uncertainty on the output was quantified as weighting factors. The weighting factors were used to integrate the represented distributions into a single one for further sampling. Finally, the sampling results then served as the elements of the DOE matrix to construct meta-models. The proposed method was demonstrated through an analytical case and a real-world vehicle crashworthiness design problem.
- Subjects
COMPUTERS in automotive engineering; COMPUTER-aided design; ENGINEERING software; MATHEMATICAL models of engineering; FINITE element method; STATISTICAL bootstrapping
- Publication
Journal of Mechanical Science & Technology, 2016, Vol 30, Issue 10, p4645
- ISSN
1738-494X
- Publication type
Article
- DOI
10.1007/s12206-016-0935-6