Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 46, Issue 2, Pages 159-170Publisher
SPRINGER
DOI: 10.1007/s00158-012-0760-1
Keywords
Surrogate model; Bayesian metric; Data uncertainty; Crashworthiness design optimization; Sample size; Design of experiment (DOE); Uniform latin hypercube sampling (ULHS)
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Funding
- National Natural Science Foundation of China [50875146]
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Surrogate model or response surface based design optimization has been widely adopted as a common process in automotive industry, as large-scale, high fidelity models are often required. However, most surrogate models are built by using a limited number of design points without considering data uncertainty. In addition, the selection of surrogate model in the literature is often arbitrary. This paper presents a Bayesian metric to complement root mean square error for selecting the best surrogate model among several candidates in a library under data uncertainty. A strategy for automatically selecting the best surrogate model and determining a reasonable sample size was proposed for design optimization of large-scale complex problems. Lastly, a vehicle example with full-frontal and offset-frontal impacts was presented to demonstrate the proposed methodology.
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