Surrogate model uncertainty quantification for reliability-based design optimization
Document Type
Article
Publication Date
12-2019
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
Surrogate models have been widely employed as approximations of expensive physics-based simulations to alleviate the computational burden in reliability-based design optimization. Ignoring the surrogate model uncertainty due to the lack of training samples will lead to untrustworthy designs in product development. This paper addresses the surrogate model uncertainty in reliability analysis using the equivalent reliability index (ERI) and further develops a new smooth sensitivity analysis approach to facilitate the surrogate model-based product design process. By using the Gaussian process (GP) modeling technique, a Gaussian mixture model (GMM) is constructed for reliability analysis using Monte Carlo simulations. To propagate both input variations and surrogate model uncertainty, the probability of failure is approximated by calculating the equivalent reliability index using the first and second statistical moments of the GMM. The sensitivity of ERI with respect to design variables is analytically derived based on the GP predictions. Three case studies are used to demonstrate the effectiveness and robustness of the proposed approach.
Publication Title
Reliability Engineering & System Safety
Recommended Citation
Li, M.,
&
Wang, Z.
(2019).
Surrogate model uncertainty quantification for reliability-based design optimization.
Reliability Engineering & System Safety,
192.
http://doi.org/10.1016/j.ress.2019.03.039
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1493