Time-variant reliability-based design optimization using sequential kriging modeling
Document Type
Article
Publication Date
3-21-2018
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
This paper presents a sequential Kriging modeling approach (SKM) for time-variant reliability-based design optimization (tRBDO) involving stochastic processes. To handle the temporal uncertainty, time-variant limit state functions are transformed into time-independent domain by converting the stochastic processes and time parameter to random variables. Kriging surrogate models are then built and enhanced by a design-driven adaptive sampling scheme to accurately identify potential instantaneous failure events. By generating random realizations of stochastic processes, the time-variant probability of failure is evaluated by the surrogate models in Monte Carlo simulation (MCS). In tRBDO, the first-order score function is employed to estimate the sensitivity of time-variant reliability with respect to design variables. Three case studies are introduced to demonstrate the efficiency and accuracy of the proposed approach.
Publication Title
Structural and Multidisciplinary Optimization
Recommended Citation
Li, M.,
Bai, G.,
&
Wang, Z.
(2018).
Time-variant reliability-based design optimization using sequential kriging modeling.
Structural and Multidisciplinary Optimization,
58(3), 1051-1065.
http://doi.org/10.1007/s00158-018-1951-1
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/4639