A new local update-based method for reliability-based design optimization

• Published in: Engineering with computers Vol. 37; no. 4; pp. 3591 - 3603
• Main Authors: Zhang, Jinhao, Xiao, Mi, Gao, Liang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2021; Springer Nature B.V
• Subjects: Approximation; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Computer Science; Computer-Aided Engineering (CAD; Control; Design optimization; Math. Applications in Chemistry; Mathematical analysis; Mathematical and Computational Engineering; Metamodels; Original Article; Reliability analysis; Systems Theory; Most probable target point; Reliability-based design optimization; Implicit objective and constraint functions; Kriging metamodel; Local update
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-020-01019-6

Reliability-based design optimization (RBDO) is often associated with computationally expensive computer simulations in practical engineering applications, in which both objective and constraint functions may be implicit and time-consuming to evaluate. To improve the efficiency of RBDO, metamodels are often employed to substitute the implicit and expensive functions. In this paper, a new local update-based method is proposed for RBDO under the approximation of both objective and constraint functions. In this method, Kriging metamodels are employed to replace both objective and constraint functions, and sequential optimization and reliability assessment (SORA) is adopted to perform RBDO. Meanwhile, a local update method is developed for the approximation of objective and constraint functions, termed as LUOC. In LUOC, two local update strategies are proposed. For the constraint functions, one local update strategy judges the active constraints and then sequentially refines their Kriging metamodels in the local regions around the most probable target points obtained by SORA. For the objective function, the other local update strategy based on expected improvement is developed to refine the Kriging metamodel in the local region around the SORA solution with considering the Kriging prediction variance. Three examples including a battery design application are presented to test the accuracy and efficiency of the proposed method for RBDO.