A sequential sampling strategy to improve reliability-based design optimization with implicit constraint functions

Xiaotian Zhuang, Rong Pan

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

Reliability-based design optimization (RBDO) has a probabilistic constraint that is used for evaluating the reliability or safety of the system. In modern engineering design, this task is often performed by a computer simulation tool such as finite element method (FEM). This type of computer simulation or computer experiment can be treated a black box, as its analytical function is implicit. This paper presents an efficient sampling strategy on learning the probabilistic constraint function under the design optimization framework. The method is a sequential experimentation around the approximate most probable point (MPP) at each step of optimization process. Our method is compared with the methods of MPP-based sampling, lifted surrogate function, and nonsequential random sampling. We demonstrate it through examples.

Original languageEnglish (US)
Article number021002
JournalJournal of Mechanical Design, Transactions of the ASME
Volume134
Issue number2
DOIs
StatePublished - Feb 16 2012

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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