Bayesian reliability-based design optimization using eigenvector dimension reduction (EDR) method

Byeng D. Youn, Pingfeng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

In practical engineering design, most data sets for system uncertainties are insufficiently sampled from unknown statistical distributions, known as epistemic uncertainty. Existing methods in uncertainty-based design optimization have difficulty in handling both aleatory and epistemic uncertainties. To tackle design problems engaging both epistemic and aleatory uncertainties, reliability-based design optimization (RBDO) is integrated with Bayes theorem. It is referred to as Bayesian RBDO. However, Bayesian RBDO becomes extremely expensive when employing the first- or second-order reliability method (FORM/SORM) for reliability predictions. Thus, this paper proposes development of Bayesian RBDO methodology and its integration to a numerical solver, the eigenvector dimension reduction (EDR) method, for Bayesian reliability analysis. The EDR method takes a sensitivity-free approach for reliability analysis so that it is very efficient and accurate compared with other reliability methods such as FORM/SORM. Efficiency and accuracy of the Bayesian RBDO process are substantially improved after this integration.

Original languageEnglish (US)
Pages (from-to)107-123
Number of pages17
JournalStructural and Multidisciplinary Optimization
Volume36
Issue number2
DOIs
StatePublished - Aug 2008
Externally publishedYes

Keywords

  • Bayesian
  • Eigenvector dimension reduction
  • Epistemic
  • RBDO
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization

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