Uncertainty quantification in structural dynamic analysis using two-level Gaussian processes and Bayesian inference

K. Zhou, J. Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

A probabilistic framework for efficient uncertainty quantification in structural dynamic analysis is presented. This framework is built upon the combination of two-level Gaussian processes emulator and Bayesian inference technique. The underlying idea is to employ the two-level Gaussian processes emulator to integrate together small amount of high-fidelity data from full-scale finite element analysis and large amount of low-fidelity data from order-reduced analysis to improve the response variation prediction. As component mode synthesis (CMS) is adopted in order-reduced modeling, we then utilize the improved response variation prediction on modal characteristics to update the CMS model to facilitate the efficient probabilistic analysis of any responses of concern. The effectiveness of this framework is demonstrated through systematic case studies.

Original languageEnglish
Pages (from-to)95-115
Number of pages21
JournalJournal of Sound and Vibration
Volume412
DOIs
Publication statusPublished - 6 Jan 2018
Externally publishedYes

Keywords

  • Bayesian inference
  • Component mode synthesis (CMS)
  • Model updating
  • Order-reduced modeling
  • Two-level Gaussian processes emulator
  • Uncertainty quantification

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Acoustics and Ultrasonics
  • Mechanical Engineering

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