Global sensitivity analysis via multi-fidelity polynomial chaos expansion

Pramudita Satria Palar, Lavi Rizki Zuhal, Koji Shimoyama, Takeshi Tsuchiya

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

The presence of uncertainties is inevitable in engineering design and analysis, where failure in understanding their effects might lead to the structural or functional failure of the systems. The role of global sensitivity analysis in this aspect is to quantify and rank the effects of input random variables and their combinations to the variance of the random output. In problems where the use of expensive computer simulations is required, metamodels are widely used to speed up the process of global sensitivity analysis. In this paper, a multi-fidelity framework for global sensitivity analysis using polynomial chaos expansion (PCE) is presented. The goal is to accelerate the computation of Sobol sensitivity indices when the deterministic simulation is expensive and simulations with multiple levels of fidelity are available. This is especially useful in cases where a partial differential equation solver computer code is utilized to solve engineering problems. The multi-fidelity PCE is constructed by combining the low-fidelity and correction PCE. Following this step, the Sobol indices are computed using this combined PCE. The PCE coefficients for both low-fidelity and correction PCE are computed with spectral projection technique and sparse grid integration. In order to demonstrate the capability of the proposed method for sensitivity analysis, several simulations are conducted. On the aerodynamic example, the multi-fidelity approach is able to obtain an accurate value of Sobol indices with 36.66% computational cost compared to the standard single-fidelity PCE for a nearly similar accuracy.

Original languageEnglish
Pages (from-to)175-190
Number of pages16
JournalReliability Engineering and System Safety
Volume170
DOIs
Publication statusPublished - 2018 Feb

Keywords

  • Global sensitivity analysis
  • Multi-fidelity
  • Polynomial chaos expansion
  • Sobol indices

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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