From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information

被引：33

作者：

Gray, A. ^{[1
]}

Wimbush, A. ^{[1
]}

de Angelis, M. ^{[1
]}

Hristov, P. O. ^{[1
]}

Calleja, D. ^{[1
]}

Miralles-Dolz, E. ^{[1
]}

Rocchetta, R. ^{[2
]}

机构：

[1] Univ Liverpool, Inst Risk & Uncertainty, Liverpool, Merseyside, England

[2] Tech Univ Eindhoven, Dept Math & Comp Sci, Eindhoven, Netherlands

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2022年 / 165卷

基金：

英国工程与自然科学研究理事会;

关键词：

Bayesian calibration; Probability bounds analysis; Uncertainty propagation; Uncertainty reduction; Epistemic uncertainty; Optimisation under uncertainty; RELIABILITY-BASED DESIGN; SENSITIVITY-ANALYSIS; BHATTACHARYYA DISTANCE; ROBUST RELIABILITY; SUBSET SIMULATION; MODEL; OPTIMIZATION; PROBABILITY; IDENTIFICATION;

D O I：

10.1016/j.ymssp.2021.108210

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application.

引用

页数：39

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