Dynamic reliability and variance-based global sensitivity analysis of pipes conveying fluid with both random and convex variables. Issue 4 (31st August 2020)

• Record Type: Journal Article
• Authors: Chen, Bingqian; Wang, Anqiang; Guo, Qing; Dai, Jiayin; Liu, Yongshou
• Is Part Of: Engineering computations. Volume 38:Issue 4(2021)
• Page Start: 1789
• Page End: 1806
• Publication Date: 2020-08-31
• Subjects: Pipes conveying fluid -- Convex model -- Dynamic reliability -- Active learning Kriging model -- Variance-based global sensitivity analysis; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-06-2020-0299
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: This paper aims to solve the problem that pipes conveying fluid are faced with severe reliability failures under the complicated working environment. Design/methodology/approach: This paper proposes a dynamic reliability and variance-based global sensitivity analysis (GSA) strategy with non-probabilistic convex model for pipes conveying fluid based on the first passage principle failure mechanism. To illustrate the influence of input uncertainty on output uncertainty of non-probability, the main index and the total index of variance-based GSA analysis are used. Furthermore, considering the efficiency of traditional simulation method, an active learning Kriging surrogate model is introduced to estimate the dynamic reliability and GSA indices of the structure system under random vibration. Findings: The variance-based GSA analysis can measure the effect of input variables of convex model on the dynamic reliability, which provides useful reference and guidance for the design and optimization of pipes conveying fluid. For designers, the rankings and values of main and total indices have essential guiding role in engineering practice. Originality/value: The effectiveness of the proposed method to calculate the dynamic reliability and sensitivity of pipes conveying fluid while ensuring the calculation accuracy and efficiency in the meantime.