文献类型：期刊文献

英文题名：Physics-based neural network for probabilistic low cycle fatigue and ratcheting assessments of pressurized elbow pipe component

作者：Wang, Xiaoxiao[1];Chen, Haofeng[1,2];Xuan, Fuzhen[2]

机构：[1]Univ Strathclyde, Dept Mech & Aerospace Engn, James Weir Bldg, 75 Montrose St, Glasgow City G1 1XJ, Scotland;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：172

外文期刊名：INTERNATIONAL JOURNAL OF FATIGUE

收录：;EI(收录号：20231113730980);WOS:【SCI-EXPANDED(收录号:WOS:000955667000001)】；

基金：The authors gratefully acknowledge the support from the National Natural Science Foundation of China (51828501 and 52150710540) , the East China University of Science and Technology, and the University of Strathclyde during the course of this work.

语种：英文

外文关键词：Probabilistic analysis; Artificial neural network; Linear Matching Method; Low cycle fatigue; Ratcheting

摘要：Elbow pipe components are frequently subjected to complicated thermo-mechanical load combinations cyclically in nuclear engineering, facing failures related to cyclic plastic responses, including low cycle fatigue (LCF) and ratcheting. To deal with the risk management of important pipelines, the probabilistic LCF and ratcheting as-sessments are indispensable to evaluate the reliability of such components considering the uncertain operating parameters. In this study, the new probabilistic Linear Matching Method (pLMM) framework is proposed to address the probabilistic structural integrity assessment, quantitatively predicting the statistical distribution of LCF life and ratchet limit by the surrogate model with the novel Linear Matching Method-driven neural network (LDNN). With the numerical investigations on the elbow pipe structure presented, the probabilistic assessment boundaries and reliability-centred evaluation diagrams in terms of LCF life and ratchet limit are established respectively, which are beneficial to get rid of the conservativeness of the traditional design schemes with safety factor.