Machine learning-assisted probabilistic creep life assessment for high-temperature superheater outlet header considering material uncertainty

作     者：Zhang, Zhen Wang, Xiaowei Li, Zheng Xia, Xianxi Chen, Yefeng Zhang, Tianyu Zhang, Hao Yang, Zheyi Zhang, Xiancheng Gong, Jianming 

作者机构：Nanjing Tech Univ Sch Mech & Power Engn Nanjing 211816 Peoples R China Jiangsu Key Lab Design & Manufacture Extreme Press Nanjing 211816 Peoples R China East China Univ Sci & Technol Key Lab Pressure Syst & Safety Minist Educ Shanghai 200237 Peoples R China Suzhou Nucl Power Res Inst Suzhou Xihuan Rd 1688 Suzhou 215004 Peoples R China Xian Thermal Power Res Inst Co Ltd Xian Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING》 (国际压力容器与管道杂志)

年 卷 期：2024年第209卷

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 

基　　金：National Natural Science Foundation of China [3 52375149, U21B2077] Key support project of the National Key R & D Program of China [2022YFF0605600] China Postdoctoral Science Foundation [2023M741667] 

主　　题：Creep life Reliability assessment Machine learning High -temperature superheater outlet header 

摘      要：The high-temperature superheater outlet header (Outlet Header) in ultra-supercritical (USC) thermal power plants is subjected to high temperatures and pressures, which increases the risk of creep failure. To assess the structural reliability of the Outlet Header, it is necessary to consider the impact of uncertainty factors. Furthermore, the diverse operating conditions make reliability assessment inconvenient. This study evaluates the creep life reliability of the Outlet Header based on material uncertainty and simplifies the assessment process using machine learning methods. Considering the scatter of creep rupture data, the uncertainty of material constants in the Larson-Miller (LM) model is quantified by randomly sampling a specific number of creep rupture life data. Based on the results of uncertainty quantification and finite element analysis, the distribution of the Outlet Header s creep life is obtained to calculate its reliability under design life. Machine learning is employed to assist in the reliability assessment of creep life under different operating conditions of Outlet Header. The results indicate that Artificial Neural Network (ANN) demonstrates good performance in this study, and an assessment diagram based on the ANN has been constructed. This approach provides a practical solution for assessing the reliability of high-temperature components in engineering.