文献类型：期刊文献

英文题名：Physics-Based Probabilistic Assessment of Creep-Fatigue Failure for Pressurized Components by Extended Direct Steady Cycle Analysis-Driven Neural Network

作者：Wang, Xiaoxiao[1]; Ma, Zhiyuan[1]; Yang, Jie[2]; Chen, Haofeng[3]; Xuan, Fuzhen[3]

机构：[1] Department of Mechanical & Aerospace Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street, Glasgow, G1 1XJ, United Kingdom; [2] Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; [3] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2022

外文期刊名：SSRN

收录：EI(收录号：20220440809)

语种：英文

外文关键词：Creep - Damage detection - Fatigue of materials - Fracture mechanics - Reliability analysis

摘要：To achieve a high-reliability design with a feasible balance between accuracy and efficiency, the physics-based probabilistic assessment for creep-fatigue failure is proposed under the probabilistic Linear Matching Method (pLMM) framework. At the physical level, the structural failure mechanism is reflected in the prepared training database, which is generated by the direct method, extended Direct Steady Cycle Analysis (eDSCA) procedures. And to efficiently express the relationship between design parameters and structural responses implicitly, the extended DSCA-driven neural network (EDDNN) is built with the superior fitting quality of damage and lifetime. With the benchmarks of pressurized structures provided, the applicability of the proposed probabilistic analysis approach in solving practical problems is demonstrated, where the reliability-based evaluation diagram is established according to different requirements. Furthermore, a novel data classification scheme is proposed to deal with the randomness in creep damage-dominated probabilistic assessment. ? 2022, The Authors. All rights reserved.