长沙理工大学学报（自然科学版）2024，Vol.21Issue(4)：44-67,24.DOI:10.19951/j.cnki.1672-9331.20231225002
氢环境下金属材料的疲劳寿命预测:从裂纹的角度
Fatigue life prediction of metal materials in hydrogen environment based on cracks
摘要
Abstract
With the rapid development of hydrogen energy technology,metal equipment is increasingly used in hydrogen environments.However,the hydrogen embrittlement effect will significantly weaken the fatigue performance of metal materials,posing hidden dangers to the safety of related equipment.Therefore,it is of great significance to accurately predict the fatigue life of metal materials in a hydrogen environment.This paper systematically analyzed the fatigue crack growth behavior of metal materials in a hydrogen environment and summarized the effects of various parameters on the fatigue crack growth rate under the hydrogen embrittlement effect.At the same time,the research on fatigue properties of metal materials in a hydrogen environment and the application of fatigue life prediction methods were investigated.The fatigue crack growth rate of metal materials in hydrogen environment can be used as an input to calculate the fatigue life of materials,but research has found that the fatigue crack growth rate is affected by a variety of parameters.Although the method based on fracture mechanics is commonly used in the fatigue crack growth stage and serves as a commonly used theory for fatigue life prediction in a hydrogen environment,its solution efficiency needs to be improved.With its efficient and accurate prediction performance,machine learning is widely used in the life prediction of various fatigue problems.However,it is still less applied in the field of fatigue life prediction of metal materials in a hydrogen environment.If relevant data enhancement methods can be used to expand fatigue life data in hydrogen environments,machine learning-based methods can be used for life prediction,which may significantly improve the efficiency of fatigue life prediction of metal materials in hydrogen environments.关键词
氢环境/疲劳性能/裂纹扩展/寿命预测/机器学习Key words
hydrogen environment/fatigue performance/crack growth/life prediction/machine learning分类
交通工程引用本文复制引用
聂鹏,杨世源,郭永强,王永金,潘立栋,张家铭,孟德彪..氢环境下金属材料的疲劳寿命预测:从裂纹的角度[J].长沙理工大学学报（自然科学版）,2024,21(4):44-67,24.基金项目
广东省基础与应用基础研究基金资助项目(2022A1515240010) （2022A1515240010）
中央高校基本科研业务费专项资金资助项目(FRF-EYIT-23-08) Project(2022A1515240010)supported by the Guangdong Basic and Applied Basic Research Foundation （FRF-EYIT-23-08）
Project(FRF-EYIT-23-08)supported by Fundamental Research Funds for the Central Universities （FRF-EYIT-23-08）
