CrMoV钢近门槛值区疲劳裂纹扩展评价模型准确性分析  

Accuracy analysis of evaluation models for fatigue crack growth in the near-threshold regime of CrMoV steel

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作  者:尧全恒 朱明亮[1] 轩福贞[1] YAO Quanheng;ZHU Mingiang;XUAN Fuzhen(School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai200237,China)

机构地区:[1]华东理工大学机械与动力工程学院,上海200237

出  处:《压力容器》2024年第3期1-7,40,共8页Pressure Vessel Technology

基  金:国家自然科学基金项目(51922041)。

摘  要:近门槛值区的疲劳裂纹扩展行为及疲劳门槛值的确定对于工程材料与结构的疲劳性能和损伤容限设计具有重要意义。采用25Cr2Ni2MoV钢的疲劳裂纹扩展数据,运用Paris模型、NASGRO模型、Zhu-Xuan模型分别预测疲劳裂纹扩展门槛值,比较各模型的预测误差;进一步将Zhu-Xuan模型与转折点预测模型结合而建立的双线性评价模型,与ASME规范和BS 7910标准中的评价模型,开展相对误差和准确度的比较。结果表明,Zhu-Xuan模型误差在10%以内,准确度最高;双线性评价模型预测结果与试验数据具有较好的一致性、更高的准确度和稳定性,其精度提升程度最高可达28.6%。得出Zhu-Xuan模型及双线性评价模型对CrMoV钢材料的门槛值预测及寿命精准评定具有较好的适用性的结论。The fatigue crack growth behavior in the near-threshold region and the determination of the fatigue threshold value are of great significance for the fatigue performance and damage tolerance design of engineering materials and structures.The fatigue crack growth data of 25Cr2Ni2MoV steel are used to predict the fatigue crack growth threshold using the Paris model,NASGRO model,and Zhu-Xuan model to compare the prediction errors of each model;The bilinear evaluation model developed by combining the Zhu-Xuan model with the transition point prediction model is further developed to carry out a comparison of the relative error and accuracy with the evaluation models in the ASME code and BS 7910 standard.The results show that the Zhu-Xuan model has the highest accuracy with an error of less than 10%,and the bilinear evaluation model predicts better consistency,higher accuracy and stability with the experimental data,with an improvement in accuracy of up to 28.6%.The study shows that the Zhu-Xuan model and the bilinear evaluation model have better applicability to the prediction of the threshold value and the precise evaluation of the life of CrMoV steel.

关 键 词:CRMOV钢 疲劳裂纹扩展 Zhu-Xuan模型 双线性模型 近门槛值区 精度 

分 类 号:TH142.2[一般工业技术—材料科学与工程] TG142.1[机械工程—机械制造及自动化]

 

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