文献类型：期刊文献

英文题名：High-temperature creep-fatigue-oxidation behaviors of P92 steel: Evaluation of life prediction models

作者：Wang, Kang-Kang[1];Wen, Jian-Feng[1];Xia, Xian-Xi[2];Wang, Run-Zi[1,3];Zhang, Guo-Dong[2];Zhang, Xian-Cheng[1];Tu, Shan-Tung[1]

机构：[1]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety MOE, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Suzhou Nucl Power Res Inst, Suzhou, Peoples R China;[3]Tohoku Univ, Grad Sch Engn, Fracture & Reliabil Res Inst, Sendai, Miyagi, Japan

年份：2023

卷号：46

期号：2

起止页码：682

外文期刊名：FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES

收录：;EI(收录号：20224913212033);WOS:【SCI-EXPANDED(收录号:WOS:000883182900001)】；

基金：This research was funded by the National Natural Science Foundation of China (No. 52122506 and No. 51875203), the Higher Education Discipline Innovation Project (111 Project, No. B13020), the Program of Oriental Scholars of Shanghai Universities, and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：creep-fatigue; damage model; life assessment; oxidation; P92 steel

摘要：Damages caused by the effects of cyclic loading (fatigue) and high temperature (creep and oxidation) have been considered critical and need to be appropriately evaluated. A series of strain-controlled fatigue and creep-fatigue tests are performed on P92 at 873 K under oxygen-containing environment. The creep-fatigue life prediction results are summarized using models based on strain-range partition, Manson-Coffin equation and linear damage summation (LDS) rule. Obviously, the models based on the LDS rule show relatively good performance with an error band of +/- 2.5. In view of the adverse effects of oxidation on creep-fatigue endurance, this paper further develops a physically-based oxidation damage equation, which is incorporated into LDS rule for the improvement of life assessment. The predicted and experimental results falling into +/- 1.5 error band proved the accuracy of the proposed oxidation damage equation in the LDS rule. Additionally, model selection criteria are recommended to evaluate the model prediction capabilities.