A probabilistic combined high and low cycle fatigue life prediction framework for the turbine shaft with random geometric parameters

被引:17
|
作者
Bai, Song [1 ]
Li, Yan-Feng [1 ,2 ]
Huang, Hong-Zhong [1 ,2 ]
Ma, Qian [3 ,4 ]
Lu, Ning [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Syst Reliabil & Safety, Chengdu 611731, Sichuan, Peoples R China
[3] Beijing Inst Aerosp Informat, Beijing 100854, Peoples R China
[4] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
Combined cycle fatigue; Life prediction; Highly stressed volume; Geometric parameters; Turbine shaft; CRACK-GROWTH-BEHAVIOR; MODEL; SUPERALLOY; COMPONENTS; STRENGTH; LCF; RELIABILITY; DESIGN; DISCS; HCF;
D O I
10.1016/j.ijfatigue.2022.107218
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Combined high and low cycle fatigue (CCF) is a common failure mode of turbine shaft. This study presents a generic procedure for probabilistic CCF life prediction, and combined with the modified highly stressed volume (HSV) method to improve a prediction framework, which considers the effect of random geometric parameters on fatigue life and establishes the dynamic relationship between key geometric parameters and life distribution. The probabilistic CCF life modeling of turbine shafts with stochastic geometric parameters is presented, whose predicted result is consistent with practical engineering. This research is of great significance for life estimation and prolongation of turbine shaft.
引用
收藏
页数:12
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