A probabilistic combined high and low cycle fatigue life prediction framework for the turbine shaft with random geometric parameters. (December 2022)

• Record Type: Journal Article
• Authors: Bai, Song; Li, Yan-Feng; Huang, Hong-Zhong; Ma, Qian; Lu, Ning
• Is Part Of: International journal of fatigue. Volume 165(2022)
• Publication Date: 2022-12
• Subjects: Combined cycle fatigue -- Life prediction -- Highly stressed volume -- Geometric parameters -- Turbine shaft; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2022.107218
• Languages: English
• ISSNs: 0142-1123

Abstract

Highlights: CCF loading characteristic is considered in fatigue life prediction of turbine shaft. The model for CCF life prediction contains HCF-LCF interaction damage. Generic procedure-based life prediction considers material and load uncertainties. The effect of random geometric parameters on CCF life is quantified by HSV method. Probabilistic CCF life modelling of a turbine shaft is presented and validated. Abstract: 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.