Probabilistic Low-Cycle Fatigue Analysis of Gas Turbine Components under Varying Operating Conditions
Dissertation Title: Probabilistic Low-Cycle Fatigue Analysis of Gas Turbine Components under Varying Operating Conditions
Speaker: Zixi Han, Ph.D. candidate at UM-SJTU Joint Institute
Time: 2:00 p.m., October 25, 2022 ( Beijing Time)
The design of gas turbine components involves many criteria, among which the low-cycle fatigue (LCF) life is a very common and important requirement. In real applications, gas turbines usually work under different operating cycles, resulting in the variation of the LCF life of gas turbine components. Therefore, this dissertation is dedicated to develop efficient LCF life estimation approaches for gas turbine components under varying operating conditions. Firstly, the variation of the operating condition as well as its potential influence on the LCF life of gas turbine components is confirmed by several examples in the historical operational data. Then, an approach is presented to estimate the probability distribution of the LCF life under random operating conditions using the sequential convolution of the single-cycle damage distribution. Next, to consider the scatter of the fatigue life, a novel probabilistic LCF model, ProbLCF-VA model, is developed, which gives the form of probability distribution of the LCF life, both for the situation with given load histories and for the one with load spectra, respectively. Finally, the ProbLCF-VA model is validated using multiple sets of fatigue test data.
Zixi Han is a Ph.D. student in Mechanical Engineering at the Shanghai Jiao Tong University, supervised by Professor Mian Li. He received his B.E. degree in Mechanical Engineering at Tongji University in 2016. His research interest is the design and analysis of gas turbine components using historical operational data based on various probabilistic approaches.