Probabilistic LCF life assessment for turbine discs with DC strategy-based wavelet neural network regression

Abstract

Low cycle fatigue (LCF) seriously affects the performance and reliability of turbomachinery like aeroengine. Probabilistic LCF assessment can effectively quantify risk and improve components reliability. To improve the accuracy and efficiency of probabilistic LCF life assessment, a distributed collaborative (DC)-wavelet neural network regression (WNNR) (called as DC-WNNR) surrogate model is developed by proposing Bayesian regularization-Quasi Newton (BR-QN) error control technique. The mathematical model of DC-WNNR is structured and the corresponding probabilistic framework of LCF life assessment is introduced. The probabilistic LCF life assessment for turbine discs is regarded as one case to evaluate the proposed method with respect to various uncertainties such as material property, load fluctuation and model variability. The analysis results reveal that the reliability-based LCF life of a turbine disc under reliability 99.87% is 6 058 cycles; fatigue strength coefficient σf′ and strain range Δɛt play a leading role on the fatigue life failure since their effect probabilities of 45% and 36%, respectively. The comparison of methods (Monte Carlo method, WNNR, DC response surface method and DC-WNNR) shows that the DC-WNNR holds high efficiency and accuracy for the probabilistic LCF assessment. The present efforts offer an effective way for predicting and evaluating structural LCF failure from a probabilistic perspective.