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Materials (Basel). 2019 May 10;12(9). pii: E1545. doi: 10.3390/ma12091545.

Reliability-Based Low Fatigue Life Analysis of Turbine Blisk with Generalized Regression Extreme Neural Network Method.

Author information

1
School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China. zhangchunyi@hrbust.edu.cn.
2
School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China. wjs19931208@163.com.
3
School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China. jinghuizhe@163.com.
4
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China. cwfei@fudan.edu.cn.
5
School of Computer Science and Technology, Beihang University, Beijing 10191, China. tangwenzhong@buaa.edu.cn.

Abstract

Turbine blisk low cycle fatigue (LCF) is affected by various factors such as heat load, structural load, operation parameters and material parameters; it seriously influences the reliability and performance of the blisk and aeroengine. To study the influence of thermal-structural coupling on the reliability of blisk LCF life, the generalized regression extreme neural network (GRENN) method was proposed by integrating the basic thoughts of generalized regression neural network (GRNN) and the extreme response surface method (ERSM). The mathematical model of the developed GRENN method was first established in respect of the LCF life model and the ERSM model. The method and procedure for reliability and sensitivity analysis based on the GRENN model were discussed. Next, the reliability and sensitivity analyses of blisk LCF life were performed utilizing the GRENN method under a thermal-structural interaction by regarding the randomness of gas temperature, rotation speed, material parameters, LCF performance parameters and the minimum fatigue life point of the objective of study. The analytical results reveal that the reliability degree was 0.99848 and the fatigue life is 9419 cycles for blisk LCF life when the allowable value is 6000 cycles so that the blisk has some life margin relative to 4500 cycles in the deterministic analysis. In comparison with ERSM, the computing time and precision of the proposed GRENN under 10,000 simulations is 1.311 s and 99.95%. This is improved by 15.18% in computational efficiency and 1.39% in accuracy, respectively. Moreover, high efficiency and high precision of the developed GRENN become more obvious with the increasing number of simulations. In light of the sensitivity analysis, the fatigue ductility index and temperature are the key factors of determining blisk LCF life because their effect probabilities reach 41% and 26%, respectively. Material density, rotor speed, the fatigue ductility coefficient, the fatigue strength coefficient and the fatigue ductility index are also significant parameters for LCF life. Poisson's ratio and elastic modulus of materials have little effect. The efforts of this paper validate the feasibility and validity of GRENN in the reliability analysis of blisk LCF life and give the influence degrees of various random parameters on blisk LCF life, which are promising to provide useful insights for the probabilistic optimization of turbine blisk LCF life.

KEYWORDS:

extremum response surface method; generalized regression neural network; low cycle fatigue life; reliability analysis; turbine blisk

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