Fatigue reliability and sensitivity analysis of turbine disk with fuzzy failure status

具有模糊失效状态的涡轮盘疲劳可靠性及灵敏度分析

¹ School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an 710072, China
² Aircraft Reliability Institute, Northwestern Polytechnical University, Xi'an 710072, China

Received: 23 January 2021

Abstract

Low-cycle fatigue is typical failure mode of aero-engine turbine disk, traditional reliability analysis method based on the binary state assumption has certain limitations for turbine disk reliability evaluation, because it doesn't consider the change of damage strength parameter caused by loading sequences and the enhanced damage by small load. On the basis of fatigue reliability analysis of the turbine disk, this paper considers the fuzzy state assumption of turbine disk, then select the membership function and indicate fuzzy failure probability of turbine disk, which can be transformed into a series of conventional failure probability by Gaussian quadrature. An active learning Kriging model is used to orderly calculate the failure probability corresponding to different limit state functions and the fuzzy failure probability of turbine disk. A global sensitivity index based on fuzzy failure probability is established to analyze the influence of input variables on the fuzzy failure probability, which is helpful to the reliability design and structural optimization of the turbine disk.

摘要

低循环疲劳是航空发动机涡轮盘的典型失效模式，传统的基于二元状态假设的可靠性分析没有考虑载荷加载顺序以及小载荷强化损伤等因素导致的损伤极限变化，因此有一定的局限性。在涡轮盘疲劳可靠性分析基础上再考虑失效状态的模糊性，选定隶属函数来表示涡轮盘的模糊失效概率，再利用高斯公式将模糊失效概率转为一系列常规失效概率。建立一个自适应学习的Kriging模型来计算不同失效面对应的失效概率，继而求解涡轮盘的模糊失效概率。建立基于模糊失效概率的全局灵敏度指标，分析输入变量对模糊失效概率的影响，分析结果有助于涡轮盘的可靠性设计与优化。