航空发动机限寿件疲劳可靠度计算新方法

针对目前的航空发动机限寿件（ELLP）疲劳可靠性分析中的小失效概率事件以及其极限状态函数具有较强非线性的特点，提出了一种具有自更新机制的半径外自适应重要抽样（AUMCROAIS）疲劳可靠性分析方法。该方法首先利用蒙特卡罗自适应重要抽样（MCAIS）快速逼近真实设计验算点（MPP）附近，随后以近似设计验算点为中心进行极坐标抽样，并依次构造主动学习函数，对近极限状态函数和抽样半径进行最优选取，从而实现最优抽样半径的更新，通过不断的更新确定出最优抽样半径，加速失效概率计算的收敛。本方法提高了设计验算点的收敛速度同时保证了计算精度，解决了小失效概率事件以及强非线性极限状态函数可靠度计算难题，最后以某型发动机压气机轮盘为对象应用本方法，并与传统的蒙特卡罗仿真（MCS）方法、蒙特卡罗半径外自适应重要抽样法（MCROAIS）和一阶可靠性方法（FORM）进行了对比，验证了本方法的高效率、鲁棒性和仿真精度。

A new method for fatigue reliability calculation of aero-engine limited life parts

Aiming at the small failure probability events and the strong non-linearity of limit state function in the fatigue reliability analysis of aero-engine Limited Life Parts (ELLP), a fatigue reliability analysis method based on the Auto Updating Monte Carlo Radius-Outside Adaptive Importance Sampling (AUMCROAIS) is proposed. In this method, the Monte Carlo Adaptive Importance Sampling (MCAIS) is used to approach the Most Probable Point (MPP) efficiently, then the polar coordinate sampling is employed by taking the approximate MPP as the sampling center. An active learning function is constructed to optimize the near-limit state function and sampling radius, so that the optimal sampling radius can be updated. The optimal sampling radius can be determined through continuous updating, accelerating the convergence of failure probability. This method improves the convergence speed of MPP points and ensures the accuracy of calculation. It solves the problem of reliability calculation of small failure probability events and strong non-linear limit state function. Finally, taking a compressor disk of an engine as an application, and the efficiency, robustness and simulation accuracy of the proposed method are verified by comparing with the traditional Monte Carlo Simulation (MCS) method, the Monte Carlo Radius-Outside Adaptive Importance Sampling (MCROAIS), and First-Order Reliability Method (FORM).