Open Access
 Issue JNWPU Volume 39, Number 6, December 2021 1312 - 1319 https://doi.org/10.1051/jnwpu/20213961312 21 March 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## 1 涡轮盘低循环疲劳寿命可靠性分析

### 1.1 涡轮盘载荷简化分析

 图13种工作状态对应的应力应变响应

## 3 基于AK-MC的模糊失效概率及灵敏度分析方法

### 3.1 基于AK-MC的模糊失效概率分析方法

Kriging模型是通过一组真实的输入输出样本建立的, 以此来对待测点处的响应进行预测[16]。通常Kriging的预测值服从正态分布, 其中表示预测值, 表示预测方差。为了改善Kriging模型预测精度, 许多学者[17-19]提出利用学习函数来自适应更新Kriging模型。为了处理失效概率估计问题, Echard等[20]提出了U学习函数, 可以表示为

(17) 式为同时考虑k个失效面的U学习函数, 代表估计值到一系列失效面的距离与估计值标准差的比值。因此, 对于Kriging模型更新可以采用使U(x)最小的加点准则

Kriging模型更新完成后, 可以利用该模型对涡轮盘的响应进行估计, 进而求解涡轮盘的模糊失效概率为

## 4 分析及讨论

### 4.2 涡轮盘灵敏度分析

 图2全局灵敏度指标对比图

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## All Figures

 图13种工作状态对应的应力应变响应 In the text
 图2全局灵敏度指标对比图 In the text

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