航空发动机限寿件高效失效概率算法研究综述

李果; 刘俊博; 周惠敏; 丁水汀

doi:10.13224/j.cnki.jasp.20220299

航空发动机限寿件高效失效概率算法研究综述

doi: 10.13224/j.cnki.jasp.20220299

李果¹,
刘俊博^1,*, ,,
周惠敏¹,
丁水汀²

1.
北京航空航天大学能源与动力工程学院，北京 100191
2.
中国民航大学，天津 300300

详细信息

作者简介:
李果（1983-），男，副教授、博士生导师，博士，主要从事航空发动机安全性与适航研究

通讯作者:
刘俊博（1992-），男，博士生，主要从事航空发动机限寿件安全性机理评估研究。E-mail：liujunbo@buaa.edu.cn

中图分类号: V239
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-02
- 网络出版日期: 2022-09-21

Review on efficient algorithm of failure probability for aero-engine life limited parts

1.
School of Energy and Power Engineering，Beihang University，Beijing 100191，China
2.
Civil Aviation University of China，Tianjin 300300，China

摘要

摘要:
综述了为提高失效概率计算效率的研究成果，包括基于抽样的高效失效概率算法和基于积分的高效失效概率算法。其中，基于抽样的高效失效概率算法在传统蒙特卡洛模拟方法的基础上，通过重要性抽样方法在失效域抽样、最优抽样技术优化分区样本量、分区细化技术减少分区数量，从而减少蒙特卡洛模拟样本量。另外，基于积分的高效失效概率算法通过建立N次飞行循环与初始循环（N = 0）随机变量空间的映射关系，解决了时变失效区域中概率密度函数难以求解的困难。在与蒙特卡洛相对误差小于5% 条件下，积分算法时间成本降低了数十倍。
- 航空发动机 /
- 寿命限制件 /
- 高能转子轮盘 /
- 概率失效风险评估 /
- 高效失效概率算法 /
- 数值积分算法 /
- 蒙特卡洛方法
Abstract:
The research results for improving the efficiency of failure probability calculation, including the efficient sampling-based algorithms and the efficient integration-based algorithms, were summarized. Among them, based on the traditional Monte Carlo simulation method, the importance sampling algorithm generated samples in the failure domain. The optimal sampling technology optimized the zone sample size. The zone refinement technology reduced the number of zones, thus reducing the Monte Carlo simulation sample size. In addition, the direct integration was realized by establishing the mapping relationship of failure domain at N flight cycles and the initial (N = 0) flight cycles based on the probability density theory. When the relative error with Monte Carlo was less than 5%, the calculation time cost was reduced by at least tens of times.
- aero-engine /
- life limited parts /
- high energy rotor disk /
- probabilistic failure risk assessment /
- efficient algorithm of failure probability /
- numerical integration algorithm /
- Monte Carlo method

HTML

图 1 钛合金硬α缺陷和粉末高温合金缺陷^[2,26-27]

Figure 1. Hard-α inclusion in titanium and inclusion in powder superalloy ^[2,26-27]

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图 2 初始缺陷分布曲线^[12]

Figure 2. Initial defect distribution curve^[12]

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图 3 超声检出概率曲线^[7]

Figure 3. Ultrasonic detection of probability curve^[7]

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图 4 蒙特卡洛模拟方法流程图

Figure 4. Flow chart of Monte Carlo simulation

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图 5 积分失效域示意图^[15]

Figure 5. Schematic diagram of failure domain for integral^[15]

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图 6 分区细化过程^[18]

Figure 6. Zone refinement process^[18]

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图 7 数值积分结果与蒙特卡洛模拟对比^[19]

Figure 7. Numerical integration results compared with Monte Carlo simulation^[19]

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图 8 逆向求解裂纹扩展示意图^[20]

Figure 8. Inverse crack growth solution considering multivariate^[20]

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图 9 变量个数对失效概率计算时间的影响^[20]

Figure 9. Influence of number of variables on failure probability computation time^[20]

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图 10 考虑无损检测的积分算法原理示意图^[38]

Figure 10. Principle Schematic of integration algorithm considering nondestructive inspection^[38]

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