基于稀疏贝叶斯的航空发动机风扇声模态重构

王菲; 李行健; 王亚南; 杜军; 文璧; 乔百杰; 陈雪峰

doi:10.13224/j.cnki.jasp.20250217

基于稀疏贝叶斯的航空发动机风扇声模态重构

doi: 10.13224/j.cnki.jasp.20250217

王菲^1,,
李行健¹,
王亚南¹,
杜军²,
文璧^{1, 3},
乔百杰^1,*, ,,
陈雪峰¹

1.
西安交通大学机械工程学院，西安 710049
2.
中国航发四川燃气涡轮研究院，四川绵阳 621000
3.
太行国家实验室试验测试研究中心，成都 610500

基金项目: 国家自然科学基金（52475130，52305127）

详细信息

作者简介:
王菲（2001-），女，硕士生，主要从事航空发动机气动声学研究。E-mail：dzra123456@stu.xjtu.edu.cn

通讯作者:
乔百杰（1985-），男，教授，博士，主要从事航空发动机健康监测研究。E-mail：qiao1224@xjtu.edu.cn

中图分类号: V231.3
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- 被引次数: 0
出版历程
- 收稿日期: 2025-05-06
- 网络出版日期: 2025-11-12

Sparse Bayesian based reconstruction of acoustic modes for aircraft engine fans

WANG Fei^1
,,
LI Xingjian¹,
WANG Yanan¹,
DU Jun²,
WEN Bi^{1, 3},
QIAO Baijie^{1,*
, ,},
CHEN Xuefeng¹

1.
School of Mechanical Engineering，Xi’an Jiaotong University，Xi’an 710049，China
2.
Sichuan Gas Turbine Establishment，Aero Engine Corporation of China，Mianyang Sichuan 621000，China
3.
Experimental and Testing Research Center，Taihang National Laboratory，Chengdu 610500，China

摘要

摘要:
针对航空发动机风扇管道声模态重构时，均匀环形声阵列所需传感器数量庞大，而传统基于L1范数的压缩感知方法存在幅值低估的问题，研究基于稀疏贝叶斯的航空发动机风扇声模态重构方法，建立稀疏贝叶斯分层先验模型，利用块坐标下降法求解，有效解释并量化测量过程中的不确定性；利用非支配遗传算法优化阵列布局，提高声模态重构精度。开展了某3.5级航空发动机风扇声模态测试试验，结果表明：在相同传声器数目下，稀疏贝叶斯方法的重构平均误差低于L1范数正则化方法；在低速工况下，采用6支传感器最优布局，稀疏贝叶斯方法对周向模态阶数为5的声模态重构误差为0.01 dB；在高速工况下，采用8支传感器最优布局，稀疏贝叶斯方法对周向模态阶数为5和−12的声模态重构误差分别为0.50 dB和0.46 dB。
- 航空发动机风扇 /
- 声模态重构 /
- 稀疏贝叶斯 /
- 非支配遗传算法 /
- 阵列布局优化
Abstract:
To address the large number of sensors required by uniform circular arrays in duct acoustic mode reconstruction for aero-engines, and the amplitude underestimation problem of traditional L1-norm-based compressed sensing methods, a sparse Bayesian approach for fan noise modal reconstruction was proposed. A hierarchical sparse Bayesian prior model was established and solved using a block coordinate descent algorithm, effectively characterizing and quantifying uncertainties in the measurement process. Furthermore, a non-dominated sorting genetic algorithm was employed to optimize array configuration and enhance reconstruction accuracy. Fan noise modal tests were conducted on a 3.5-stage aero-engine. Results showed that, under the same number of microphones, the sparse Bayesian method achieved lower reconstruction error than the L1-norm regularization method. Under low-speed condition, with an optimized layout of 6 sensors, the reconstruction error for circumferential mode order 5 was 0.01 dB. Under high-speed condition, with 8 optimally placed sensors, the reconstruction errors for mode orders 5 and −12 were 0.50 dB and 0.46 dB, respectively. The study demonstrated that the sparse Bayesian method significantly improved the accuracy of duct acoustic mode reconstruction with fewer sensors.
- aero-engine fan /
- acoustic mode reconstruction /
- sparse Bayesian /
- non-dominated sorting genetic algorithm /
- array configuration optimization

HTML

图 1 非支配排序遗传算法流程图

Figure 1. Flowchart of non-dominated sorting genetic algorithm

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图 2 叶片通过频率下各周向位置处的声压幅值

Figure 2. Amplitude of sound pressure at each circumferential position of the blade passing through the frequency

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图 3 安装在0°传声器信号时域、频域图

Figure 3. Installed on 0° microphone signal spectrum signal

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图 4 声模态稀疏重构

Figure 4. Sparse reconstruction of acoustic modes

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图 5 声模态重构误差

Figure 5. Acoustic modal reconstruction error

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图 6 最优布局传感器示意图

Figure 6. Schematic diagram of optimal layout sensor

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图 7 布局优化后模态识别结果

Figure 7. Modal recognition results after layout optimization

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图 8 声学测试试验系统

Figure 8. Acoustic testing system

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图 9 风扇测试试验台简图

Figure 9. Schematic diagram of compressor test bench

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图 10 传声器周向布置分布图

Figure 10. Distribution diagram of circumferential arrangement of microphones

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图 11 风扇试验台稳态试验历程

Figure 11. Steady state test process of fan test bench

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图 12 转速为6500 r/min工况1号传声器频谱图

Figure 12. Spectrum diagram of microphone 1 at Ω=6500 r/min operating condition

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图 13 转速为8588 r/min工况1号传声器频谱图

Figure 13. Spectrum diagram of microphone 1 at Ω=8588 r/min operating condition

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图 14 转速为6500 r/min下风扇单音噪声模态分解

Figure 14. Modal decomposition of single tone noise from a compressor under Ω=6500 r/min

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图 15 转速为8588 r/min风扇单音噪声声模态分解

Figure 15. Modal decomposition of single tone noise from a compressor under Ω=8588 r/min

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图 16 转速为6500 r/min声模态重构图

Figure 16. Acoustic modal reconstruction diagram under Ω=6500 r/min

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图 17 转速为6500 r/min蒙特卡洛试验绝对误差

Figure 17. Absolute error of Monte Carlo experiment under Ω=6500 r/min

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图 18 转速为6500 r/min工况下最佳布局下声模态重构

Figure 18. Acoustic mode reconstruction at Ω=6500 r/min with optimal array configuration

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图 19 转速为8588 r/min，m = 5蒙特卡洛仿真结果

Figure 19. Monte Carlo simulation results of m=5 under Ω=8588 r/min

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图 20 转速为8588 r/min，m = −12蒙特卡洛仿真结果

Figure 20. Monte Carlo simulation results of m=−12 under Ω=8588 r/min

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图 21 高速工况下声模态重构图

Figure 21. Acoustic modal reconstruction diagram under supersonic conditions

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图 22 高速工况最佳布局图

Figure 22. Optimal layout diagram for supersonic operating conditions

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图 23 高速工况最佳布局下声模态重构

Figure 23. Acoustic modal reconstruction under optimal layout of supersonic operating conditions

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表 1 风扇试验器叶片数量

Table 1. Fan tester blade quantity

叶片种类个数

导流叶片 17
一级转子叶片 22
一级静子叶片 15

下载: 导出CSV

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