基于深度学习的压气机叶型气动特性预测

杜周; 徐全勇; 宋振寿; 王晗丁; 马玉林

doi:10.13224/j.cnki.jasp.20210741

基于深度学习的压气机叶型气动特性预测

doi: 10.13224/j.cnki.jasp.20210741

杜周^{1, 2,},
徐全勇^2,*, ,,
宋振寿³,
王晗丁³,
马玉林¹

1.
辽宁工程技术大学力学与工程学院，辽宁阜新 123000
2.
清华大学航空发动机研究院，北京 100084
3.
西安电子科技大学人工智能学院，西安 710071

基金项目: 国家科技重大专项(J2019-Ⅴ-0001-0092，J2019-Ⅴ-0013-0108）

详细信息

作者简介:
杜周（1996-），男，硕士生，主要从事流体仿真与人工智能相结合方面的研究。E-mail：321115728@qq.com

通讯作者:
徐全勇（1980-），男，副教授，博士，主要从事航空发动机气动热力学方面的研究。E-mail：xuquanyong@tsinghua.edu.cn

中图分类号: V231.3
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- 文章访问数: 205
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-31
- 网络出版日期: 2023-06-08

Prediction of aerodynamic characteristics of compressor blade profile based on deep learning

DU Zhou^{1, 2
,},
XU Quanyong^{2,*
, ,},
SONG Zhenshou³,
WANG Handing³,
MA Yulin¹

1.
School of Mechanics and Engineering， Liaoning Technical University，Fuxin Liaoning 123000，China
2.
Institute for Aero Engine，Tsinghua University，Beijing 100084，China
3.
School of Artificial Intelligence，Xidian University，Xi’an 710071，China

摘要

摘要:
采用了数值模拟与机器学习相结合的方式对压气机双圆弧叶型流场气动参数预测开展了研究。对双圆弧叶型进行参数化批量建模，通过计算流体力学进行数值模拟，将数值模拟的模型数据与气动性能的映射提供给多层神经网络（MLP）和卷积神经网络（CNN）进行学习，分别对预测模型的准确率进行了测试比较。研究发现：通过深度学习的方式可以有效的对压气机内部流场气动参数进行准确预测，该模型预测的压力系数误差率小于0.2%，总压损失系数误差率小于1.2%，并证明CNN在气动参数预测的精度上优于传统全连接神经网络。
- 机器学习 /
- 深度学习 /
- 卷积神经网络 /
- 计算流体力学（CFD） /
- 压气机叶型
Abstract:
A combination of numerical simulation and machine learning was used to investigate the prediction of aerodynamic coefficients in the flow field of a double-circular-arc leaf shape of a compressor. Parametric batch modeling of the double-arc impeller shape was carried out, and numerical simulation was performed by computational fluid dynamics. The mapping of model data from numerical simulation to aerodynamic performance was provided to multilayer neural network (MLP) and convolutional neural network (CNN) for learning, and the accuracy of the prediction models was tested and compared respectively. It was found that the accurate prediction of the impeller mechanical internal flow field aerodynamic coefficients can be effectively performed by deep learning, and the error rate of the model predicted pressure coefficient was less than 0.2% and the error rate of loss coefficient was less than 1.2%, proving that CNN was better than traditional fully connected neural network in the accuracy of aerodynamic coefficient prediction.
- machine learning /
- deep learning /
- convolutional neural network /
- computational fluid dynamics (CFD) /
- compressor blade profile

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图 1 典型压气机气动设计流程

Figure 1. Typical compressor aerodynamic design process

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图 2 双圆弧叶型

Figure 2. Double-circular-arc profile

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图 3 计算域截面图

Figure 3. Computational domain cross section diagram

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图 4 截面网格

Figure 4. Cross section grid

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图 5 叶型边界层网格

Figure 5. Boundary layer grid of blade profile

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图 6 叶栅流场速度矢量图

Figure 6. Velocity vector diagram of cascade flow field

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图 7 叶栅流场压力云图

Figure 7. Pressure cloud diagram of cascade flow field

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图 8 叶栅流场马赫数云图

Figure 8. Mach number cloud diagram of cascade flow field

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图 9 样本的C_p分布

Figure 9. C_p distribution of the sample

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图 10 样本的$ \omega $分布

Figure 10. $ \omega $ distribution of the sample

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图 11 实验工况下叶栅马赫数云图

Figure 11. Cascade Mach number cloud diagram under experimental conditions

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图 12 实验与CFD仿真C_p对比

Figure 12. Comparison of C_pbetween experiment and CFD simulation

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图 13 LeNet-5结构

Figure 13. LeNet-5 structure

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图 14 ResNet基本的残差单元

Figure 14. ResNet basic residual element

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图 15 ReLU 激励函数

Figure 15. ReLU excitation function

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图 16 叶型气动参数预测流程

Figure 16. Blade aerodynamic parameters prediction process

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图 17 9个叶型流场轮廓

Figure 17. 9 blade profile flow field contours

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图 18 C_p训练误差

Figure 18. C_p training error

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图 19 $ \omega $训练误差

Figure 19. $ \omega $ training error

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图 20 C_p预测结果

Figure 20. C_p prediction results

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图 21 $ \omega $预测结果

Figure 21. $ \omega $ Forecast results

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表 1 ResNet-34 结构

Table 1. ResNet-34 architecture

层名	输出大小	参数结构
Conv1	143×218	7×7，64，步长 2
Conv2	72×109	3×3 最大池化，步长 2
Conv2	72×109	$\left[ {\begin{array}{*{20}{l}} {3 \times 3,}&{64} \\ {3 \times 3,}&{64} \end{array}} \right] \times 3$
Conv3	36×55	$\left[ {\begin{array}{*{20}{l}} {3 \times 3,}&{128} \\ {3 \times 3,}&{128} \end{array}} \right] \times 4$
Conv4	18×28	$\left[ {\begin{array}{*{20}{l}} {3 \times 3,}&{256} \\ {3 \times 3,}&{256} \end{array}} \right] \times 6$
Conv5	9×14	$\left[ {\begin{array}{*{20}{l}} {3 \times 3,}&{512} \\ {3 \times 3,}&{512} \end{array}} \right] \times 3$
Output	1×1	平均池化, 1-D 全连接

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表 2 对比测试结果

Table 2. Comparison of test results

比较方法	C_p		$ \omega $
比较方法	E_rms	E_r/%	E_rms	E_r/%
方法1	0.1504	2.872	0.2475	10.45
方法2	0.2792	5.332	0.3263	13.78
ResNet-18	0.0097	0.184	0.0293	1.256
ResNet-50	0.0126	0.240	0.0313	1.343
ResNet-34	0.0091	0.173	0.278	1.192

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