Inverse design method of airfoil based on deep learning

被引：0
|
作者：
He L. [1 ]
Qian W. [1 ]
Liu T. [1 ]
Zhang X. [1 ]
Dong K. [1 ]
机构：
[1] Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, Sichuan
来源：
Hangkong Dongli Xuebao/Journal of Aerospace Power | 2020年 / 35卷 / 09期
关键词：
Airfoil curve; Convolutional neural network; Deep learning; Inverse design method; Prediction model; Pressure distribution;
D O I：
10.13224/j.cnki.jasp.2020.09.013
中图分类号：
学科分类号：
摘要：
An inverse design method of airfoil based on deep learning was proposed, and the pressure distribution image and airfoil curve were taken as the learning objects to build the prediction model. It can obtain the airfoil curve by using convolutional neural network to extract geometric features from pressure distribution image, while avoiding the time-consuming process of traditional numerical calculation method. In the test case, 6000 samples of pressure distribution images and airfoil curves were used to train the prediction model, and the other 561 samples were used for validation with validation time only 6.7s. The average relative error between the predicted and CFD results was 0.55%. In the comparison test, noise was added to the pressure distribution curve and the output layer size was changed to further validate the performance of the model. The results show that the proposed inverse design method of airfoil has strong robustness and prediction accuracy. The method can greatly reduce the computation time and improve the design efficiency of airfoil with satisfactory design accuracy. © 2020, Editorial Department of Journal of Aerospace Power. All right reserved.
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页码：1909 / 1917
页数：8
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