An Airfoil Optimization Method Based on the Convolutional Neural Network Aerodynamic Reduced Order Model

WANG Muchen; LI Lizhou; ZHANG Jun; HUANG Yuqi; ZHANG Lin; SHI Yue

doi:10.21656/1000-0887.420137

Volume 43 Issue 1

Jan. 2022

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Article Navigation > Applied Mathematics and Mechanics > 2022 > 43(1): 77-83

WANG Muchen, LI Lizhou, ZHANG Jun, HUANG Yuqi, ZHANG Lin, SHI Yue. An Airfoil Optimization Method Based on the Convolutional Neural Network Aerodynamic Reduced Order Model[J]. Applied Mathematics and Mechanics, 2022, 43(1): 77-83. doi: 10.21656/1000-0887.420137

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An Airfoil Optimization Method Based on the Convolutional Neural Network Aerodynamic Reduced Order Model

doi: 10.21656/1000-0887.420137

1.
College of Mechatronics Engineering, North University of China, Taiyuan 030051, P.R.China
2.
Department of Mathematics, Taiyuan University, Taiyuan 030001, P.R.China

Received Date: 2021-05-17
Rev Recd Date: 2021-06-24

Available Online: 2021-12-16

Publish Date: 2022-01-01

Abstract

Abstract

To solve the nonlinear problem of airfoil shape optimization induced by nonlinear large perturbation, an optimization method was proposed based on the convolutional neural network (CNN) aerodynamic reduced order model (ROM). In the method, the aerodynamic forces on different airfoils were used as the training data for the proposed ROM. For the sake of the maximum lift-drag ratios, the ROM was applied to optimize the airfoil shape. The results show the method applies well to the prediction and optimization of airfoil shape dynamics under large perturbation. The improving effects of the parameter pooling and the radial basis function method based training data method on the accuracy of the dimensionality reduction model, were discussed. The reason for the improvement is that, the training data dimensionality reduction can cut down the number of undetermined parameters in the CNN model and make the CNN model converge better under the same data volume.
- reduced order model,
- convolutional neural networks,
- optimization,
- radial basis function,
- parameters pooling

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References

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