Volume 43 Issue 1
Jan.  2022
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Muchen WANG, Lizhou LI, Jun ZHANG, Yuqi HUANG, Lin ZHANG, Yue SHI. 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
Citation: Muchen WANG, Lizhou LI, Jun ZHANG, Yuqi HUANG, Lin ZHANG, Yue SHI. 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

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

doi: 10.21656/1000-0887.420137
  • Received Date: 2021-05-17
  • Rev Recd Date: 2021-06-24
  • Available Online: 2021-12-16
  • Publish Date: 2022-01-01
  • 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.

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