Fast Inverse Design of Transonic Airfoils by Combining Deep Learning and Efficient Global Optimization

Fast Inverse Design of Transonic Airfoils by Combining Deep Learning and Efficient Global Optimization

In this paper, a deep learning model trained to generate well-posed pressure distributions at transonic speeds is coupled by the efficient global optimization (EGO) algorithm to speed up the inverse design process for transonic airfoils. First, the Wasserstein generative adversarial network (WGAN) i...

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Bibliographic Details
Main Authors: Feng Deng, Jianmiao Yi
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Aerospace
Subjects:
inverse design
transonic airfoil
deep learning
Wasserstein Generative Adversarial Network (WGAN)
Deep Convolutional Neural Network (DCNN)
efficient global optimization
Online Access:https://www.mdpi.com/2226-4310/10/2/125

Internet

https://www.mdpi.com/2226-4310/10/2/125

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