Variational Auto Encoder-Based DNN for Lift to Drag Ratio
The convolutional neural network (CNN) method is capable of image processing and
is widely used today for aerodynamic meta-modelling tasks. CNN can predict the
aerodynamic property of an air foil based on a large enough dataset. Variational auto
encoder (VAE) has a capability of image processing too, as VAE belong to the family
of Generative Adversal Networks (GANs) they are also capable of extracting the best
feature on the basis of two additional layers (Mean and Variance). The primary goal of
this thesis is to predict the lift-to-drag ratio of air foils with various angles of attack using
VAE (a). This thesis also employs the Convolutional Neural Network (CNN) method to
calculate the lift-to-drag ratio of all air foils. The efficiency and accuracy of these two
methods are compared and discussed in this thesis, and it is demonstrated that the VAE
method can maintain a relatively competitive level of accuracy while being far more time
efficient than the analytical method and CNN method.
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Posted Jun 9, 2024
The efficiency and accuracy of the CNN and the Variational autoencoder (VAE) methods are compared.
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