Effects of hyperparameters on flow field reconstruction around a foil by convolutional neural networks

Abstract

Unsteady flow field over a foil at Rec = 300 was used to train the convolutional neural network (CNN) model with different hyperparameter settings based on the mode decomposing CNN autoencoder (MD-CNN-AE). The effects of three key hyperparameters, such as the number of convolution kernels, the kernel size and the number of convolution layers of CNN models, on the flow field reconstruction were investigated. The reconstructed error and the energy distribution of the orthogonal basis of the flow field were utilized to evaluate the differences. The decomposed fields generated by the latent vectors in the model and the corresponding mode decomposition performed by the proper orthogonal decomposition (POD) were visualized in the physical space to observe the differences directly. The present results showed that there is no obvious monotonicity between the reconstructed error and the number of kernels; secondly, the kernel size had little effect on the error; however, when considering the number of convolution layers, a conclusion contrary to the general information in the literature is obtained, that the reconstructed error increases with the number of convolution layers. The relationship between the decomposed field and the orthogonal basis was also analyzed.