Reduced-order modelling of a bluff body turbulent wake flow field using hierarchical convolutional neural network autoencoder
In this study,a nonlinear hierarchical convolutional autoencoder(H-CNN-AE)is employed to sort the energy content of the latent vectors of AE,which is analogous to the method of proper orthogonal decomposition(POD),and at the same time result in better performance in terms of reduced-order modelling within limits.This method is applied to a turbulent wake behind a three-dimensional circular cylinder bluff body at Re=20 000.We assess the ability of H-CNN-AE with L2 error and compares it with the results of POD.Furthermore,the concept of grouping AE-modes is extended.We observe the change of mean square error between the reconstructed and the original flow when adding the number of latent vectors of each group.It is demonstrated that when the number of subnetworks and low-dimensional vectors in latent space of each subnetwork is small,H-CNN-AE has better capability to restore the flow field than POD.However,it is also found that the strength of H-CNN-AE will weaken with the increase of the number of subnetworks and latent AE modes and will even be inferior to POD under certain conditions.
hierarchical autoencoderconvolutional neural networkbluff body turbulent wakereduced order model
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