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The fast and accurate prediction of unsteady flow becomes a serious challengein fluid dynamics, due to the high-dimensional and nonlinear characteristics. Anovel hybrid deep neural network (DNN) architecture was designed to capture theunsteady flow spatio-temporal features directly from the high-dimensionalunsteady flow fields. The hybrid deep neural network is constituted by theconvolutional neural network (CNN), convolutional Long Short Term Memory neuralnetwork (ConvLSTM) and deconvolutional neural network (DeCNN). The flow arounda cylinder at various Reynolds numbers and the flow around an airfoil at higherReynolds number are carried out to establish the datasets used to train thenetworks separately. The trained hybrid DNNs were then tested by the predictionof the flow fields at future occasions. The predicted flow fields using thetrained hybrid DNNs are in good agreement with the flow fields calculateddirectly by the computational fluid dynamic solver.

A new prediction method of unsteady wake flow by the hybrid deep neural network