Prediction of Near-Wake Velocity in Laminar Flow over a Circular Cylinder Using Neural Networks with Instantaneous Wall Pressure Input

In the present study, to predict the transverse velocity field in the near-wake of laminar flow over a circular cylinder at the Reynolds numbers of 60 and 300, we construct neural networks with instantaneous wall pressures on the cylinder surface as the input variables. For the two-dimensional unsteady flow at Re=60, a fully connected neural network (FCNN) is considered. On the other hand, for a three-dimensional unsteady flow at Re=300 having spanwise variations, we employ two different convolutional neural networks based on an encoder–FCNN (CNN-F) or an encoder–decoder (CNN-D) structure. Numerical simulations are carried out for both Reynolds numbers to obtain instantaneous flow fields, from which the input and output datasets are generated for training these neural networks. At the Reynolds numbers considered, the neural networks constructed accurately predict the transverse velocity fields in the near-wake over the cylinder using the information of instantaneous wall pressures as the input variables. In addition, at Re=300, it is observed that CNN-D shows a better prediction ability than CNN-F.