The convolutional neural network is widely applied in the classification of images and medicine. Some current networks are used in aerospace engineering and show a high potential in determining aerodynamic forces and flow fields. This article constructs a convolutional neural network for predicting pressure and velocity fields around a two-dimensional aircraft wing model (airfoil model). Training data is computed using the Reynolds-averaged method, and then extracted, focusing on the flow around the wing. Input data includes geometric parameters, and airfoil inlet velocity, and output data includes pressure field and flow velocity around the airfoil. The convolutional neural network is based on improving the U-Net network model, commonly used in medical applications. The results show that the convolutional neural network accurately predicts flow around the airfoil, with an average error below 3%. Therefore, this network can be used and further developed to predict flow around the wing. The network is then applied to predict the pressure and pressure fields around a blunt-based model with different aspect ratios. The main feature of the flow can be extracted from the network. Results related to pressure distribution, velocity, and method error are presented and discussed in the study. This study also suggests improving the network and applying it to pressure and velocity fields in aerospace engineering
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