APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN DETERMINING THE VELOCITY AND PRESSURE FIELDS AROUND AIRFOIL MODELS

The article constructs a convolutional neural network for predicting pressure and velocity fields around a twodimensional aircraft wing model (airfoil model). Training data is computed using the Reynolds-averaged method,then extracted, focusing on the flow around the wing. Input data includes geometric parameters, 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. Results related to pressure distribution, velocity, and method error are presented and discussed in the study.