Kanat etrafındaki akış alanının tahmini üzerine bir derin öğrenme metodolojisi

This study aims to predict flow fields around airfoils using a deep learning methodology based on an encoder-decoder convolutional neural network. Neural network training and evaluation are performed from a set of computational fluid dynamics (CFD) solutions of the 2-D flow field around a group of known airfoils at a wide range of angles of attack. Reynolds averaged Navier-Stokes (RANS)-based CFD simulations are performed at a selected Mach number on the transonic regime on high-quality structured computational grids. The results of these simulations are utilized as training data set. For better shape learning, a distance map is generated from airfoil shape and provided to the algorithm at data locations of the flow quantities, i.e., pressure coefficient, Mach number, relative to the airfoil shape. The predictive ability of the model is scrutinized both qualitatively and quantitatively. The flow features associated with the transonic effects and the angle of attack variation, such as the shock waves and the flow separation, are well predicted. The results indicate that the presented model provides remarkably good flow field predictions at a fraction of the computational time of CFD simulations. The predicted flow field allows the computation of the aerodynamic coefficients, providing an accurate and fast airfoil selection tool for aircraft designers.