Predicting aerodynamic characteristics of airfoils using artificial neural network

In this study, an artificial neural network (ANN)-based method is proposed to predict the aerodynamic characteristics of airfoils, such as NACA 0012, NACA 0015, NACA 0018, NACA 0021, and NACA 0025, approximating the flow around airfoils as a function of the Reynolds number ( Re ), angle of attack ( α ), airfoil coordinates ( X , Y ), and predicting the lift coefficient ( C L ) and drag coefficient ( C D ) without using extensive software packages. Wind turbine data were obtained for C L and C D for different α (0 ◦ ≤ α ≤ 180 ◦ ) and different values of Re (10 4 ≤ Re ≤ 10 7 ). An ANN model was trained to achieve a root mean square error ( RMSE ) of less than 0.12 and 0.025 for C L and C D , respectively. For C L and C D , the RMSE of the trained model used to evaluate the new data was less than 0.09 and 0.12, respectively. Subsequently, the results were validated in a two dimensional numerical domain using RANS-CFD simulations and experimental data, showing that the proposed ANN approach is in good agreement for predicting the stall shape and aerodynamic characteristics at an angle of attack ( α ) ranging from (0 ◦ ≤ α ≤ 30 ◦ ).