Improvement of aerodynamic coefficients of the airfoil with free form deformation with the aid of Artificial Neural Networks and Genetic Algorithm

With the advent of morphing airfoils, the aerodynamics of wind turbines and wings underwent many changes. In this study, the aerodynamic coefficients of morphing airfoil based on NACA 0015 are optimized in the range of Reynolds number 105 to 106 and the angle of attack 0 to 12 degrees using Artificial Neural Network (ANN) and Genetic Algorithm(GA). First, the airfoils were created in MATLAB software by random control points and mesh generated in Gambit software, then in two-dimensional Ansys software were simulated. The simulation results, including lift and drag coefficients, separation point and pressure center, with control points were used to train the Artificial Neural Network (ANN). The trained function is given as an input function to the Genetic Algorithm(GA) to optimize the desired coefficients.Lift coefficient, the center of pressure, separation point and lift to drag ratio were optimized as a single objective, In single-objective optimization, the lift coefficient was increased by 18% using the morphing airfoil. Also, the lift coefficient and the center of pressure, the lift coefficient and the drag coefficient were optimized as the dual-objectives optimization. In the optimization of the dual objectives, lift and drag coefficients were controlled by 0.8 and 0.03, respectively, by the morphing airfoils.