Amirkabir University of Technology Amirkabir Journal of Mechanical Engineering 2008-6032 53 7 2021 09 23 Improvement of aerodynamic coefficients of the airfoil with free form deformation with the aid of Artificial Neural Networks and Genetic Algorithm Improvement of aerodynamic coefficients of the airfoil with free form deformation with the aid of Artificial Neural Networks and Genetic Algorithm 4113 4132 4267 10.22060/mej.2021.18982.6932 FA Mohsen Kazemi Department of mechanical engineering , faculty of engineering , Ferdowsi University of Mashhad , Mashhad , Iran Amirhossein Fardi Department of mechanical engineering , faculty of engineering , Ferdowsi University of Mashhad , Mashhad , Iran Mohammad Javad Maghrebi Department of mechanical engineering , faculty of engineering , Ferdowsi University of Mashhad, Mashhad, Iran Journal Article 2020 09 09 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.<br />Lift coefficient, 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. 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.<br />Lift coefficient, 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. https://mej.aut.ac.ir/article_4267_fb8e51c5c713f2aaf71f62e03c5298db.pdf