Inverse Design of Airfoils Using Artificial Neural Network

With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airfoil research. In this paper, an inverse design method of airfoil using ANN and profile database is proposed. In preliminary design phase of the UAV, airfoil database and ANN are constructed and validated to design a profile that satisfied the aerodynamic character of the airfoil for a given flight performance and availability are verified. Many types of airfoils are parameterized using the PARSEC method. For the NASA SC, RAE, HQ, and NACA series of airfoil, the aerodynamic characteristics at the specified Reynolds number and angle of attack are calculated and database are created. These aerodynamic characters are trained by ANN to establish the inverse design process of the airfoil. For the airfoil obtained through the reverse design process, the lift and drag values obtained using CFD calculations are in relatively good agreement with the setting target values.