Airfoil Optimization with Metaheuristic Artificial Bee Colony Algorithm Supported by Neural Network Trained Using Nasa-Foilsim Data

Abstract

In this study, the wing profile, which is difficult to calculate and determine, has been optimized with the help of Foilsim data and optimization algorithms. Foilsim data provided by NASA (National Aeronautics and Space Administration) and used by many researchers, especially in developing model airplanes, has been provided to use in aircraft wing shape optimization. Although Foilsim is a very useful simulation program for designers, it cannot be used effectively in optimization processes due to its web environment. Lift coefficient is needed for Lift equation in airfoil shape optimization. Lift coefficient depends on angle, camber, and thickness of airfoil Calculation of Lift coefficient is difficult and needs heavy mathematical equations or real experiments. By using Foilsim data and optimization algorithm (Artificial Neural Networks: ANN, Artificial Bee Colony: ABC), wing angle, camber and thickness values, which are difficult to determine and calculate, were estimated and comparative experiments of the values were made. (Fixed Lift, Fixed Speed, Fixed Wing Area). Experimental results have shown that it is a useful study for airfoil shape optimization. In short, in this study, by using the Foilsim data and the optimization algorithm to provide the lifting force determined by the designer, the most suitable angle, camber, thickness values of the wing, which are difficult to determine and calculate, are determined to enable the production of efficient aircraft. The user enters the desired lift value into the ABC optimization algorithm and finds the required wing properties for the desired lift value.