VERIFICATION OF THE PARSEC METHOD AND OPTIMIZATION OF NACA-4412, SG-6043 USING GENETIC ALGORITHM IN MATLAB

Historically, airfoil design optimization has been done through many different methods, or with the use of improving the aerodynamics in some ways as will be discussed below. Of all the PMS methods that are currently available in the literature, PARSEC marks itself out as the best fitting. One of the most effective methods for changing the shape of an airfoil because of high flexibility and accuracy. The current study aims to determine the utility of the PARSEC parameterization method on two airfoil models. Airfoils NACA 4412, and SG6043 within an angle attack of -10 to 15 degrees, by employing a genetic algorithm. The reliability of the PARSEC method is also assessed by reconstructing the geometries of the airfoil and then comparing the shapes with the original airfoils where these characteristics have a significant influence on the airfoil efficiency. For instance lift coefficient (CL), drag coefficient (CD), and the ratio CL/Cd at different angles of attack. The study also includes the improvement of the aerodynamic design of both airfoils through the use of a genetic algorithm which is coded and run in MATLAB, with the PARSEC parameters used as the base for optimization. It is also important for one to conduct some comparisons between the PARSEC-optimized airfoils and the standard airfoil. The performance of the PARSEC method in making the wing shape the same and similar to the original one is accurate. Aerodynamic characteristics. Significantly, the same was realized in the optimized airfoil and the original airfoil which recorded the maximum pushover speed, drag, and lift characteristics to ±0.3 at an angle of attack of 8 ° and Reynolds number of 10 e5. This paper supports the efficiency of the used PARSEC parameterization. It is found to act as an effective means of support to engineers and researchers who would like to use this method to improve airfoil’s characteristics in aviation, and aeronautical, Such as aerospace applications, and wind turbines. The findings show a positive change in the dependent measures, showing how the variables in the present study fare compared to other studies. By comparing the aerodynamic efficiency of the optimized airfoils, paying attention to the results indicates the use of a genetic algorithm should help to increase several aspects of the wing’s performance