Aerodynamic optimization using proficient optimization algorithms

Abstract

Nowadays engineering analysis relies heavily on computer-based solution algorithms to investigate the performance of an engineering system. Computational fluid dynamics (CFD) is one of the computer-based solution methods which are more widely employed in aerospace engineering. The computational power and time required to carry out the analysis increases as the fidelity of the analysis increases. Aerodynamic shape optimization has become a vital part of aircraft design in the recent years. Generally if we want to optimize an airfoil we have to describe the airfoil and for that, we need to have at least hundred points of x and y co-ordinates. It is really difficult to optimize airfoils with this large number of co-ordinates. Nowadays many different schemes of parameter sets are used to describe general airfoil such as B-spline, Hicks-Henne Bump function, PARSEC etc. The main goal of these parameterization schemes is to reduce the number of needed parameters as few as possible while controlling the important aerodynamic features effectively. Here the work has been done on the PARSEC geometry representation method. The objective of this work is to introduce the knowledge of describing general airfoil using twelve parameters by representing its shape as a polynomial function. And also we have introduced the concept of Particle Swarm Optimization (PSO), Simplex-Simulated Annealing (SIMPSA) and Genetic Algorithm to optimize the aerodynamic characteristics of a general airfoil for specific conditions. A MATLAB program has been developed to implement PARSEC, Panel Technique, Genetic Algorithm, SIMPSA and PSO. This program has been tested for a standard NACA 2411 airfoil and optimized to improve its coefficient of lift. Pressure distribution and co-efficient of lift for airfoil geometries has been calculated using panel method. NACA 2411 airfoil has been generated using PARSEC and optimized for 5.0 deg angle of attack using PSO, Genetic Algorithm and SIMPSA.