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The Method of search algorithms or optimisation algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimisation problem. Nowadays various optimisation methods such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimisation (PSO) etc., are more widely employed to solve the aerodynamic shape optimisation problems. In addition to the optimisation method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimisation process. Since the reduction in the number of design parameters is one of the most important requirements for the aerodynamic shape optimisation problem, it becomes important to mathematically describe the airfoil geometry with minimum number of design parameters. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimisation algorithms. It is also demonstrated that the estimation of a suitable optimisation scheme for a given optimisation problem. An aerodynamic shape optimisation problem is formulated for NACA 0012 airfoil and solved using the methods of Particle Swarm Optimisation and Genetic Algorithm for 5.0 deg angle of attack. The results show that the particle swarm optimisation scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the PSO shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

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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. The Method of search algorithms or optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays various optimization methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc., are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimization process. 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 Algorithm which is one kind of Non-Traditional Optimization technique to optimize the aerodynamic characteristics of a general airfoil for specific conditions. An aerodynamic shape optimization problem is formulated for NACA 2411 airfoil and solved using the method of Particle Swarm Optimization for 5.0 deg angle of attack. A MATLAB program has been developed to implement PARSEC, Panel Technique, and PSO Algorithm. 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 Algorithm. The results show that the particle swarm optimization scheme is more effective in finding the optimum solution among the various possible solutions.

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