Proceedings of the National Conference On Energy Technology and Industrial Automation 2018 (NCETIA2018) 13 th December, 2018, Chittagong, Bangladesh (ISBN : 978-984-34-5487-4) NCETIA2018-PI-020 NCETIA 2018 1. INTRODUCTION An airfoil is the shape of a wing (wings of an aircraft), blade (of a rotor or a turbine) or sail. The National Advisory Committee for Aeronautics (NACA, which is the predecessor to NASA) developed some definite shapes of the airfoil, which are known as NACA airfoils. When an airfoil moves through the fluid, it creates aerodynamic force. The component of this force perpendicular to the direction of motion is called the lift (Fl) and parallel to the direction of motion is called drag (Fd). The drag and lift force depend on the density of the fluid (ρ), the free stream fluid velocity (V), and the size, shape and orientation of the body. As they depend on so many things, it is convenient to work with appropriate dimensionless numbers that represent the drag and lift characteristics of the body [1]. These numbers are lift coefficient (C l ) and drag coefficient (C d ). They are defined as           &           , where A is the frontal area. Unlike lift, drag is usually an undesirable force for the aerodynamic structure because it creates resistance to its motion. Therefore, we need an optimized shape of the structure, which will face reduced drag and increased lift in the direction of its motion. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are the two bio-inspired optimization techniques. If we examine the feature of nature closely, we will concur that, nature is the perfect example of optimization. By following the technique of nature, we can always come up with the best solution to tackle the natural force, e.g. the undesirable drag force. GA is an evolutionary based optimization algorithm, which follows the principles of the Charles Darwin theory of survival of the fittest [2]. This algorithm begins with a population of candidate solution (phenotype). Each phenotype has a set of properties (genotype). Each genotype goes through iterative process and thus creates a generation. Fitness of every individual in each generation is evaluated using an appropriate fitness function suitable for the problem. The best-fit individuals are selected and they undergo crossover and mutation to give a new set of solutions (offspring). The new solution set again goes through iteration process. The algorithm usually terminates when a maximum number of generations have been produced or a satisfactory fitness level has been reached. On the other hand, PSO is also a population based optimization technique. However, it is inspired by social behavior, like searching for food of a folk of bird or a school of fish. PSO starts with a population of random solutions (called particle). These particles are moved around in the search-space according to their own best known position as well as the entire swarm's best known position defined by fitness function. When improved positions are obtained these will then guide the movements of the swarm. The process is repeated and by doing so, a satisfactory solution is discovered. For airfoil shape optimization, both GA & PSO can be used effectively. Abstract-For analysis & optimization purposes, it is necessary to represent an airfoil with fewer parameters. In this paper, the two-dimensional surface of an airfoil has been represented by 5th order Bézier curve. So, each of the upper and lower surface of the airfoil can be represented by (5+1) = 6 control points only. For optimization purposes, control points of the cubic B-spline are used for modeling the airfoil as analyses are done with Qblade. Here, the design space has been defined as the 25% above and 25% below the y coordinates of the control points. Within this design space, two optimized shapes are obtained by using Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) tool of Matlab respectively. Each shape has a higher coefficient of lift-to-drag ratio (Cl/Cd) than that of the original airfoil for a range of angle of attack (AoA). So, these two shapes can definitely be used in various aerodynamic applications like in wind turbine and in aircraft wings to get better lift and reduced amount of drag force. Keywords: Bézier curve, cubic B-spline, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Qblade. Modeling and Optimization of NACA 2412 Airfoil Umme Kawsar Alam 1 , Fazle Rabby 2 and Md. Mahbubul Alam 3 1,3 Department of Mechanical Engineering, Chittagong University of Engineering & Technology, Chittagong-4349 Bangladesh. 2 Department of Computer Science & Engineering, Sheikh Fazilatunnesa Mujib University, Jamalpur-2000, Bangladesh. koli_kawsar24@yahoo.com * , rabbycsecuet@gmail.com, malam@cuet.ac.bd