The airfoil/wing design is probably the most important part of an aircraft design. A practical aerodynamic design of airfoil requires optimal performance on a wide range of operating conditions. These requirements are often found to be conflicting and demand designer expertise for satisfactory results, not to mention the computational burden of the simulations. Although there exists many studies on direct and inverse design of airfoils, less attention has been paid to simultaneous consideration of multiple objectives. In this paper, a multi-objective optimal airfoil design procedure is presented. PARSEC parametrization method has been utilized to express the airfoil geometry in terms of twelve physical parameters. The aerodynamic performance is obtained by 2D panel method using XFOIL package. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been applied for airfoil geometry design because it is efficient and keeps the diversity among the solution set. The objective functions and constraints are chosen to enhance the flight performance at takeoff, cruise, and landing conditions for a long range cargo aircraft. Objectives include maximization of lift to drag ratio (CL/CD), maximization of rate of change of lift to attack angle (dCL/dα) for having increased lift at takeoff/landing condition and minimization of pitching moment CM2. Two applied constraints are CL > CLmin at operating condition and thickness ≤ %25. Each evaluation is consist of finding the optimal operating angle of attack and reporting the corresponding objective values. The quality of the solution at various generations has been studied to guarantee the convergence of the solution. Like any other multi-objective optimization problem (MOP), the solution would be a set of Pareto optimal configurations. Although having multiple solutions gives us a better understanding of the problem, only one configuration should be chosen by the designer. A post processing technique is also used to help the decision maker to choose the most appropriate compromise in the solution set. The method is found to be effective in finding efficient set of airfoils. The simulation is also found to be effective because it can be done on a regular personal computer. It should be noticed that the method can be easily applied to other airfoil design applications by simply modifying the objective functions and the constraints.

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