Shape optimization of airfoils in transonic flow using a multi-objective genetic algorithm

Shape optimization of transonic airfoils requires creating an airfoil that reduces the drag due to transonic shocks by either eliminating them or reducing their strength at a given transonic cruise speed while maintaining the lift. The RAE 2822 and NACA 0012 airfoils are most widely used test cases for validation of computational modeling in transonic flow. This study employs a multi-objective genetic algorithm (MOGA) for shape optimization of RAE 2822 and NACA 0012 airfoils to achieve two objectives, namely the elimination of shock and maintaining or increasing the lift at a given transonic Mach number and angle of attack. The commercially available software FLUENT is employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a two-equation turbulence model. It is shown that the MOGA can generate superior airfoils compared to the original airfoils by achieving both the objectives.