Optimization of thick wind turbine airfoils using a genetic algorithm

Abstract

In this study, we optimized thick airfoils for wind turbines using a genetic algorithm (GA) coupled with computational fluid dynamics (CFD) and geometric parameterization based on the Akima curve fitting method. Complex and separated flow fields around the airfoils of each design generation were obtained by performing Reynolds-averaged Navier-Stokes steady flow simulation based on the in-house code of an implicit high-resolution upwind relaxation scheme for finite volume formulation. Airfoils with 40 % and 35 % thickness values were selected as baseline airfoils. An airfoil becomes thicker toward the blade root area, thereby increasing blade stiffness and lowering its aerodynamic efficiency. We optimized the airfoils to simultaneously maximize aerodynamic efficiency and blade thickness. The design variables and objective function correspond to the airfoil coordinates and the lift-to-drag ratio at a high angle of attack with airfoil thickness constraints. We improved the lift-to-drag ratio by 30 %similar to 40 % compared with the baseline airfoils by performing optimization using GA and CFD. The improved airfoils are expected to achieve a 5 %similar to 11 % higher torque coefficient while minimizing the thrust coefficient near the blade root area.