Optimization of flatback airfoils for wind turbine blades

In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. In this paper, we employ a genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA is significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT is used for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. It is shown that the combined GA/ANN optimization technique is capable of accurately and efficiently finding globally optimal flatback airfoils.