Optimization of flatback airfoils for wind-turbine blades using a genetic algorithm

In recent years, the airfoil sections with blunt trailing edges (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, a genetic algorithm is used for shape optimization of flatback airfoils for generating maximum lift-to-drag ratio. The computational efficiency of a genetic algorithm can be significantly enhanced with an artificial neural network. The commercially available software FLUENT is used for calculation of the flowfield using the Reynolds-averaged Navier-Stokes equations in conjunction with a turbulence model. It is shown that the genetic algorithm optimization technique is capable of accurately and efficiently finding globally optimal flatback airfoils.