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 a previous paper, ASME ES2010-90373, we employed a single objective genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA was significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT was employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. In this paper, we employ a multi-objective GA to optimize the flatback airfoils to achieve two objectives, namely the generation of maximum lift as well as the maximum lift to drag ratio. It is shown that the multi-objective GA optimization can generate superior flatback airfoils compared to those obtained by using single objective GA algorithm.

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