Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front

A Computational Fluid Dynamics (CFD) and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant aerodynamic loads. The Lift Coefficient () and Drag Coefficient () data at a range of 0° to 12° angles of attack () and at three different Reynolds numbers (, 479, 210, and 958, 422) for all the six airfoils were obtained. Realizable turbulence model with a second-order upwind solution method was used in the simulations. The standard least square method was used to generate response surface by the statistical code JMP. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to determine the Pareto optimal set based on the response surfaces. Each Pareto optimal solution represents a different compromise between design objectives. This gives the designer a choice to select a design compromise that best suits the requirements from a set of optimal solutions. The Pareto solution set is presented in the form of a Pareto optimal front.