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

Abstract EN

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 (CL) and Drag Coefficient (CD) data at a range of 0° to 12° angles of attack (α) and at three different Reynolds numbers (Re=68,459, 479, 210, and 958, 422) for all the six airfoils were obtained.

Realizable k-ε 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.