An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network

Abstract

In this research, an optimization of a wind turbine airfoil is performed by Genetic Algorithm (GA) as optimization method, coupled with CFD (Computational Fluid Dynamics) and Artificial Neural Network (ANN). A pressure-based implicit procedure is applied to solve the Navier-Stokes equations on a nonorthogonal mesh with collocated finite volume formulation to calculate the aerodynamic coefficients. The boundedness criteria for the numerical procedure are determined by Normalized Variable Diagram (NVD) scheme and the k-ε eddy-viscosity turbulence model is utilized. ANN has been used as surrogate model to reduce computational cost and time.  Single objective and multi objective optimization of wind turbine airfoil have been performed and the results of optimization are presented. To decrease the number of design variables and producing a smooth shaped airfoil, modified Hicks-Henne functions are applied. In this process, the Eppler E387 airfoil has been applied as the base airfoil. The angle of attack varies from 0 to 20 degrees and Reynolds number of the flow is 460000. The presented technique decreases the time of optimization by 99.5%. Moreover, the results manifest the good agreement of trained ANN outputs and CFD simulation. In addition, the Multi-objective optimization can attain the better solutions than single objective to design a wind turbine airfoil with good stall characteristics.