In this research, optimization of a wind turbine airfoil is done by Genetic Algorithm (GA) as optimization method, coupled with CFD (Computational Fluid Dynamics) and Artificial Neural Network (ANN). A pressure-based implicit procedure is used 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 from 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 reduce the number of design variables and producing a smooth shaped airfoil, modified Hicks-Henne functions are used. In this process, the Eppler E387 airfoil has been applied as the base airfoil. Angle of attack varies from 0 to 20 degrees and Reynolds number of the flow is 460000.
Optimization of Wind Turbine Airfoil With Good Stall Characteristics by Genetic Algorithm Using CFD and Neural Network
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Djavareshkian, MH, & Latifi, A. "Optimization of Wind Turbine Airfoil With Good Stall Characteristics by Genetic Algorithm Using CFD and Neural Network." Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition. Volume 6B: Energy. San Diego, California, USA. November 15–21, 2013. V06BT07A083. ASME. https://doi.org/10.1115/IMECE2013-64598
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