Aerodynamic performance improvement of wind turbine blade by cavity shape optimization. (March 2019)

• Record Type: Journal Article
• Authors: Fatehi, Mostafa; Nili-Ahmadabadi, Mahdi; Nematollahi, Omid; Minaiean, Ali; Kim, Kyung Chun
• Is Part Of: Renewable energy. Volume 132(2019)
• Page Start: 773
• Page End: 785
• Publication Date: 2019-03
• Subjects: Riso_B1 airfoil -- Cavity -- Aerodynamic performance -- Optimization -- Wind turbine -- Genetic algorithm; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.08.047
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Many conventional airfoils, despite a good performance at their design points, get out of optimal conditions outside the design points. One passive way to enhance the airfoil performance is to use a cavity with an optimized shape. In this study, Riso_B1_18 airfoil, having a remarkable aerodynamic performance for wind turbine blades, is selected as a substrate for deploying an optimized cavity on the airfoil. For shape optimization of a cavity, its shape and downstream suction surface are parametrized to reach an optimum lift-to-drag ratio as the target function by using the genetic algorithm. The results of transient numerical solution indicate that the optimized cavity is well capable of draping vortex to control the stall margin, prevent flow fluctuations and significantly increase the lift-to-drag ratio at off-design conditions. To validate the performance improvement obtained from this numerical optimization, a force measurement setup is accomplished in a wind tunnel with 30 × 30 cm 2 test section to measure the lift and drag forces of the Riso airfoil with and without optimized cavity. The experimental results shows that the lift-to-drag ratio increases 31% at AOA = 14° and 57% at AOA = 20° due to using the optimized cavity. Highlights: A cavity shape optimization using Genetic algorithm is presented. A Riso_B1_18 airfoil which used in wind turbine blade are optimized. Lift-to-drag ratio is selected as the target function in the genetic algorithm. A 31% increment in lift-to-drag ratio is seen at AOA = 14 experimentally. A 57% increment in lift-to-drag ratio is seen at AOA = 20 experimentally.