Kriging surrogate-based genetic algorithm optimization for blade design of a horizontal axis wind Turbine

Abstract

Horizontal axis wind turbines are some of the most widely used clean energy generators in the world. Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultaneously minimize the cost of energy. This work presents the optimization of new Mexico blades for a horizontal axis wind turbine at the wind speed of 10 m/s. The optimization problem is posed to maximize the power coefficient while the design variables are twist angles on the blade radius and rotating axis positions on a chord length of the airfoils. Computational fluid dynamics was used for the aerodynamic simulation. Surrogate-assisted optimization was applied to reduce computational time. A surrogate model called a Kriging model, using a Gaussian correlation function along with various regression models, was applied while a genetic algorithm was used as an optimizer. The results obtained in this study are discussed and compared with those obtained from the original model. It was found that the Kriging model with linear regression gives better results than the Kriging model with second-order polynomial regression. The optimum blade obtained in this study showed better performance than the original blade at a low wind speed of 10 m/s.