Large cities have a significant area of buildings with roofs that are not used most of the time. Vertical-axis wind turbines are suitable for this kind of on-site renewable energy generation. Since wind speeds are not high in these cities, a suitable solution to improve energy generation is to add a Wind Booster. This paper presents a methodology useful for selecting and optimizing the main components of a Wind Booster. As a case of study, we present this methodology in a Wind Booster for a Vertical Axis Wind Turbine (VAWT) that considers the wind flow’s specific behavior in a particular city. The final Wind Booster design is state of the art and makes use of Computational Fluid Dynamics (CFD) and Design of Experiments (DOE) techniques. We experimented with the conditions of Mexico City, obtaining a 35.23% increase in torque with the optimized Wind Booster configuration. The results obtained show the potential of this methodology to improve the performance of this kind of system. Moreover, since wind behavior is very different in each city, our proposal could be beneficial for researchers looking to implement the best possible wind turbine in their locality.

In this work, a 3D computational model based on computational fluid dynamics (CFD) is built to simulate the aerodynamic behavior of a Savonius-type vertical axis wind turbine with a semi-elliptical profile. This computational model is used to evaluate the performance of the wind turbine in terms of its power coefficient (Cp). Subsequently, a full factorial design of experiments (DOE) is defined to obtain a representative sample of the search space on the geometry of the wind turbine. A dataset is built on the performance of each geometry proposed in the DOE. This process is carried out in an automated way through a scheme of integrated computational platforms. Later, a surrogate model of the wind turbine is fitted to estimate its performance using machine learning algorithms. Finally, a process of optimization of the geometry of the wind turbine is carried out employing metaheuristic optimization algorithms to maximize its Cp; the final optimized designs are evaluated using the computational model for validating their performance.