Optimizing H-Darrieus Wind Turbine Performance with Double-Deflector Design

Abstract

This study aims to improve an H-Darrieus vertical-axis wind turbine (VAWT) by imposing a novel double-deflector design. A computational fluid dynamics (CFD) model was implemented to examine the aerodynamic characteristics of the VAWT with double deflectors. Geometrics factors related to the locations of the two deflectors were considered, and the orthogonal array based on the Taguchi method was constructed for CFD simulation. The CFD results were further provided as the training data for the artificial neural network (ANN) to forecast the optimal configuration. The results indicate that the performance of a VAWT with a double-deflector design could exceed that of a bare VAWT or that of one using a single deflector. The mean power coefficient for a bare VAWT is 0.37, although it could be much higher with a proper setup using double deflectors. The prediction of ANN analysis is consistent with the result of CFD simulation, in which the difference between the ANN prediction and CFD simulation is generally less than 4.48%. The result confirms the accuracy of the prediction of the optimal VAWT performance with a double-deflector design.

Keywords

• vertical-axis wind turbine (VAWT)
• double deflectors
• computational fluid dynamics
• artificial neural network (ANN)
• optimization