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

Wei-Hsin Chen (), Trinh Tung Lam, Min-Hsing Chang (), Liwen Jin, Chih-Che Chueh and Gerardo Lumagbas Augusto
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Wei-Hsin Chen: Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan
Trinh Tung Lam: International Degree Program on Energy Engineering, National Cheng Kung University, Tainan 701, Taiwan
Min-Hsing Chang: Department of Energy Engineering, National United University, Miaoli 360, Taiwan
Liwen Jin: Institute of Building Environment and Sustainable Technology, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 712000, China
Chih-Che Chueh: Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan
Gerardo Lumagbas Augusto: Mechanical Engineering Department, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines

Energies, 2024, vol. 17, issue 2, 1-24

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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