计算流体力学
垂直轴风力涡轮机
空气动力学
涡轮机
海洋工程
人工神经网络
风速
田口方法
环境科学
模拟
计算机科学
航空航天工程
气象学
工程类
物理
人工智能
机器学习
作者
Wei‐Hsin Chen,Trinh Tung Lam,Min‐Hsing Chang,Liwen Jin,Chih‐Che Chueh,Gerardo L. Augusto
出处
期刊:Energies
[MDPI AG]
日期:2024-01-19
卷期号:17 (2): 503-503
被引量:4
摘要
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.
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