A hybrid wake method for simulating yaw tandem wind turbine

被引:0
作者
Yuan, Yuming [2 ]
Zhou, Binzhen [1 ,2 ]
Yang, Zhiwei [2 ]
Liu, Bo [3 ]
Zhou, Zhipeng [3 ]
Li, Mingxin [4 ,5 ]
机构
[1] South China Univ Technol, State Key Lab Subtrop Bldg & Urban Sci, Guangzhou 510641, Peoples R China
[2] South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou 510641, Peoples R China
[3] China Power Engn Consulting Grp Co LTD, Beijing 100032, Peoples R China
[4] Univ Strathclyde, Dept Elect & Elect Engn, Glasgow City, Scotland
[5] Univ Tokyo, Sch Engn, Dept Civil Engn, 7-3-1 Hongo,Bunkyo Ku, Tokyo, Japan
基金
中国国家自然科学基金;
关键词
Yawed wake; Wake model; Velocity distribution; Wind turbine; Wake steering; Wake deflection; FARM; MODEL; IMPACT;
D O I
10.1016/j.oceaneng.2024.119549
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Accurately describing the meandering of the wake of a yaw wind turbine is crucial. Most yaw analytical models use a superposition model to consider the interaction of wind turbine wake, which can introduce certain errors. A hybrid wake model for wind turbine, which is based on the Computational Fluid Dynamics-Improving the dynamic wake meandering (CFD-IDWM) hybrid wake model for analyzing the yaw of a tandem wind turbine, is proposed in this study. A search zero method is proposed to address the challenges faced by existing wake center tracking methods when applied to hybrid wake models. In contrast to conventional yaw wake models, the hybrid model employs CFD tools in the near wake to predict wind turbine wake interactions, thereby circumventing errors associated with wake superposition methods. Numerical simulations were performed on the wake of a tandem wind turbine under various yaw and tilt angles. The wake deflection and velocity distribution verification CFD results, and the formation of counter-rotating vortex pairs (CVP) was accurately reconstructed in the downstream computational domain. Compared to traditional CFD methods, the hybrid model has improved computational efficiency by 40%, with consistent accuracy, and provides significant improvements for predicting the total power generation of yaw wind turbines.
引用
收藏
页数:15
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