TY - JOUR
T1 - Discussion on the spatial-temporal inhomogeneity characteristic of horizontal-axis wind turbine's wake and improvement of four typical wake models
AU - Zhang, Shaohai
AU - Gao, Xiaoxia
AU - Lin, Jiawei
AU - Xu, Shinai
AU - Zhu, Xiaoxun
AU - Sun, Haiying
AU - Yang, Hongxing
AU - Wang, Yu
AU - Lu, Hao
N1 - Funding Information:
The work described in this paper was supported by the National Natural Science Foundation of China (No. 52076081 ), the Fundamental Research Funds for the Central Universities (No. 2020MS107 ), the Research Institute for Sustainable Urban Development (RISUD) with account number of BBW8 of The Hong Kong Polytechnic University (No. 1-BBW8 ), the Post-graduate's Innovation Fund Project of Hebei Province (No. CXZZSS2023192 ). We sincerely thank Professor Tian De and Associate Professor Wu Guangxing for their strong support for the experiment.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/5
Y1 - 2023/5
N2 - Previous studies on wake of horizontal-axis wind turbines (HAWTs) mainly focus on the spatial distribution but ignore the time-varying characteristics of the wake profile which highly affect the operation strategy of the downstream HAWTs to ensure the high-efficiency and low-fatigue load. This paper proposed a mathematical method for studying the spatial-temporal inhomogeneity of wake profiles. Considering the anisotropy of wake expansion, the turbulence intensity was modified to avoid repeated experimental calculation of empirical parameters such as wake expansion coefficient. In addition, the super-Gaussian function was used to modify the near wake model, and the modified near wake model was combined with four typical far wake models to obtain four temporal wake models that can express the time-varying characteristics of the wake. The accuracy of the improved four temporal wake models were verified by wind tunnel test, and the relative errors were analyzed. Results show that the prediction errors of the modified two-dimensional temporal wake model are basically within 4%, which is smaller than the other three wake models. The method presented in this paper can effectively describe the spatial distribution and time-varying characteristics of wake expansion, and can provide a reference for optimizing the control strategy of HAWTs.
AB - Previous studies on wake of horizontal-axis wind turbines (HAWTs) mainly focus on the spatial distribution but ignore the time-varying characteristics of the wake profile which highly affect the operation strategy of the downstream HAWTs to ensure the high-efficiency and low-fatigue load. This paper proposed a mathematical method for studying the spatial-temporal inhomogeneity of wake profiles. Considering the anisotropy of wake expansion, the turbulence intensity was modified to avoid repeated experimental calculation of empirical parameters such as wake expansion coefficient. In addition, the super-Gaussian function was used to modify the near wake model, and the modified near wake model was combined with four typical far wake models to obtain four temporal wake models that can express the time-varying characteristics of the wake. The accuracy of the improved four temporal wake models were verified by wind tunnel test, and the relative errors were analyzed. Results show that the prediction errors of the modified two-dimensional temporal wake model are basically within 4%, which is smaller than the other three wake models. The method presented in this paper can effectively describe the spatial distribution and time-varying characteristics of wake expansion, and can provide a reference for optimizing the control strategy of HAWTs.
KW - Super-Gaussian function
KW - Temporal expression
KW - Time-varying characteristics
KW - Wake model
KW - Wind tunnel experiment
UR - http://www.scopus.com/inward/record.url?scp=85150766745&partnerID=8YFLogxK
U2 - 10.1016/j.jweia.2023.105368
DO - 10.1016/j.jweia.2023.105368
M3 - Journal article
AN - SCOPUS:85150766745
SN - 0167-6105
VL - 236
JO - Journal of Wind Engineering and Industrial Aerodynamics
JF - Journal of Wind Engineering and Industrial Aerodynamics
M1 - 105368
ER -