Abstract

This study develops and verifies two advanced wake models based on super-Gaussian distribution: three-dimensional (3D) super-Gaussian (3DSG) and 3D anisotropic super-Gaussian (3DASG) models. They have a smooth Gaussian–top-hat shape (a combination of Gaussian and top-hat shapes) in the near-wake region that gradually transitions to a Gaussian shape in the far-wake region. These models are based on the law of mass conservation and considers wind shear effect; hence, they can accurately describe asymmetric wind distribution in the vertical direction. Because of this Gaussian–top-hat shape, the model is more accurate in simulating the wake in the near-wake region. The anisotropic model also considers different wake expansion rates in various dimensions, rendering the model more realistic. The accuracy and generality of the two models are verified using four wake data sets obtained from wind tunnel tests and wind field measurements. The validation includes the prediction of the wake profile and relative error of the models. The results show that the two models can well predict the wake distribution of various sizes of turbines at any spatial location in the full-wake region.

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Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was financially supported by National Natural Science Foundation of China (NSFC) (Grant Nos. 52069010 and 52369017) and Applied Basic Research Key Project of Yunnan (202401AS070058).

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Go to Journal of Energy Engineering
Journal of Energy Engineering
Volume 150Issue 4August 2024

History

Received: Oct 19, 2023
Accepted: Mar 14, 2024
Published online: Jun 10, 2024
Published in print: Aug 1, 2024
Discussion open until: Nov 10, 2024

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Professor, Dept. of Energy and Power Engineering, Kunming Univ. of Science and Technology, Kunming, Yunnan 650093, China. Email: [email protected]
Graduate Student, Dept. of Energy and Power Engineering, Kunming Univ. of Science and Technology, Kunming, Yunnan 650093, China (corresponding author). ORCID: https://orcid.org/0009-0004-1315-2908. Email: [email protected]
Professor, Dept. of Engineering Mechanics, Kunming Univ. of Science and Technology, Kunming, Yunnan 650500, China. ORCID: https://orcid.org/0000-0002-9270-9257. Email: [email protected]
Xiaoxu Zhang [email protected]
Doctoral Student, Dept. of Energy and Power Engineering, Kunming Univ. of Science and Technology, Kunming, Yunnan 650093, China. Email: [email protected]
Graduate Student, School of Energy and Environment, Inner Mongolia Univ. of Science and Technology, Baotou, Inner Mongolia 014010, China. Email: [email protected]

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