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Modelling of wind turbine wake using large eddy simulation

Author

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  • Sedaghatizadeh, Nima
  • Arjomandi, Maziar
  • Kelso, Richard
  • Cazzolato, Benjamin
  • Ghayesh, Mergen H.

Abstract

In an array of wind turbines, the interaction of the downstream machines with the wakes from the upstream ones results in a reduction in the overall wind farm performance. Turbine wakes are a major source of turbulence which exerts fluctuating loads on the blades of the downstream turbines, resulting in the generation of noise and fatigue of the turbine blades. There are many semi-empirical wind turbine wake models in the literature. This paper, develops a fully numerical model of wind turbine wakes using CFD by means of a Large Eddy Simulation (LES). The new LES model is tested against experimental data, showing very good agreement. The advantages of the LES model compared to the available semi-empirical models in the literature are discussed and it is shown that the LES model is very accurate compared to the conventional semi-empirical wake models usually used in industry. Moreover, the LES model is used as a benchmark to compare the accuracy of these semi-empirical models; it is shown that the model proposed by Jensen can predict the velocity deficit most accurately among the semi-empirical models, while the highest degree of accuracy in the wake expansion is achieved by using the Larsen model.

Suggested Citation

  • Sedaghatizadeh, Nima & Arjomandi, Maziar & Kelso, Richard & Cazzolato, Benjamin & Ghayesh, Mergen H., 2018. "Modelling of wind turbine wake using large eddy simulation," Renewable Energy, Elsevier, vol. 115(C), pages 1166-1176.
    • Handle: RePEc:eee:renene:v:115:y:2018:i:c:p:1166-1176
    DOI: 10.1016/j.renene.2017.09.017
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    References listed on IDEAS

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    1. Luo, Kun & Zhang, Sanxia & Gao, Zhiying & Wang, Jianwen & Zhang, Liru & Yuan, Renyu & Fan, Jianren & Cen, Kefa, 2015. "Large-eddy simulation and wind-tunnel measurement of aerodynamics and aeroacoustics of a horizontal-axis wind turbine," Renewable Energy, Elsevier, vol. 77(C), pages 351-362.
    2. Krogstad, Per-Åge & Eriksen, Pål Egil, 2013. "“Blind test” calculations of the performance and wake development for a model wind turbine," Renewable Energy, Elsevier, vol. 50(C), pages 325-333.
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    Cited by:

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    12. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    13. Minh-Quang Tran & Yi-Chen Li & Chen-Yang Lan & Meng-Kun Liu, 2020. "Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region," Energies, MDPI, vol. 13(24), pages 1-16, December.
    14. Li, Siyi & Robert, Arnaud & Faisal, A. Aldo & Piggott, Matthew D., 2024. "Learning to optimise wind farms with graph transformers," Applied Energy, Elsevier, vol. 359(C).
    15. Liu, Weiqi & Shi, Jian & Chen, Hailong & Liu, Hengxu & Lin, Zi & Wang, Lingling, 2021. "Lagrangian actuator model for wind turbine wake aerodynamics," Energy, Elsevier, vol. 232(C).
    16. Gilani, Hooman Azad & Hoseinzadeh, Siamak & Karimi, Hirou & Karimi, Ako & Hassanzadeh, Amir & Garcia, Davide Astiaso, 2021. "Performance analysis of integrated solar heat pump VRF system for the low energy building in Mediterranean island," Renewable Energy, Elsevier, vol. 174(C), pages 1006-1019.
    17. Hoseinzadeh, Siamak & Astiaso Garcia, Davide & Huang, Lizhen, 2023. "Grid-connected renewable energy systems flexibility in Norway islands’ Decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    18. Arabgolarcheh, Alireza & Jannesarahmadi, Sahar & Benini, Ernesto, 2022. "Modeling of near wake characteristics in floating offshore wind turbines using an actuator line method," Renewable Energy, Elsevier, vol. 185(C), pages 871-887.
    19. Sun, Haiying & Yang, Hongxing, 2020. "Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model," Renewable Energy, Elsevier, vol. 147(P1), pages 192-203.
    20. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.

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