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Effect of different atmospheric boundary layers on the wake characteristics of NREL phase VI wind turbine

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  • Syed Ahmed Kabir, Ijaz Fazil
  • Ng, E.Y.K.

Abstract

In this study, the interaction of horizontal axis wind turbine (HAWT) with neutrally stratified atmospheric boundary layer (ABL) and its wake characteristics are investigated. Important wake characteristics of wind turbine such as velocity deficit and turbulence level are analyzed. For this purpose, Unsteady Reynolds-Averaged Navier-Stokes (URANS) using k-ε turbulence closure models are performed using commercial Computational Fluid Dynamics (CFD) software called ANSYS FLUENT. Full rotor CFD simulations of the NREL Phase VI wind turbine by virtually placing on a flat surface with different aerodynamic roughness lengths are performed. Discussions on effective modelling of horizontal homogeneity for the undisturbed ABL is included. The influence of inflow ABL's turbulence level in the wake velocity recovery and the ground effect on the wake turbulence intensity (TI) is analyzed. In addition, comparison of rotor aerodynamics of wind turbine in different terrains is performed using pressure coefficient distributions. Finally, the necessity of inclusion of TI recovery in addition to velocity recovery in the wake for the wind farm alignment is discussed.

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  • Syed Ahmed Kabir, Ijaz Fazil & Ng, E.Y.K., 2019. "Effect of different atmospheric boundary layers on the wake characteristics of NREL phase VI wind turbine," Renewable Energy, Elsevier, vol. 130(C), pages 1185-1197.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:1185-1197
    DOI: 10.1016/j.renene.2018.08.083
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    References listed on IDEAS

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    1. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
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    5. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
    6. Syed Ahmed Kabir, Ijaz Fazil & Ng, E.Y.K., 2017. "Insight into stall delay and computation of 3D sectional aerofoil characteristics of NREL phase VI wind turbine using inverse BEM and improvement in BEM analysis accounting for stall delay effect," Energy, Elsevier, vol. 120(C), pages 518-536.
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    Cited by:

    1. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Zhang, Dongqin & Liu, Zhenqing & Li, Weipeng & Hu, Gang, 2023. "LES simulation study of wind turbine aerodynamic characteristics with fluid-structure interaction analysis considering blade and tower flexibility," Energy, Elsevier, vol. 282(C).
    3. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    4. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Xu Ning & Decheng Wan, 2019. "LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics," Sustainability, MDPI, vol. 11(24), pages 1-26, December.
    6. N. Aravindhan & M. P. Natarajan & S. Ponnuvel & P.K. Devan, 2023. "Recent developments and issues of small-scale wind turbines in urban residential buildings- A review," Energy & Environment, , vol. 34(4), pages 1142-1169, June.
    7. Shantanu Purohit & Ijaz Fazil Syed Ahmed Kabir & E. Y. K. Ng, 2021. "On the Accuracy of uRANS and LES-Based CFD Modeling Approaches for Rotor and Wake Aerodynamics of the (New) MEXICO Wind Turbine Rotor Phase-III," Energies, MDPI, vol. 14(16), pages 1-26, August.
    8. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    9. Syed Ahmed Kabir, Ijaz Fazil & Safiyullah, Ferozkhan & Ng, E.Y.K. & Tam, Vivian W.Y., 2020. "New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows," Energy, Elsevier, vol. 193(C).
    10. Ji, Baifeng & Zhong, Kuanwei & Xiong, Qian & Qiu, Penghui & Zhang, Xu & Wang, Liang, 2022. "CFD simulations of aerodynamic characteristics for the three-blade NREL Phase VI wind turbine model," Energy, Elsevier, vol. 249(C).
    11. Tian, Linlin & Song, Yilei & Xiao, Pengcheng & Zhao, Ning & Shen, Wenzhong & Zhu, Chunling, 2022. "A new three-dimensional analytical model for wind turbine wake turbulence intensity predictions," Renewable Energy, Elsevier, vol. 189(C), pages 762-776.
    12. Santo, G. & Peeters, M. & Van Paepegem, W. & Degroote, J., 2019. "Dynamic load and stress analysis of a large horizontal axis wind turbine using full scale fluid-structure interaction simulation," Renewable Energy, Elsevier, vol. 140(C), pages 212-226.
    13. Mustafa Kaya, 2019. "A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades," Sustainability, MDPI, vol. 11(16), pages 1-25, August.

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