IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v240y2025ics0960148124022626.html
   My bibliography  Save this article

Influence of thrust coefficient on the wake of a wind turbine: A numerical and analytical study

Author

Listed:
  • Vahidi, Dara
  • Porté-Agel, Fernando

Abstract

In this study, large-eddy simulations (LES) are used to analyze how variations in the thrust coefficient influence the wake of an actuator disk. In a neutrally-stratified turbulent boundary layer, the flow through a stand-alone actuator disk is simulated on flat terrain with two different aerodynamic roughness lengths, using a thrust coefficient varying from 0.4 to 0.9 in 0.1 increments. The simulation results show that the thrust-coefficient variation leads to considerable differences in the wake velocity deficit and added turbulence intensity distributions up to downwind distances of around 12 disk diameters. Moreover, the resulting dataset is used to evaluate the performance of several analytical and empirical wake models for different thrust coefficients. Emphasis is placed on the models for the mean wake velocity deficit, near-wake length, and added turbulence intensity. In general, the models that incorporate physically-based approaches within their framework have an advantage over empirical models derived from a given dataset. The former yields predictions for the wake velocity deficit and near-wake length that are more accurate and robust to the thrust-coefficient variation. For the added turbulence intensity, the predictions of some commonly used models are compared with the LES data, and recommendations for their range of applicability are given.

Suggested Citation

  • Vahidi, Dara & Porté-Agel, Fernando, 2025. "Influence of thrust coefficient on the wake of a wind turbine: A numerical and analytical study," Renewable Energy, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:renene:v:240:y:2025:i:c:s0960148124022626
    DOI: 10.1016/j.renene.2024.122194
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124022626
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.122194?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jian Teng & Corey D. Markfort, 2020. "A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data," Energies, MDPI, vol. 13(14), pages 1-19, July.
    2. Mahdi Abkar & Jens Nørkær Sørensen & Fernando Porté-Agel, 2018. "An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes," Energies, MDPI, vol. 11(7), pages 1-10, July.
    3. 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.
    4. Majid Bastankhah & Fernando Porté-Agel, 2017. "A New Miniature Wind Turbine for Wind Tunnel Experiments. Part II: Wake Structure and Flow Dynamics," Energies, MDPI, vol. 10(7), pages 1-19, July.
    5. Ingrid Neunaber & Michael Hölling & Martin Obligado, 2022. "Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development," Energies, MDPI, vol. 15(22), pages 1-15, November.
    6. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    7. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    8. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
    9. Tristan Revaz & Fernando Porté-Agel, 2021. "Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models," Energies, MDPI, vol. 14(13), pages 1-22, June.
    10. Huang, Ming & Ferreira, Carlos & Sciacchitano, Andrea & Scarano, Fulvio, 2022. "Wake scaling of actuator discs in different aspect ratios," Renewable Energy, Elsevier, vol. 183(C), pages 866-876.
    11. Dara Vahidi & Fernando Porté-Agel, 2022. "A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer," Energies, MDPI, vol. 15(24), pages 1-18, December.
    12. Arslan Salim Dar & Fernando Porté-Agel, 2022. "An Analytical Model for Wind Turbine Wakes under Pressure Gradient," Energies, MDPI, vol. 15(15), pages 1-13, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Souaiby, Marwa & Porté-Agel, Fernando, 2024. "An improved analytical framework for flow prediction inside and downstream of wind farms," Renewable Energy, Elsevier, vol. 225(C).
    2. Dar, Arslan Salim & Porté-Agel, Fernando, 2022. "Wind turbine wakes on escarpments: A wind-tunnel study," Renewable Energy, Elsevier, vol. 181(C), pages 1258-1275.
    3. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    4. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    5. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.
    6. Dara Vahidi & Fernando Porté-Agel, 2022. "A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer," Energies, MDPI, vol. 15(24), pages 1-18, December.
    7. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
    8. Zhang, Ziyu & Huang, Peng & Bitsuamlak, Girma & Cao, Shuyang, 2024. "Large-eddy simulation of upwind-hill effects on wind-turbine wakes and power performance," Energy, Elsevier, vol. 294(C).
    9. Kirchner-Bossi, Nicolas & Porté-Agel, Fernando, 2024. "Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm," Renewable Energy, Elsevier, vol. 220(C).
    10. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    11. Ziyu Zhang & Peng Huang & Haocheng Sun, 2020. "A Novel Analytical Wake Model with a Cosine-Shaped Velocity Deficit," Energies, MDPI, vol. 13(13), pages 1-20, June.
    12. Razi, P. & Ramaprabhu, P. & Tarey, P. & Muglia, M. & Vermillion, C., 2022. "A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions," Renewable Energy, Elsevier, vol. 200(C), pages 1602-1617.
    13. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    14. Mingqiu Liu & Zhichang Liang & Haixiao Liu, 2022. "Numerical Investigations of Wake Expansion in the Offshore Wind Farm Using a Large Eddy Simulation," Energies, MDPI, vol. 15(6), pages 1-19, March.
    15. 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.
    16. Duan, Guiyue & Gattari, Daniele & Porté-Agel, Fernando, 2025. "Theoretical and experimental study on power performance and wake characteristics of a floating wind turbine under pitch motion," Applied Energy, Elsevier, vol. 378(PA).
    17. Li, Li & Wang, Bing & Ge, Mingwei & Huang, Zhi & Li, Xintao & Liu, Yongqian, 2023. "A novel superposition method for streamwise turbulence intensity of wind-turbine wakes," Energy, Elsevier, vol. 276(C).
    18. Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
    19. Pawar, Suraj & Sharma, Ashesh & Vijayakumar, Ganesh & Bay, Chrstopher J. & Yellapantula, Shashank & San, Omer, 2022. "Towards multi-fidelity deep learning of wind turbine wakes," Renewable Energy, Elsevier, vol. 200(C), pages 867-879.
    20. Arslan Salim Dar & Fernando Porté-Agel, 2022. "An Analytical Model for Wind Turbine Wakes under Pressure Gradient," Energies, MDPI, vol. 15(15), pages 1-13, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:240:y:2025:i:c:s0960148124022626. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.