IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p93-d125064.html
   My bibliography  Save this article

On the Evolution of the Integral Time Scale within Wind Farms

Author

Listed:
  • Huiwen Liu

    (College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China
    Mechanical Science and Engineering, University of Illinois, Urbana, IL 61801, USA)

  • Imran Hayat

    (Aerospace Engineering, University of Illinois, Urbana, IL 61801, USA)

  • Yaqing Jin

    (Mechanical Science and Engineering, University of Illinois, Urbana, IL 61801, USA)

  • Leonardo P. Chamorro

    (Mechanical Science and Engineering, University of Illinois, Urbana, IL 61801, USA
    Aerospace Engineering, University of Illinois, Urbana, IL 61801, USA
    Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801, USA)

Abstract

A wind-tunnel investigation was carried out to characterize the spatial distribution of the integral time scale ( T u ) within, and in the vicinity of, two model wind farms. The turbine arrays were placed over a rough wall and operated under high turbulence. The two layouts consisted of aligned units distinguished only by the streamwise spacing ( Δ x T ) between the devices, set at five and ten rotor diameters d T (or S x = Δ x T / d T = 5 and 10). They shared the same spanwise spacing between turbines of 2.5 d T ; this resulted in arrays of 8 × 3 and 5 × 3 horizontal-axis turbines. Hotwire anemometry was used to characterize the instantaneous velocity at various vertical and transverse locations along the central column of the wind farms. Results show that T u was modulated by the wind farm layout. It was significantly reduced within the wind farms and right above them, where the internal boundary layer develops. The undisturbed levels above the wind farms were recovered only at ≈ d T / 2 above the top tip. This quantity appeared to reach adjusted values starting the fifth row of turbines in the S x = 5 wind farm, and earlier in the S x = 10 counterpart. Within the adjusted zone, the distribution of T u at hub height exhibited a negligible growth in the S x = 5 case; whereas it underwent a mild growth in the S x = 10 wind farm. In addition, the flow impinging the inner turbines exhibited T u / T i n c u < 1 , where T i n c u is the integral time scale of the overall incoming flow. Specifically, T u → β T i n c u at z = z h u b , where β < 1 within standard layouts of wind farms, in particular β ≈ 0.5 and 0.7 for S x = 5 and 10.

Suggested Citation

  • Huiwen Liu & Imran Hayat & Yaqing Jin & Leonardo P. Chamorro, 2018. "On the Evolution of the Integral Time Scale within Wind Farms," Energies, MDPI, vol. 11(1), pages 1-11, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:93-:d:125064
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/93/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/93/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    2. Nicolas Tobin & Ali M. Hamed & Leonardo P. Chamorro, 2015. "An Experimental Study on the Effects ofWinglets on the Wake and Performance of a ModelWind Turbine," Energies, MDPI, vol. 8(10), pages 1-18, October.
    3. Yaqing Jin & Huiwen Liu & Rajan Aggarwal & Arvind Singh & Leonardo P. Chamorro, 2016. "Effects of Freestream Turbulence in a Model Wind Turbine Wake," Energies, MDPI, vol. 9(10), pages 1-12, October.
    4. Leonardo P. Chamorro & Fernando Porté-Agel, 2011. "Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study," Energies, MDPI, vol. 4(11), pages 1-21, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fu, Shifeng & Zhang, Buen & Zheng, Yuan & Chamorro, Leonardo P., 2020. "In-phase and out-of-phase pitch and roll oscillations of model wind turbines within uniform arrays," Applied Energy, Elsevier, vol. 269(C).
    2. Zhang, Yuquan & Zang, Wei & Zheng, Jinhai & Cappietti, Lorenzo & Zhang, Jisheng & Zheng, Yuan & Fernandez-Rodriguez, E., 2021. "The influence of waves propagating with the current on the wake of a tidal stream turbine," Applied Energy, Elsevier, vol. 290(C).
    3. Shyuan Cheng & Yaqing Jin & Leonardo P. Chamorro, 2020. "Wind Turbines with Truncated Blades May Be a Possibility for Dense Wind Farms," Energies, MDPI, vol. 13(7), pages 1-13, April.
    4. Emmanuvel Joseph Aju & Dhanush Bhamitipadi Suresh & Yaqing Jin, 2020. "The Influence of Winglet Pitching on the Performance of a Model Wind Turbine: Aerodynamic Loads, Rotating Speed, and Wake Statistics," Energies, MDPI, vol. 13(19), pages 1-15, October.
    5. Xinkai Li & Ke Yang & Hao Hu & Xiaodong Wang & Shun Kang, 2019. "Effect of Tailing-Edge Thickness on Aerodynamic Noise for Wind Turbine Airfoil," Energies, MDPI, vol. 12(2), pages 1-25, January.
    6. Zheng, Yidan & Liu, Huiwen & Chamorro, Leonardo P. & Zhao, Zhenzhou & Li, Ye & Zheng, Yuan & Tang, Kexin, 2023. "Impact of turbulence level on intermittent-like events in the wake of a model wind turbine," Renewable Energy, Elsevier, vol. 203(C), pages 45-55.
    7. Buen Zhang & Shyuan Cheng & Fanghan Lu & Yuan Zheng & Leonardo P. Chamorro, 2020. "Impact of Topographic Steps in the Wake and Power of a Wind Turbine: Part A—Statistics," Energies, MDPI, vol. 13(23), pages 1-14, December.
    8. Nicolas Tobin & Adam Lavely & Sven Schmitz & Leonardo P. Chamorro, 2019. "Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability," Energies, MDPI, vol. 12(8), pages 1-12, April.
    9. Liang, Xiaoling & Fu, Shifeng & Cai, Fulin & Han, Xingxing & Zhu, Weijun & Yang, Hua & Shen, Wenzhong, 2023. "Experimental investigation on wake characteristics of wind turbine and a new two-dimensional wake model," Renewable Energy, Elsevier, vol. 203(C), pages 373-381.
    10. Zhenzhou Shao & Ying Wu & Li Li & Shuang Han & Yongqian Liu, 2019. "Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes," Energies, MDPI, vol. 12(4), pages 1-14, February.

    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. Hayat, Imran & Chatterjee, Tanmoy & Liu, Huiwen & Peet, Yulia T. & Chamorro, Leonardo P., 2019. "Exploring wind farms with alternating two- and three-bladed wind turbines," Renewable Energy, Elsevier, vol. 138(C), pages 764-774.
    2. Liang, Xiaoling & Fu, Shifeng & Cai, Fulin & Han, Xingxing & Zhu, Weijun & Yang, Hua & Shen, Wenzhong, 2023. "Experimental investigation on wake characteristics of wind turbine and a new two-dimensional wake model," Renewable Energy, Elsevier, vol. 203(C), pages 373-381.
    3. Zehtabiyan-Rezaie, Navid & Abkar, Mahdi, 2024. "An extended k−ɛ model for wake-flow simulation of wind farms," Renewable Energy, Elsevier, vol. 222(C).
    4. Zhang, Buen & Jin, Yaqing & Cheng, Shyuan & Zheng, Yuan & Chamorro, Leonardo P., 2022. "On the dynamics of a model wind turbine under passive tower oscillations," Applied Energy, Elsevier, vol. 311(C).
    5. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    6. Shyuan Cheng & Mahmoud Elgendi & Fanghan Lu & Leonardo P. Chamorro, 2021. "On the Wind Turbine Wake and Forest Terrain Interaction," Energies, MDPI, vol. 14(21), pages 1-13, November.
    7. Emmanuvel Joseph Aju & Dhanush Bhamitipadi Suresh & Yaqing Jin, 2020. "The Influence of Winglet Pitching on the Performance of a Model Wind Turbine: Aerodynamic Loads, Rotating Speed, and Wake Statistics," Energies, MDPI, vol. 13(19), pages 1-15, October.
    8. Zhang, Ziyu & Huang, Peng, 2023. "Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model," Renewable Energy, Elsevier, vol. 219(P1).
    9. Zhaobin Li & Xiaohao Liu & Xiaolei Yang, 2022. "Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes," Energies, MDPI, vol. 15(18), pages 1-28, September.
    10. Shyuan Cheng & Yaqing Jin & Leonardo P. Chamorro, 2020. "Wind Turbines with Truncated Blades May Be a Possibility for Dense Wind Farms," Energies, MDPI, vol. 13(7), pages 1-13, April.
    11. Buen Zhang & Shyuan Cheng & Fanghan Lu & Yuan Zheng & Leonardo P. Chamorro, 2020. "Impact of Topographic Steps in the Wake and Power of a Wind Turbine: Part A—Statistics," Energies, MDPI, vol. 13(23), pages 1-14, December.
    12. Xiaolei Yang & Daniel Foti & Christopher Kelley & David Maniaci & Fotis Sotiropoulos, 2020. "Wake Statistics of Different-Scale Wind Turbines under Turbulent Boundary Layer Inflow," Energies, MDPI, vol. 13(11), pages 1-17, June.
    13. 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).
    14. Stevens, Richard J.A.M. & Martínez-Tossas, Luis A. & Meneveau, Charles, 2018. "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments," Renewable Energy, Elsevier, vol. 116(PA), pages 470-478.
    15. 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).
    16. Mattuella, J.M.L. & Loredo-Souza, A.M. & Oliveira, M.G.K. & Petry, A.P., 2016. "Wind tunnel experimental analysis of a complex terrain micrositing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 110-119.
    17. Kale, Baris & Buckingham, Sophia & van Beeck, Jeroen & Cuerva-Tejero, Alvaro, 2023. "Comparison of the wake characteristics and aerodynamic response of a wind turbine under varying atmospheric conditions using WRF-LES-GAD and WRF-LES-GAL wind turbine models," Renewable Energy, Elsevier, vol. 216(C).
    18. Yaqing Jin & Huiwen Liu & Rajan Aggarwal & Arvind Singh & Leonardo P. Chamorro, 2016. "Effects of Freestream Turbulence in a Model Wind Turbine Wake," Energies, MDPI, vol. 9(10), pages 1-12, October.
    19. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    20. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).

    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:gam:jeners:v:11:y:2018:i:1:p:93-:d:125064. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.