IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v164y2021icp346-361.html
   My bibliography  Save this article

Dynamic effects of inertial particles on the wake recovery of a model wind turbine

Author

Listed:
  • Smith, Sarah E.
  • Travis, Kristin N.
  • Djeridi, Henda
  • Obligado, Martín
  • Cal, Raúl Bayoán

Abstract

Impacting particles such as rain, dust, and other debris can have devastating structural effects on wind turbines, but little is known about the interaction of such debris within turbine wakes. This study aims to characterize behavior of inertial particles within the turbulent wake of a wind turbine and relative effects on wake recovery. Here a model wind turbine is subjected to varied two-phase inflow conditions, with wind as the carrier fluid (Reλ=49−88) and polydisperse water droplets (18-40 μm in diameter) at varied concentrations (Φv=0.24×10−5 - 1.3×10−5), comparing with sub-inertial particles [i.e., tracers] that follow the inflow streamlines. Phase doppler interferometry (PDI) and particle image velocimetry (PIV) were employed at multiple downstream locations, centered with respect to turbine hub height. Analysis considers energy and particle size distribution within the wake focusing on turbulence statistics and preferential concentration. PDI data show droplet size varied with wake location and volume fraction, and the inflow velocity of Reλ=66.58 demonstrated Φv dependent increases in streamwise velocity deficits of 59.5%–62.6% and 15.8%–19.8% for near and far wake, respectively. PIV data indicated correlation of particle concentration to wake expansion and amplified downward trajectory over the entire interrogation field. Contributions to kinetic energy and momentum are diminished overall for inertial particle cases compared to single-phase, except turbulent momentum flux u’v’¯, where shearing effects are visible at the rotor top edge in near wake and concentrated magnitudes increase in far wake correlating with increased Φv. Application of Voronoi analysis identifies clustering behavior in far wake and is validated as motivation for future studies.

Suggested Citation

  • Smith, Sarah E. & Travis, Kristin N. & Djeridi, Henda & Obligado, Martín & Cal, Raúl Bayoán, 2021. "Dynamic effects of inertial particles on the wake recovery of a model wind turbine," Renewable Energy, Elsevier, vol. 164(C), pages 346-361.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:346-361
    DOI: 10.1016/j.renene.2020.09.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120314579
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.09.037?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. Rockel, Stanislav & Peinke, Joachim & Hölling, Michael & Cal, Raúl Bayoán, 2016. "Wake to wake interaction of floating wind turbine models in free pitch motion: An eddy viscosity and mixing length approach," Renewable Energy, Elsevier, vol. 85(C), pages 666-676.
    2. Ali, Naseem & Cal, Raúl Bayoán, 2019. "Scale evolution, intermittency and fluctuation relations in the near-wake of a wind turbine array," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 215-229.
    3. 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)

    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. Zhang, Baoshou & Song, Baowei & Mao, Zhaoyong & Tian, Wenlong, 2017. "A novel wake energy reuse method to optimize the layout for Savonius-type vertical axis wind turbines," Energy, Elsevier, vol. 121(C), pages 341-355.
    2. 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.
    3. 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).
    4. 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.
    5. Li, B. & Zhou, D.L. & Wang, Y. & Shuai, Y. & Liu, Q.Z. & Cai, W.H., 2020. "The design of a small lab-scale wind turbine model with high performance similarity to its utility-scale prototype," Renewable Energy, Elsevier, vol. 149(C), pages 435-444.
    6. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    7. Wang, Xinbao & Cai, Chang & Cai, Shang-Gui & Wang, Tengyuan & Wang, Zekun & Song, Juanjuan & Rong, Xiaomin & Li, Qing'an, 2023. "A review of aerodynamic and wake characteristics of floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    8. 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.
    9. Majid Bastankhah & Fernando Porté-Agel, 2017. "A New Miniature Wind Turbine for Wind Tunnel Experiments. Part I: Design and Performance," Energies, MDPI, vol. 10(7), pages 1-19, July.
    10. Jae-ho Jeong & Kwangtae Ha, 2020. "Evaluation of Wind Flow Characteristics by RANS-Based Numerical Site Calibration (NSC) Method with Met-Tower Measurements and Its Application to a Complex Terrain," Energies, MDPI, vol. 13(19), pages 1-16, October.
    11. 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.
    12. Karina Soto-Rivas & David Richter & Cristian Escauriaza, 2019. "A Formulation of the Thrust Coefficient for Representing Finite-Sized Farms of Tidal Energy Converters," Energies, MDPI, vol. 12(20), pages 1-17, October.
    13. C. A. Lopez-Villalobos & O. Rodriguez-Hernandez & R. Campos-Amezcua & Guillermo Hernandez-Cruz & O. A. Jaramillo & J. L. Mendoza, 2018. "Wind Turbulence Intensity at La Ventosa, Mexico: A Comparative Study with the IEC61400 Standards," Energies, MDPI, vol. 11(11), pages 1-19, November.
    14. Viggiano, Bianca & Sakradse, Greg & Smith, Sarah & Mungin, Rihana & Ramasubramanian, Pradeep & Ringle, Dan & Travis, Kristin & Ali, Naseem & Solovitz, Stephen & Cal, Raúl Bayoán, 2021. "Intermittent event evaluation through a multifractal approach for variable density jets," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    15. Dai, Xuan & Xu, Da & Zhang, Mengqi & Stevens, Richard J.A.M., 2022. "A three-dimensional dynamic mode decomposition analysis of wind farm flow aerodynamics," Renewable Energy, Elsevier, vol. 191(C), pages 608-624.
    16. 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).
    17. Shuolin Xiao & Di Yang, 2019. "Large-Eddy Simulation-Based Study of Effect of Swell-Induced Pitch Motion on Wake-Flow Statistics and Power Extraction of Offshore Wind Turbines," Energies, MDPI, vol. 12(7), pages 1-17, April.
    18. Böhme, Gustavo S. & Fadigas, Eliane A. & Gimenes, André L.V. & Tassinari, Carlos E.M., 2018. "Wake effect measurement in complex terrain - A case study in Brazilian wind farms," Energy, Elsevier, vol. 161(C), pages 277-283.
    19. 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.
    20. Tanmoy Chatterjee & Yulia T. Peet, 2020. "Dynamics of Large Scale Turbulence in Finite-Sized Wind Farm Canopy Using Proper Orthogonal Decomposition and a Novel Fourier-POD Framework," Energies, MDPI, vol. 13(7), pages 1-25, April.

    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:164:y:2021:i:c:p:346-361. 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.