IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p923-d103521.html
   My bibliography  Save this article

A New Miniature Wind Turbine for Wind Tunnel Experiments. Part II: Wake Structure and Flow Dynamics

Author

Listed:
  • Majid Bastankhah

    (Wind Engineering and Renewable Energy Laboratory (WIRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland)

  • Fernando Porté-Agel

    (Wind Engineering and Renewable Energy Laboratory (WIRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland)

Abstract

An optimized three-bladed horizontal-axis miniature wind turbine, called WiRE-01, with the rotor diameter of 15 cm is designed and fully characterized in Part I of this study. In the current part of the study, we investigate the interaction of the turbine with a turbulent boundary layer. The comparison of the spectral density of the thrust force and the one of the incoming velocity revealed new insights on the use of turbine characteristics to estimate incoming flow conditions. High-resolution stereoscopic particle image-velocimetry (S-PIV) measurements were also performed in the wake of the turbine operating at optimal conditions. Detailed information on the velocity and turbulence structure of the turbine wake is presented and discussed, which can serve as a complete dataset for the validation of numerical models. The PIV data are also used to better understand the underlying mechanisms leading to unsteady loads on a downstream turbine at different streamwise and spanwise positions. To achieve this goal, a new method is developed to quantify and compare the effect of both turbulence and mean shear on the moment of the incoming momentum flux for a hypothetical turbine placed downstream. The results show that moment fluctuations caused by turbulence are bigger under full-wake conditions, whereas those caused by mean shear are clearly dominant under partial-wake conditions. Especial emphasis is also placed on how the mean wake flow distribution is affected by wake meandering. Conditional averaging based on the instantaneous position of the wake center revealed that when the wake meanders laterally to one side, a high-speed region exists on the opposite side. The results show that, due to this high-speed region, large lateral meandering motions do not lead to the expansion of the mean wake cross-section in the lateral direction.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:923-:d:103521
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/923/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/7/923/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Sina Shamsoddin & Fernando Porté-Agel, 2016. "A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer," Energies, MDPI, vol. 9(5), pages 1-23, May.
    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. 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.
    5. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    6. 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.
    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. Francesco Mazzeo & Derek Micheletto & Alessandro Talamelli & Antonio Segalini, 2022. "An Experimental Study on a Wind Turbine Rotor Affected by Pitch Imbalance," Energies, MDPI, vol. 15(22), pages 1-16, November.
    2. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2021. "Wind speed modeling for cascade clusters of wind turbines Part 2: Wind speed reduction and aggregation superposition," Energy, Elsevier, vol. 215(PB).
    3. Xiaolei Yang & Fotis Sotiropoulos, 2019. "A Review on the Meandering of Wind Turbine Wakes," Energies, MDPI, vol. 12(24), pages 1-20, December.
    4. 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).
    5. Öztürk, Buğrahan & Hassanein, Abdelrahman & Akpolat, M Tuğrul & Abdulrahim, Anas & Perçin, Mustafa & Uzol, Oğuz, 2023. "On the wake characteristics of a model wind turbine and a porous disc: Effects of freestream turbulence intensity," Renewable Energy, Elsevier, vol. 212(C), pages 238-250.
    6. Ingrid Neunaber & Michael Hölling & Richard J. A. M. Stevens & Gerard Schepers & Joachim Peinke, 2020. "Distinct Turbulent Regions in the Wake of a Wind Turbine and Their Inflow-Dependent Locations: The Creation of a Wake Map," Energies, MDPI, vol. 13(20), pages 1-20, October.
    7. Rosario Lanzafame & Stefano Mauro & Michele Messina & Sebastian Brusca, 2020. "Development and Validation of CFD 2D Models for the Simulation of Micro H-Darrieus Turbines Subjected to High Boundary Layer Instabilities," Energies, MDPI, vol. 13(21), pages 1-23, October.
    8. Dongqin Zhang & Yang Liang & Chao Li & Yiqing Xiao & Gang Hu, 2022. "Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation," Energies, MDPI, vol. 15(19), pages 1-26, October.
    9. Zygmunt Szczerba & Piotr Szczerba & Kamil Szczerba & Marek Szumski & Krzysztof Pytel, 2023. "Wind Tunnel Experimental Study on the Efficiency of Vertical-Axis Wind Turbines via Analysis of Blade Pitch Angle Influence," Energies, MDPI, vol. 16(13), pages 1-21, June.
    10. 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).
    11. 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.
    12. Tristan Revaz & Mou Lin & Fernando Porté-Agel, 2020. "Numerical Framework for Aerodynamic Characterization of Wind Turbine Airfoils: Application to Miniature Wind Turbine WiRE-01," Energies, MDPI, vol. 13(21), pages 1-18, October.
    13. Yu-Ting Wu & Chang-Yu Lin & Che-Ming Hsu, 2020. "An Experimental Investigation of Wake Characteristics and Power Generation Efficiency of a Small Wind Turbine under Different Tip Speed Ratios," Energies, MDPI, vol. 13(8), pages 1-19, April.
    14. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    15. Huilai Ren & Xiaodong Zhang & Shun Kang & Sichao Liang, 2018. "Actuator Disc Approach of Wind Turbine Wake Simulation Considering Balance of Turbulence Kinetic Energy," Energies, MDPI, vol. 12(1), pages 1-19, December.
    16. Mou Lin & Fernando Porté-Agel, 2019. "Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models," Energies, MDPI, vol. 12(23), pages 1-18, November.
    17. Nikolaos Chrysochoidis-Antsos & Gerard J.W. van Bussel & Jan Bozelie & Sander M. Mertens & Ad J.M. van Wijk, 2021. "Performance Characteristics of A Micro Wind Turbine Integrated on A Noise Barrier," Energies, MDPI, vol. 14(5), pages 1-29, February.
    18. 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.

    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, Ziyu & Huang, Peng, 2023. "Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model," Renewable Energy, Elsevier, vol. 219(P1).
    2. 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).
    3. 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.
    4. 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.
    5. Ingrid Neunaber & Michael Hölling & Richard J. A. M. Stevens & Gerard Schepers & Joachim Peinke, 2020. "Distinct Turbulent Regions in the Wake of a Wind Turbine and Their Inflow-Dependent Locations: The Creation of a Wake Map," Energies, MDPI, vol. 13(20), pages 1-20, October.
    6. 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).
    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. 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.
    9. Mou Lin & Fernando Porté-Agel, 2019. "Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models," Energies, MDPI, vol. 12(23), pages 1-18, November.
    10. Pankaj K. Jha & Earl P. N. Duque & Jessica L. Bashioum & Sven Schmitz, 2015. "Unraveling the Mysteries of Turbulence Transport in a Wind Farm," Energies, MDPI, vol. 8(7), pages 1-29, June.
    11. Chen, Guang & Li, Xiao-Bai & Liang, Xi-Feng, 2022. "IDDES simulation of the performance and wake dynamics of the wind turbines under different turbulent inflow conditions," Energy, Elsevier, vol. 238(PB).
    12. Fan, Xiantao & Ge, Mingwei & Tan, Wei & Li, Qi, 2021. "Impacts of coexisting buildings and trees on the performance of rooftop wind turbines: An idealized numerical study," Renewable Energy, Elsevier, vol. 177(C), pages 164-180.
    13. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
    14. 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.
    15. 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.
    16. 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).
    17. 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).
    18. 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).
    19. 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.
    20. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.

    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:10:y:2017:i:7:p:923-:d:103521. 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.