IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i13p2624-d246586.html
   My bibliography  Save this article

New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence

Author

Listed:
  • Takanori Uchida

    (Research Institute for Applied Mechanics (RIAM), Kyushu University, 6-1 Kasuga-kouen, Kasuga, Fukuoka 816-8580, Japan)

  • Yasushi Kawashima

    (West Japan Engineering Consultants, Inc., Denki Building Kyosokan 7F, 2-1-82 Watanabe-dori, Chuo-ku, Fukuoka 810-0004, Japan)

Abstract

The present study scrutinized the impacts of terrain-induced turbulence on wind turbine blades, examining measurement data regarding wind conditions and the strains of wind turbine blades. Furthermore, we performed a high-resolution large-eddy simulation (LES) and identified the three-dimensional airflow structures of terrain-induced turbulence. Based on the LES results, we defined the Uchida-Kawashima Scale_1 (the U-K scale_1), which is a turbulence evaluation index, and clarified the existence of the terrain-induced turbulence quantitatively. The threshold value of the U-K scale_1 was determined as 0.2, and this index was confirmed to not be dependent on the inflow profile, the influence of the horizontal grid resolution, and the influence of the computed azimuth. In addition, we defined the Uchida-Kawashima Scale_2 (the U-K scale_2), which is a fatigue damage evaluation index based on the measurement data and the design value obtained by DNV GL’s Bladed. DNV GL (Det Norske Veritas Germanischer Lloyed) is a third party certification body in Norway, and Bladed has been the industry standard aero-elastic wind turbine modeling software. Using the U-K scale_2, the following results were revealed: the U-K scale_2 was 0.86 < 1.0 (within the designed value) in the case of northerly wind, and the U-K scale_2 was 1.60 > 1.0 (exceeding the designed value) in the case of easterly wind. As a result, it was revealed that the blades of the target wind turbine were directly and strongly affected by terrain-induced turbulence when easterly winds occurred.

Suggested Citation

  • Takanori Uchida & Yasushi Kawashima, 2019. "New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence," Energies, MDPI, vol. 12(13), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2624-:d:246586
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/13/2624/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/13/2624/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dhunny, A.Z. & Lollchund, M.R. & Rughooputh, S.D.D.V., 2017. "Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD)," Renewable Energy, Elsevier, vol. 101(C), pages 1-9.
    2. Takanori Uchida, 2018. "Computational Fluid Dynamics (CFD) Investigation of Wind Turbine Nacelle Separation Accident over Complex Terrain in Japan," Energies, MDPI, vol. 11(6), pages 1-13, June.
    3. Takanori Uchida, 2018. "LES Investigation of Terrain-Induced Turbulence in Complex Terrain and Economic Effects of Wind Turbine Control," Energies, MDPI, vol. 11(6), pages 1-15, June.
    4. Tang, Xiao-Yu & Zhao, Shumian & Fan, Bo & Peinke, Joachim & Stoevesandt, Bernhard, 2019. "Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts," Applied Energy, Elsevier, vol. 238(C), pages 806-815.
    5. Takanori Uchida & Susumu Takakuwa, 2019. "A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain," Energies, MDPI, vol. 12(10), pages 1-19, May.
    6. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    7. Simões, Teresa & Estanqueiro, Ana, 2016. "A new methodology for urban wind resource assessment," Renewable Energy, Elsevier, vol. 89(C), pages 598-605.
    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. Takanori Uchida, 2020. "Effects of Inflow Shear on Wake Characteristics of Wind-Turbines over Flat Terrain," Energies, MDPI, vol. 13(14), pages 1-31, July.
    2. Takanori Uchida & Yoshihiro Taniyama & Yuki Fukatani & Michiko Nakano & Zhiren Bai & Tadasuke Yoshida & Masaki Inui, 2020. "A New Wind Turbine CFD Modeling Method Based on a Porous Disk Approach for Practical Wind Farm Design," Energies, MDPI, vol. 13(12), pages 1-27, June.
    3. Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, September.
    4. Takanori Uchida, 2019. "Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain Using High-Resolution Elevation Data and Surface Roughness Data Constructed with a Drone," Energies, MDPI, vol. 12(19), pages 1-20, October.
    5. Takanori Uchida & Tadasuke Yoshida & Masaki Inui & Yoshihiro Taniyama, 2021. "Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach," Energies, MDPI, vol. 14(8), pages 1-33, April.
    6. Takanori Uchida & Kenichiro Sugitani, 2020. "Numerical and Experimental Study of Topographic Speed-Up Effects in Complex Terrain," Energies, MDPI, vol. 13(15), pages 1-38, July.
    7. Takanori Uchida & Susumu Takakuwa, 2020. "Numerical Investigation of Stable Stratification Effects on Wind Resource Assessment in Complex Terrain," Energies, MDPI, vol. 13(24), pages 1-32, December.

    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. Takanori Uchida & Kenichiro Sugitani, 2020. "Numerical and Experimental Study of Topographic Speed-Up Effects in Complex Terrain," Energies, MDPI, vol. 13(15), pages 1-38, July.
    2. Takanori Uchida, 2019. "Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain Using High-Resolution Elevation Data and Surface Roughness Data Constructed with a Drone," Energies, MDPI, vol. 12(19), pages 1-20, October.
    3. Takanori Uchida & Susumu Takakuwa, 2019. "A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain," Energies, MDPI, vol. 12(10), pages 1-19, May.
    4. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.
    5. Sarah Jamal Mattar & Mohammad Reza Kavian Nezhad & Michael Versteege & Carlos F. Lange & Brian A. Fleck, 2021. "Validation Process for Rooftop Wind Regime CFD Model in Complex Urban Environment Using an Experimental Measurement Campaign," Energies, MDPI, vol. 14(9), pages 1-19, April.
    6. Takanori Uchida & Susumu Takakuwa, 2020. "Numerical Investigation of Stable Stratification Effects on Wind Resource Assessment in Complex Terrain," Energies, MDPI, vol. 13(24), pages 1-32, December.
    7. Akintayo T. Abolude & Wen Zhou, 2018. "A Comparative Computational Fluid Dynamic Study on the Effects of Terrain Type on Hub-Height Wind Aerodynamic Properties," Energies, MDPI, vol. 12(1), pages 1-14, December.
    8. Takanori Uchida, 2018. "Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain by Large-Eddy Simulation (LES) Technique," Energies, MDPI, vol. 11(10), pages 1-15, October.
    9. Yang, Lin & Rojas, Jose I. & Montlaur, Adeline, 2020. "Advanced methodology for wind resource assessment near hydroelectric dams in complex mountainous areas," Energy, Elsevier, vol. 190(C).
    10. Cheng, Xu & Yan, Bowen & Zhou, Xuhong & Yang, Qingshan & Huang, Guoqing & Su, Yanwen & Yang, Wei & Jiang, Yan, 2024. "Wind resource assessment at mountainous wind farm: Fusion of RANS and vertical multi-point on-site measured wind field data," Applied Energy, Elsevier, vol. 363(C).
    11. Ismail Kamdar & Shahid Ali & Juntakan Taweekun & Hafiz Muhammad Ali, 2021. "Wind Farm Site Selection Using WAsP Tool for Application in the Tropical Region," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    12. Yan, Bowen & Shen, Ruifang & Li, Ke & Wang, Zhenguo & Yang, Qingshan & Zhou, Xuhong & Zhang, Le, 2023. "Spatio-temporal correlation for simultaneous ultra-short-term wind speed prediction at multiple locations," Energy, Elsevier, vol. 284(C).
    13. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    14. KC, Anup & Whale, Jonathan & Urmee, Tania, 2019. "Urban wind conditions and small wind turbines in the built environment: A review," Renewable Energy, Elsevier, vol. 131(C), pages 268-283.
    15. Arteaga-López, Ernesto & Angeles-Camacho, César, 2021. "Innovative virtual computational domain based on wind rose diagrams for micrositing small wind turbines," Energy, Elsevier, vol. 220(C).
    16. Takanori Uchida, 2018. "Computational Fluid Dynamics Approach to Predict the Actual Wind Speed over Complex Terrain," Energies, MDPI, vol. 11(7), pages 1-14, June.
    17. Gürdal Ertek & Lakshmi Kailas, 2021. "Analyzing a Decade of Wind Turbine Accident News with Topic Modeling," Sustainability, MDPI, vol. 13(22), pages 1-34, November.
    18. Daniel Tabas & Jiannong Fang & Fernando Porté-Agel, 2019. "Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics," Energies, MDPI, vol. 12(7), pages 1-12, April.
    19. Koichi Watanabe & Yuji Ohya & Takanori Uchida, 2019. "Power Output Enhancement of a Ducted Wind Turbine by Stabilizing Vortices around the Duct," Energies, MDPI, vol. 12(16), pages 1-17, August.
    20. Li, Jiale & Wang, Xuefei & Yu, Xiong (Bill), 2018. "Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment," Applied Energy, Elsevier, vol. 213(C), pages 469-485.

    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:12:y:2019:i:13:p:2624-:d:246586. 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.