IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2506-d358827.html
   My bibliography  Save this article

Numerical Analysis of the Effect of Offshore Turbulent Wind Inflow on the Response of a Spar Wind Turbine

Author

Listed:
  • Rieska Mawarni Putri

    (Department of Mechanical and Structural Engineering and Material Science, University of Stavanger, 4036 Stavanger, Norway)

  • Charlotte Obhrai

    (Department of Mechanical and Structural Engineering and Material Science, University of Stavanger, 4036 Stavanger, Norway)

  • Jasna Bogunovic Jakobsen

    (Department of Mechanical and Structural Engineering and Material Science, University of Stavanger, 4036 Stavanger, Norway)

  • Muk Chen Ong

    (Department of Mechanical and Structural Engineering and Material Science, University of Stavanger, 4036 Stavanger, Norway)

Abstract

Turbulent wind at offshore sites is known as the main cause for fatigue on offshore wind turbine components. Numerical simulations are commonly used to predict the loads and motions of floating offshore wind turbines; however, the definition of representative wind input conditions is necessary. In this study, the load and motion responses of a spar-type Offshore Code Comparison Collaboration (OC3) wind turbine under different turbulent wind conditions is studied and investigated by using SIMO-Riflex in Simulation Workbench for Marine Applications (SIMA) workbench. Using the two spectral models given in the International Electrotechnical Commission (IEC) standards, it is found that a lower wind lateral coherence under neutral atmospheric stability conditions results in an up to 27% higher tower base side–side bending moment and a 20% higher tower top torsional moment. Comparing different atmospheric stability conditions simulated using a spectral model based on FINO1 wind data measurement, the highest turbulent energy content under very unstable conditions yields a 26% higher tower base side–side bending moment and a 27% higher tower top torsional moment than neutral conditions, which have the lowest turbulent energy content and turbulent intensity. The yaw-mode of the OC3 wind turbine is found to be the most influenced component by assessing variations in both the lateral coherence and the atmospheric stability conditions.

Suggested Citation

  • Rieska Mawarni Putri & Charlotte Obhrai & Jasna Bogunovic Jakobsen & Muk Chen Ong, 2020. "Numerical Analysis of the Effect of Offshore Turbulent Wind Inflow on the Response of a Spar Wind Turbine," Energies, MDPI, vol. 13(10), pages 1-22, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2506-:d:358827
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2506/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/10/2506/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Doubrawa, Paula & Churchfield, Matthew J. & Godvik, Marte & Sirnivas, Senu, 2019. "Load response of a floating wind turbine to turbulent atmospheric flow," Applied Energy, Elsevier, vol. 242(C), pages 1588-1599.
    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. Cheynet, Etienne & Li, Lin & Jiang, Zhiyu, 2024. "Metocean conditions at two Norwegian sites for development of offshore wind farms," Renewable Energy, Elsevier, vol. 224(C).
    2. Navid Belvasi & Frances Judge & Jimmy Murphy & Cian Desmond, 2022. "Analysis of Floating Offshore Wind Platform Hydrodynamics Using Underwater SPIV: A Review," Energies, MDPI, vol. 15(13), pages 1-26, June.

    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, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
    2. Zhou, Yang & Xiao, Qing & Liu, Yuanchuan & Incecik, Atilla & Peyrard, Christophe & Wan, Decheng & Pan, Guang & Li, Sunwei, 2022. "Exploring inflow wind condition on floating offshore wind turbine aerodynamic characterisation and platform motion prediction using blade resolved CFD simulation," Renewable Energy, Elsevier, vol. 182(C), pages 1060-1079.
    3. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
    4. Cai, Yefeng & Zhao, Haisheng & Li, Xin & Liu, Yuanchuan, 2023. "Aerodynamic analysis for different operating states of floating offshore wind turbine induced by pitching movement," Energy, Elsevier, vol. 285(C).
    5. Rizwan Haider & Xin Li & Wei Shi & Zaibin Lin & Qing Xiao & Haisheng Zhao, 2024. "Review of Computational Fluid Dynamics in the Design of Floating Offshore Wind Turbines," Energies, MDPI, vol. 17(17), pages 1-37, August.
    6. Stanislawski, Brooke J. & Thedin, Regis & Sharma, Ashesh & Branlard, Emmanuel & Vijayakumar, Ganesh & Sprague, Michael A., 2023. "Effect of the integral length scales of turbulent inflows on wind turbine loads," Renewable Energy, Elsevier, vol. 217(C).
    7. Ren, Yajun & Shi, Wei & Venugopal, Vengatesan & Zhang, Lixian & Li, Xin, 2024. "Experimental study of tendon failure analysis for a TLP floating offshore wind turbine," Applied Energy, Elsevier, vol. 358(C).
    8. Zi Lin & Xiaolei Liu, 2020. "Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning," Energies, MDPI, vol. 13(9), pages 1-21, May.
    9. Dong, Hongyang & Zhang, Jincheng & Zhao, Xiaowei, 2021. "Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations," Applied Energy, Elsevier, vol. 292(C).
    10. Chen, Yaling & Lin, Binliang & Liang, Dongfang, 2023. "Interactions between approaching flow and hydrokinetic turbines in a staggered layout," Renewable Energy, Elsevier, vol. 218(C).

    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:13:y:2020:i:10:p:2506-:d:358827. 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.