IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i4p1705-d1341708.html
   My bibliography  Save this article

The Probability of Ship Collision during the Fully Submerged Towing Process of Floating Offshore Wind Turbines

Author

Listed:
  • Yihong Li

    (Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Longxiang Liu

    (Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Sunwei Li

    (Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Zhen-Zhong Hu

    (Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

Abstract

As global warming intensifies, the development of offshore wind farms is swiftly progressing, especially deep-water Floating Offshore Wind Turbines (FOWTs) capable of energy capture in deep-sea regions, which have emerged as a focal point of both academic and industrial interest. Although numerous researchers have conducted comprehensive and multifaceted studies on various components of wind turbines, less attention has been paid to the operational stage responses of FOWTs to wind, waves, and currents and the reliability of their structural components. This study primarily employs a theoretical analysis to establish mathematical models under a series of reasonable assumptions, examining the possibility of collisions between FOWT transport fleets and other vessels in the passage area during the towing process. Using the model, this paper takes the Wanning Floating Offshore Wind Farm (FOWF) project, which is scheduled to be deployed in the South China Sea, as its research object and calculates the probability of collisions between FOWTs and other vessels in three months from the pier near Wanning, Hainan, to a predetermined position 22 km away. The findings of the analysis indicate that the mathematical model developed in this study integrates the quantities and velocities of navigational vessels within the target maritime area as well as the speeds, routes, and schedules of the FOWT transport fleet. By employing statistical techniques and geometric calculations, the model can determine the frequency of collisions between various types of vessels and the FOWT transport fleet during the transportation period. This has substantial relevance for future risk assessments and disaster prevention and mitigation measures in the context of FOWT transportation.

Suggested Citation

  • Yihong Li & Longxiang Liu & Sunwei Li & Zhen-Zhong Hu, 2024. "The Probability of Ship Collision during the Fully Submerged Towing Process of Floating Offshore Wind Turbines," Sustainability, MDPI, vol. 16(4), pages 1-17, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1705-:d:1341708
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/4/1705/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/4/1705/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Montewka, Jakub & Hinz, Tomasz & Kujala, Pentti & Matusiak, Jerzy, 2010. "Probability modelling of vessel collisions," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 573-589.
    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. Carine Dominguez-Péry & Lakshmi Narasimha Raju Vuddaraju & Isabelle Corbett-Etchevers & Rana Tassabehji, 2021. "Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-32, December.
    2. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    3. Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Suyi Li & Qiang Meng & Xiaobo Qu, 2012. "An Overview of Maritime Waterway Quantitative Risk Assessment Models," Risk Analysis, John Wiley & Sons, vol. 32(3), pages 496-512, March.
    5. Goerlandt, Floris & Montewka, Jakub, 2015. "Maritime transportation risk analysis: Review and analysis in light of some foundational issues," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 115-134.
    6. J Montewka & P Krata & F Goerlandt & A Mazaheri & P Kujala, 2011. "Marine traffic risk modelling – an innovative approach and a case study," Journal of Risk and Reliability, , vol. 225(3), pages 307-322, September.
    7. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Zhang, Weibin & Feng, Xinyu & Goerlandt, Floris & Liu, Qing, 2020. "Towards a Convolutional Neural Network model for classifying regional ship collision risk levels for waterway risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Silveira, P. & Teixeira, A.P. & Figueira, J.R. & Guedes Soares, C., 2021. "A multicriteria outranking approach for ship collision risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Szlapczynski, Rafal & Szlapczynska, Joanna, 2021. "A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    11. Barabadi, Abbas & Tobias Gudmestad, Ove & Barabady, Javad, 2015. "RAMS data collection under Arctic conditions," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 92-99.
    12. Abreu, Danilo T.M.P. & Maturana, Marcos C. & Droguett, Enrique Lopez & Martins, Marcelo R., 2022. "Human reliability analysis of conventional maritime pilotage operations supported by a prospective model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    13. Zyczkowski, Marcin & Szlapczynski, Rafal, 2023. "Collision risk-informed weather routing for sailboats," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Talavera, Alejandro & Aguasca, Ricardo & Galván, Blas & Cacereño, Andrés, 2013. "Application of Dempster–Shafer theory for the quantification and propagation of the uncertainty caused by the use of AIS data," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 95-105.
    15. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2021. "Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    16. Zhang, Mingyang & Kujala, Pentti & Hirdaris, Spyros, 2022. "A machine learning method for the evaluation of ship grounding risk in real operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    17. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Li, Huanhuan & Ekere, Nduka & Yang, Zaili, 2023. "Multi-scale collision risk estimation for maritime traffic in complex port waters," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    18. Gao, Dawei & Zhu, Yongsheng & Yan, Ke & Soares, C. Guedes, 2024. "Deep learning–based framework for regional risk assessment in a multi–ship encounter situation based on the transformer network," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    19. Hänninen, Maria & Kujala, Pentti, 2012. "Influences of variables on ship collision probability in a Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 27-40.
    20. Gao, Dawei & Zhu, Yongsheng & Guedes Soares, C., 2023. "Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process," Reliability Engineering and System Safety, Elsevier, vol. 230(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:jsusta:v:16:y:2024:i:4:p:1705-:d:1341708. 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.