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A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation

Author

Listed:
  • Ibrahim Engin Taze

    (Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA)

  • Md Armanul Hoda

    (Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA)

  • Irene Miquelez

    (Department Engineering, Public University of Navarre, 31006 Pamplona, Spain)

  • Payton Maddaloni

    (Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA)

  • Saeed Eftekhar Azam

    (Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA)

Abstract

This paper presents a comprehensive literature review on the digital twinning of floating offshore wind turbines (FOWTs). In this study, the digital twin (DT) is defined as a dynamic virtual model that accurately mirrors a physical system throughout its lifecycle, continuously updated with real-time data and use simulations, machine learning, and analytics to support informed decision-making. The recent advancements and major issues have been introduced, which need to be addressed before realizing a FOWT DT that can be effectively used for life extension and operation and maintenance planning. This review synthesizes relevant literature reviews focused on modeling FOWT and its specific components along with the latest research. It specifically focuses on the structural, mechanical, and energy production components of FOWTs within the DT framework. The state of the art DT for FOWT, or large scale operational civil and energy infrastructure, is not yet matured to perform real-time update of digital replicas of these systems. The main barriers include real-time coupled modeling with high fidelity, the design of sensor networks, and optimization methods that synergize the sensor data and simulations to calibrate the model. Based on the literature survey provided in this paper, one of the main barriers is uncertainty associated with the external loads applied to FOWT. In this review paper, a robust method for inverse analysis in the absence of load information has been introduced and validated by using simulated experiments. Furthermore, the regulatory requirements have been provided for FOWT life extension and the potential of DT in achieving that.

Suggested Citation

  • Ibrahim Engin Taze & Md Armanul Hoda & Irene Miquelez & Payton Maddaloni & Saeed Eftekhar Azam, 2025. "A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation," Energies, MDPI, vol. 18(13), pages 1-42, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3369-:d:1688545
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    References listed on IDEAS

    as
    1. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    2. Mario O. A. González & Gabriela Nascimento & Dylan Jones & Negar Akbari & Andressa Santiso & David Melo & Rafael Vasconcelos & Monalisa Godeiro & Luana Nogueira & Mariana Almeida & Pedro Oprime, 2024. "Logistic Decisions in the Installation of Offshore Wind Farms: A Conceptual Framework," Energies, MDPI, vol. 17(23), pages 1-20, November.
    3. Li, Yang & Shen, Xiaojun & Zhou, Chongcheng, 2023. "Dynamic multi-turbines spatiotemporal correlation model enabled digital twin technology for real-time wind speed prediction," Renewable Energy, Elsevier, vol. 203(C), pages 841-853.
    4. Ainhoa Pujana & Miguel Esteras & Eugenio Perea & Erik Maqueda & Philippe Calvez, 2023. "Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation," Energies, MDPI, vol. 16(2), pages 1-20, January.
    5. Zhao, Xiang & Dao, My Ha & Le, Quang Tuyen, 2023. "Digital twining of an offshore wind turbine on a monopile using reduced-order modelling approach," Renewable Energy, Elsevier, vol. 206(C), pages 531-551.
    6. Changhyun Kim & Minh-Chau Dinh & Hae-Jin Sung & Kyong-Hwan Kim & Jeong-Ho Choi & Lukas Graber & In-Keun Yu & Minwon Park, 2022. "Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin," Energies, MDPI, vol. 15(17), pages 1-16, August.
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