Articles | Volume 9, issue 1
https://doi.org/10.5194/wes-9-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jan 2024
Research article |
| 08 Jan 2024
| 08 Jan 2024
A digital twin solution for floating offshore wind turbines validated using a full-scale prototype
Emmanuel Branlard
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO 80401, USA
Jason Jonkman
National Renewable Energy Laboratory, Golden, CO 80401, USA
Cameron Brown
Stiesdal Offshore A/S, Copenhagen, Denmark
Jiatian Zhang
Stiesdal Offshore A/S, Copenhagen, Denmark
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- Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review H. Soltani Motlagh et al. https://doi.org/10.3390/eng6060105
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. https://doi.org/10.1088/1742-6596/2767/3/032026
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- Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review B. Chen et al. https://doi.org/10.3390/en17081964
- Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies A. Oliveira-Filho et al. https://doi.org/10.3390/en18010059
- Digital Twins for Clean Energy Systems: A State-of-the-Art Review of Applications, Integrated Technologies, and Key Challenges M. Kim et al. https://doi.org/10.3390/su18010043
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- Blockchain-based intelligent equipment assessment in manufacturing industry T. Ahanger et al. https://doi.org/10.1007/s10845-025-02621-5
- Considerations for the global commercialization of floating offshore wind energy A. Robertson et al. https://doi.org/10.1038/s44359-025-00093-7
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- Artificial Intelligence in Floating Offshore Wind Turbines: A Critical Review of Applications in Design, Monitoring, Control, and Digital Twins E. Kostecka et al. https://doi.org/10.3390/en18225937
- Technology qualification plan for very large wind turbine blades A. Kolios et al. https://doi.org/10.1016/j.egyr.2026.109704
- Insights into corrosion-fatigue deterioration in offshore wind turbine structures through digital twin applications Y. Ali et al. https://doi.org/10.1016/j.oceaneng.2025.123514
- A physics-informed autoencoder for digital twin development in offshore wind turbine installation D. Dunton et al. https://doi.org/10.1007/s00158-026-04368-w
- A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation I. Taze et al. https://doi.org/10.3390/en18133369
46 citations as recorded by crossref.
- Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics M. Zhao et al. https://doi.org/10.1016/j.ymssp.2025.112544
- Workflow for real-time simulation-based fatigue assessment in mobile machinery A. Nemov et al. https://doi.org/10.1177/16878132261424717
- Computational fluid dynamics and digital twins for wind turbines: A review R. Cockcroft & B. Thornber https://doi.org/10.1016/j.apenergy.2026.127890
- Design of Virtual Sensors for a Pyramidal Weathervaning Floating Wind Turbine H. del Pozo Gonzalez et al. https://doi.org/10.3390/jmse13081411
- Multi-response prediction and tension warning for a dual-rotor TLP wind turbine W. Zha et al. https://doi.org/10.1016/j.oceaneng.2026.128670
- A Joint Method on Dynamic States Estimation for Digital Twin of Floating Offshore Wind Turbines H. Xie et al. https://doi.org/10.3390/jmse13101981
- Wave energy converter survivability and lessons from offshore wind: a critical review K. Ruttanawijit & Y. Tiaple https://doi.org/10.1093/ooenergy/oiag011
- Design of large-scale floating offshore wind turbines: A review of advances and challenges Q. Gu et al. https://doi.org/10.1016/j.energy.2026.142644
- Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review H. Soltani Motlagh et al. https://doi.org/10.3390/eng6060105
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. https://doi.org/10.1088/1742-6596/2767/3/032026
- Health Monitoring of Offshore Wind Structures: Sensing Technology, Uncertainty, and Artificial Intelligence R. Li et al. https://doi.org/10.3390/s26154697
- Digital Twins for Offshore Wind Operation and Maintenance: A Review X. Hu et al. https://doi.org/10.1002/we.70153
- Offshore Field Demonstration of a 1:4-Scale 20-MW VolturnUS+ Floating Wind Barge in the Gulf of Maine J. Dagher et al. https://doi.org/10.1088/1742-6596/3224/8/082035
- Digital Twin-Based Approach for a Multi-Objective Optimal Design of Wind Turbine Gearboxes C. Llopis-Albert et al. https://doi.org/10.3390/math12091383
- Offshore Wind in the Energy Transition: A Comparative Analysis of Floating and Bottom-Fixed Technologies L. Villani et al. https://doi.org/10.3390/en19020487
- SCADA-Based Offshore Wind Turbine Monitoring: A Review of Methods of Addressing Marine Environmental Challenges K. Wrzask et al. https://doi.org/10.2478/pomr-2025-0061
- Model Updating of an Offshore Wind Turbine Support Structure Based on Modal Identification and Bayesian Inference C. Yu et al. https://doi.org/10.3390/jmse13122354
- Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review B. Chen et al. https://doi.org/10.3390/en17081964
- Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies A. Oliveira-Filho et al. https://doi.org/10.3390/en18010059
- Digital Twins for Clean Energy Systems: A State-of-the-Art Review of Applications, Integrated Technologies, and Key Challenges M. Kim et al. https://doi.org/10.3390/su18010043
- Floating offshore wind sector development in the mediterranean: Economic, employment and social analysis D. Vespasiano et al. https://doi.org/10.1016/j.energy.2026.140389
- POD-based sparse stochastic estimation of dynamic wind turbine blade deflections L. Schena et al. https://doi.org/10.1016/j.jsv.2026.119738
- Physics-driven and data-driven hybrid digital twin framework for methanol fuel supply system based on dynamic Bayesian network S. Liu et al. https://doi.org/10.1016/j.apenergy.2026.128822
- Recent Advances in Industrial Sensor Reliability Modeling: A Systematic Literature Review for Risk-Aware Decision-Making Under Global Supply Chain Disruptions S. Lumbantobing et al. https://doi.org/10.1109/ACCESS.2026.3733538
- Blockchain-based intelligent equipment assessment in manufacturing industry T. Ahanger et al. https://doi.org/10.1007/s10845-025-02621-5
- Considerations for the global commercialization of floating offshore wind energy A. Robertson et al. https://doi.org/10.1038/s44359-025-00093-7
- A wind turbine digital shadow for complex inflow conditions H. Hoghooghi & C. Bottasso https://doi.org/10.5194/wes-11-373-2026
- Enhancing Smart Grid Security and Efficiency: AI, Energy Routing, and T&D Innovations (A Review) H. Ishfaq et al. https://doi.org/10.3390/en18174747
- On the modeling errors of digital twins for load monitoring and fatigue assessment in wind turbine drivetrains F. Mehlan & A. Nejad https://doi.org/10.5194/wes-10-417-2025
- Multiphysics surrogate-based model updating of a jacket-supported offshore wind turbine using measured vibration and SCADA data N. Partovi-Mehr et al. https://doi.org/10.3389/fenrg.2026.1847071
- Offshore wind turbine tower design and optimization: A review and AI-driven future directions J. Alves Ribeiro et al. https://doi.org/10.1016/j.apenergy.2025.126294
- Artificial Intelligence-Driven Digital Twin for Disease Progression Prediction: A Comprehensive Review A. A. & Z. Nemer https://doi.org/10.26437/wtghzb40
- A review of digital twin methods and applications for bearings under marine operating constraints J. Shi et al. https://doi.org/10.1080/17445302.2026.2684687
- Real-Time Digital Twin for Structural Health Monitoring of Floating Offshore Wind Turbines A. Pastor-Sanchez et al. https://doi.org/10.3390/jmse13101953
- Assessment of multi-fidelity hydrodynamic models of floating wind turbines in extreme wave events J. Fontaine et al. https://doi.org/10.1016/j.renene.2025.124639
- Towards better fatigue predictions in large offshore wind turbines: A review of climate, aero-hydrodynamic, and computational approaches G. Mangia et al. https://doi.org/10.1016/j.joes.2026.02.009
- Vibration of blades and towers in large offshore and floating wind turbines: Mechanisms, modelling credibility and mitigation pathways Z. Zuo et al. https://doi.org/10.1016/j.rser.2026.117246
- Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford https://doi.org/10.5194/wes-9-2039-2024
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- Digital twins of wind turbine structures in the era of artificial intelligence: Evolution, applications, and future perspectives T. Zheng et al. https://doi.org/10.1016/j.rser.2026.117434
- Artificial Intelligence in Floating Offshore Wind Turbines: A Critical Review of Applications in Design, Monitoring, Control, and Digital Twins E. Kostecka et al. https://doi.org/10.3390/en18225937
- Technology qualification plan for very large wind turbine blades A. Kolios et al. https://doi.org/10.1016/j.egyr.2026.109704
- Insights into corrosion-fatigue deterioration in offshore wind turbine structures through digital twin applications Y. Ali et al. https://doi.org/10.1016/j.oceaneng.2025.123514
- A physics-informed autoencoder for digital twin development in offshore wind turbine installation D. Dunton et al. https://doi.org/10.1007/s00158-026-04368-w
- A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation I. Taze et al. https://doi.org/10.3390/en18133369
Saved (final revised paper)
Latest update: 01 Nov 2026
Short summary
In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The article present methods to obtain reduced-order models of floating wind turbines. The models are used to form a digital twin which combines measurements from the TetraSpar prototype (a full-scale floating offshore wind turbine) to estimate signals that are not typically measured.
In this work, we implement, verify, and validate a physics-based digital twin solution applied...
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