Hybrid Physics–AI Digital Twin Framework for Shared Mooring Systems in Deepwater Floating Offshore Wind Farms

Abstract

Floating offshore wind farms (FOWFs) provide access to deepwater wind resources beyond the reach of fixed-bottom technology. However, large-scale deployment of mooring systems remains constrained by high cost, environmental impact, and operational complexity, accounting for up to 30% of total capital expenditure. Although conventional radial layouts are easy to certify, they require numerous anchors and heavy materials, increasing cost and disturbing the seabed. Shared-anchor mooring configurations can achieve substantial material and anchor savings, yet they introduce nonlinear dynamic coupling and fatigue risks that existing design frameworks rarely address. This paper presents a prototype open-source Digital Twin (DT) framework for shared mooring arrays, combining multifidelity physics-based simulations (OpenFAST, MoorPy, FAST. Farm) with AI-driven forecasting. Based on 84 years of hindcast ocean data, long short-term memory (LSTM) neural networks predict real-time tension and fatigue accumulation. The framework demonstrates feasibility through combined simulations, probabilistic extreme-value analysis, and scaled hardware-in-the-loop (HIL) tests. It provides array-level prognostic insight by integrating sensing, anomaly detection, and adaptive decision logic. All simulation code, data sets, and plotting scripts are released openly to ensure transparency and reproducibility. Applied to a 10-turbine case study at the São Miguel (Azores) site, the framework achieved anchor reductions of approximately 40% and material savings of 34% relative to radial baselines, while maintaining API and DNV-compliant safety margins. The AI component provides actionable early warnings for preventive interventions under extreme sea states, extending anomaly lead times by several hours. The main contributions are: i) a prototype, open-source DT framework combining physics-based modeling and AI forecasting for shared moorings; ii) the development of a fatigue-aware anomaly detection and adaptive control scheme; and iii) quantitative evidence that deepwater costs, environmental footprint, and operational risk can all be reduced through intelligent shared-mooring design.

Keywords

Digital twin / Floating offshore wind farms / Shared mooring systems / Anchor sharing / Structural resilience / Lifecycle optimization / Predictive maintenance / AI-driven forecasting / Nonlinear optimization / Renewable energy

References

[1] Alsuwaiyan A, Habib AA, Imoukhuede AB, Omar MO, Maruf MA, Alqarni M, El-Maleh A, Tabbakh A, Felemban M, Azim A. A systematic literature review on vulnerabilities, mitigation techniques, and attacks in fieldprogrammable gate arrays. Arabian Journal for Science and Engineering, 2025, 50(2): 611-641

[2] Athanasopoulos PR. Offshore floating solar with compressed air storage as a baseload power plant for a data center, 2025, Cambrige. Massachusetts Institute of Technology: 1-106

[3] Bailey K, Steinberg C, Davies C, Galibert G, Hidas M, McManus MA, Murphy T, Newton J, Roughan M, Schaeffer A. Coastal mooring observing networks and their data products: recommendations for the next decade. Frontiers in Marine Science, 2019, 6: 180

[4] Battye MI, Perinpanayagam S. Digital twins in fusion energy research: Current state and future directions. IEEE Access, 2025, 13: 75787-75821

[5] Branlard E, Frontin C, Maack J, Laird D. Using intrusive approaches as a step towards accounting for stochasticity in wind turbine design. Journal of Physics: Conference Series, 2024, 2767: 082001

[6] Branlard E, Giardina D, Brown CS. Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations. Wind Energy Science, 2020, 5(3): 1155-1167

[7] Branlard E, Jonkman J, Brown C, Zhang J. A digital-twin solution for floating offshore wind turbines validated using a full-scale prototype. Wind Energy Science Discussions, 2023, 9(1): 1-24

[8] Branlard E, Jonkman J, Dana S, Doubrawa P. A digital twin based on openfast linearizations for real-time load and fatigue estimation. Journal of Physics: Conference Series, 2020, 1618: 022030

[9] Chen L. Integrated energy operation solution customized for floating offshore wind power characteristics, 2024, Scotland. University of Glasgow: 1-207

[10] Chen X, Liu X, Yu X, Li Y, Luo S, Li X. Bayesian-spatial optimization of emergency EV dispatch under multi-hazard disruptions: A behaviorally informed framework for resilient energy support in critical grid nodes. Energies, 2025, 18(17): 4629

[11] Fjeldstad A, Wormsen A. Background for revised fatigue analysis methodology of subsea connectors in DNV-RP-C203. Marine Structures, 2025, 103: 103798

[12] Gräfe M, Pettas V, Dimitrov N, Cheng PW. Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements. Wind Energy Science, 2024, 9(11): 2175-2193

[13] Hall M, Carmo L, Lozon E. Frequency-domain modeling of floating wind arrays with shared mooring lines. Wind Energy Science, 2025, 10: 3027-3043

[14] Homayouni SM, Sousa J, Moreira Marques C. Unlocking the potential of digital twins to achieve sustainability in seaports: the state of practice and future outlook. WMU Journal of Maritime Affairs, 2025, 24(1): 59-98

[15] Hu W, Fang J, Zhang Y, Liu Z, Verma AS, Liu H, Cong F, Tan J. Digital twin of wind turbine surface damage detection based on deep learning aided drone inspection. Renewable Energy, 2025, 241: 122332

[16] Huang WK, Nychka DW, Zhang H. Estimating precipitation extremes using the log-histospline. Environmetrics, 2019, 30(4): 2543

[17] Jamaleddin A, Smith B. Optimization of shared mooring systems for floating offshore wind farms. Journal of Offshore Engineering, 2025, 42(3): 123-145

[18] Jamaleddin N, Faria V, Queiroz A, Gabr M. Optimizing co-located offshore wave and wind energy systems: Feasibility, design, and portfolios assessment. Journal of Cleaner Production, 2025, 523: 146461

[19] Jayasinghe S, Mahmoodian M, Sidiq A, Nanayakkara T, Alavi A, Mazaheri S, Shahrivar F, Sun Z, Setunge S. Innovative digital twin with artificial neural networks for real-time monitoring of structural response: A port structure case study. Ocean Engineering, 2024, 312: 119187

[20] Kaimkuriya A, Sethuraman B, Gupta M. Effect of physical parameters on fatigue life of materials and alloys: A critical review. Technologies, 2024, 12(7): 100

[21] Kandemir E, Liu J, Hasan A. Digital twin-driven dynamic repositioning of floating offshore wind farms. Energy Reports, 2023, 9: 208-214

[22] Klar R, Fredriksson A, Angelakis V. Digital twins for ports: Derived from smart city and supply chain concepts. IEEE Access, 2023, 11: 71777-71799

[23] Ko M, Shafieezadeh A. Robust wind turbine monitoring for digital twin integration: A physics-informed covariance-preserving deep learning approach. Renewable Energy, 2025, 250: 123176

[24] Lee JH, Nam YS, Kim Y, Liu Y, Lee J, Yang H. Real-time digital twin for ship operation in waves. Ocean Engineering, 2022, 266: 112867

[25] Leon-Medina JX, Tibaduiza DA, Parés N, Pozo F. Digital twin technology in wind turbine components: A review. Intelligent Systems with Applications, 2025, 26: 200535

[26] Li M, Wu X, Yue X, Dai X. Investment risk assessment and countermeasure strategies for highway PPP projects in Western China: A dynamic risk accumulation modeling approach. Sustainability, 2025, 17(9): 4200

[27] Li W, Fan H, Cai L, Guo W, Wu Z, Yang P. Digital twindriven proactive-reactive scheduling framework for port multi-equipment under a complex uncertain environment. Simulation Modelling Practice and Theory, 2024, 136: 103011

[28] Liu Y, Ren H. Rapid acquisition method for structural strength evaluation stresses of the ship digital twin model. Ocean Engineering, 2023, 285: 115323

[29] Madusanka NS, Fan Y, Yang S, Xiang X. Digital twin in the maritime domain: A review and emerging trends. Journal of Marine Science and Engineering, 2023, 11(5): 1021

[30] Marino E, Gkantou M, Malekjafarian A, Bali S, Baniotopoulos C, Beeck J, Borg RP, Bruschi N, Cardiff P, Chatzi E, Čudina I, Dinu F, Efthymiou E, Ferri G, Gervásio H, Heng J, Jiang Z, Lenci S, Lukačević I, Manuel L, Meyer A, Méndez-Morales M, Osmanović A, Pakrashi V, Pandit A, Rega G, Skejić D, Tesch L, Ungureanu V, Uzunović T, Verma AS. Offshore renewable energies: A review towards floating modular energy islands—monitoring, loads, modelling and control. Ocean Engineering, 2024, 313: 119251

[31] Marino E, Gkantou M, Malekjafarian A, Bali S, Baniotopoulos C, Beeck J, Borg RP, Bruschi N, Cardiff P, Chatzi E, Čudina I, Dinu F, Efthymiou E, Ferri G, Gervásio H, Heng J, Jiang Z, Lenci S, Lukačević I, Manuel L, Meyer A, Méndez-Morales M, Osmanović A, Pakrashi V, Pandit A, Rega G, Skejić D, Tesch L, Ungureanu V, Uzunović T, Verma AS. Offshore renewable energies: exploring floating modular energy islands—materials, construction technologies, and life cycle assessment. Journal of Ocean Engineering and Marine Energy, 2025, 11: 1157-1182

[32] Mauro F, Kana A. Digital twin for ship life-cycle: A critical systematic review. Ocean Engineering, 2023, 269: 113479

[33] Miller T, Durlik I, Kostecka E, Sokołowska S, Kozlovska P, Zwolak R. Artificial intelligence in maritime cybersecurity: A systematic review of AI-driven threat detection and risk mitigation strategies. Electronics, 2025, 14(9): 1844

[34] Mogra R, Thirugnanasambantham KG, Jain SK, Bairagi V. Bridging physics and intelligence: A comprehensive review on ai-driven multi-physics fem for dynamic systems. International Conference on Interactive Design and Digital Manufacturing (ICIDDM 2K25), 2025, 1-19

[35] Musa NS, Mirza NM, Rafique SH, Abdallah AM, Murugan T. machine learning and deep learning techniques for distributed denial of service anomaly detection in software defined networks— current research solutions. IEEE Access, 2024, 12: 17982-18011

[36] Musial W, Butterfield S, Ram B. Energy from offshore wind. Offshore Technology Conference, 2006, OTC-18355

[37] Neugebauer J, Heilig L, Vo S. Digital twins in the context of seaports and terminal facilities. Flexible Services and Manufacturing Journal, 2024, 36(3): 1-97

[38] Pacheco-Blazquez R, Garcia-Espinosa J, Di Capua D, Pastor Sanchez A. A digital twin for assessing the remaining useful life of offshore wind turbine structures. Journal of Marine Science and Engineering, 2024, 12(4): 573

[39] Pérez L, Rubiato J, Tempel J, Zee T. Development and application of a design framework for shared mooring systems in floating wind farms. Applied Energy, 2021, 285: 116427

[40] Pineda SS, Lee H, Ulloa-Navas MJ, Linville RM, Garcia FJ, Galani K, Engelberg-Cook E, Castanedes MC, Fitzwalter BE, Pregent LJ, Gardashli ME, DeTure M, Vera-Garcia DV, Hucke ATS, Oskarsson BE, Murray ME, Dickson DW, Heiman M, Belzil VV, Kellis M. Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD. Cell, 2024, 187(8): 1971-1989

[41] Poozesh P, Nieto F, Fernández PM, Ríos R, Díaz-Casás V. Biofouling on offshore wind energy structures: Characterization, impacts, mitigation strategies, and future trends. Journal of Marine Science and Engineering, 2025, 13(7): 1363

[42] Rahimpour S, Shahin M, Bauk S. Cybersecurity of power electronic converters in maritime systems: Threat landscape, modeling, and resilient mitigation strategies. 2025 10th International Conference on Smart and Sustainable Technologies (SpliTech), 2025, 1-6

[43] Razi A, Chen X, Li H, Wang H, Russo B, Chen Y, Yu H. Deep learning serves traffic safety analysis: A forward-looking review. IET Intelligent Transport Systems, 2023, 17(1): 22-71

[44] Shahin M, Bertl M, Iman MRH, Ghasempouri T, Sharma R, Shah SA, Draheim D. Significant factors extraction: A combined logistic regression and apriori association rule mining approach. Computer Science On-line Conference, 2024, 295-311

[45] Shahin M, Rahimpour S, Ghasempouri T, Bauk S, Fahringer T, Draheim D. Towards smarter maritime security: Apriori algorithm optimization in serverless environments. Procedia Computer Science, 2025, 257: 452-459

[46] Shahin M, Rahimpour S, Ghasempouri T, Kujalai P, Bauk S. Leveraging large language models for iot applications: A maritime image dataset perspective. 2025 10th International Conference on Smart and Sustainable Technologies (SpliTech), 2025, 1-6

[47] Susino G. Numerical modeling of operation and maintenance costs for floating offshore wind farms in the mediterranean sea, 2025, Turin. Politecnico di Torino

[48] Taghavi M, Perera LP. Advanced data cluster analyses in digital twin development for marine engines towards ship performance quantification. Ocean Engineering, 2024, 298: 117098

[49] Taze IE, Hoda MA, Miquelez I, Maddaloni P, Eftekhar Azam SA. A review of digital twinning applications for floating offshore wind turbines: Insights, innovations, and implementation. Energies, 2025, 18(13): 3369

[50] Tian Z, Shi W, Li X, Park Y, Jiang Z, Wu J. Numerical simulations of floating offshore wind turbines with shared mooring under current-only conditions. Renewable Energy, 2025, 238: 121918

[51] Toader C, Tataru CP, Munteanu O, Serban M, Covache-Busuioc RA, Ciurea AV, Enyedi M. Decoding neurodegeneration: a review of molecular mechanisms and therapeutic advances in Alzheimer’s, Parkinson’s, and ALS. International Journal of Molecular Sciences, 2024, 25(23): 12613

[52] Vasconcelos V, Paula Barros G, Soares WA, Costa ACL. Risk-informed decision-making: Overview and applications. Reliability Modeling in Industry, 2023, 4: 381-405

[53] Walker J, Coraddu A, Collu M, Oneto L. Digital twins of the mooring line tension for floating offshore wind turbines to improve monitoring, lifespan, and safety. Journal of Ocean Engineering and Marine Energy, 2022, 8: 1-16

[54] Wan C, Liu Y, Lin W, Li Y, Wu Y. Fatigue reliability analysis of mooring lines for floating offshore wind turbines using probability density evolution method. Ocean Engineering, 2020, 202: 107110

[55] Wang S, Guedes Soares C. Statistical characterization on slamming and green water impact onto a chemical tanker in extreme sea conditions. Marine Structures, 2025, 103: 103818

[56] Wei Q, Liu Y, Dong Y, Li T, Li WA. Digital twin framework for real time ship routing considering decarbonization regulatory compliance. Ocean Engineering, 2023, 278: 114407

[57] Wilson S, Hall M, Housner S, Sirnivas S. Linearized modeling and optimization of shared mooring systems. Ocean Engineering, 2021, 241: 110009

[58] Xia J, Zou G. Operation and maintenance optimization of offshore wind farms based on digital twin: A review. Ocean Engineering, 2023, 268: 113322

[59] Yang J, Moan T, Jiang Z, Shu Q. A real-time digital twin framework for mooring integrity management of floating offshore wind turbines. Ocean Engineering, 2022, 258: 111840

[60] Yang Y, Amann C, Varfolomeev I, Gumbsch P, Kadau K. Probabilistic gas turbine rotor disk forging flaw crack nucleation model based on experimental data and plasticity-corrected stress intensity factor. Proceedings of the ASME Turbo Expo 2025: Turbomachinery Technical Conference and Exposition, 2025, V008T25A001

[61] Yang Y, Lin Y, Ning D. Numerical investigation on novel group-drag-anchor systems for soft clay seabed. Results in Engineering, 2025, 27: 106367

[62] Yazdi ANP, Benisi AH, Manzari MT. A novel, fast, physics-based, and efficient design method for radial turbine rotors. Energy Conversion and Management, 2025, 332: 119664

[63] Zhang D, Zhu Z, Chen S, Zhang C, Lu X, Zhang X, Zhang X, Davidson MR. Spatially resolved land and grid model of carbon neutrality in China. Proceedings of the National Academy of Sciences, 2024, 121(10): 2306517121

[64] Zhou C, Xu J, Miller-Hooks E, Zhou W, Chen CH, Lee LH, Chew EP, Li H. Analytics with digital-twinning: A decision support system for maintaining a resilient port. Decision Support Systems, 2021, 143: 113496

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