2026-04-13T02:02:14Zhttps://recercat.cat/oai/request
oai:recercat.cat:2117/4487842026-01-29T02:22:16Zcom_2072_1033col_2072_452950
RECERCAT author Pastor Sánchez, Andrés author García Espinosa, Julio author Capua, Daniel di author Serván Camas, Borja author Berdugo Parada, Irene 2025-10-01 Digital twins (DTs) offer significant promise for condition-based maintenance of floating offshore wind turbines (FOWTs); however, existing solutions typically compromise either on physical rigor or real-time computational performance. This paper presents a real-time DT framework that resolves this trade-off by embedding a hydro-elastic reduced-order model (ROM) that accurately captures structural dynamics and fluid–structure interaction. Integrated in a cloud-ready Internet of Things architecture, the ROM reconstructs full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness. Validation on the 5 MW OC4-DeepCWind semi-submersible platform shows that the ROM reproduces finite-element (FEM) displacements and stresses with relative errors below 1%. A three-hour load case is solved in 0.69 min for displacements and 3.81 min for stresses on a consumer-grade NVIDIA RTX 4070 Ti GPU—over two orders of magnitude faster than the full FEM model—while one million fatigue stress histories (1000 hotspots × 1000 operating scenarios) are processed in 37 min. This efficiency enables continuous structural monitoring, rapid *what-if* assessments and timely decision-making for targeted inspections and adaptive control. By effectively combining physics-based reduced-order modeling with high-throughput computation, the proposed framework overcomes key barriers to DT deployment: computational overhead, physical fidelity and scalability. Although demonstrated on a steel platform, the approach is readily extensible to composite structures and multi-turbine arrays, providing a robust foundation for cost-effective and reliable deep-water wind-energy operations.Postprint (published version) http://creativecommons.org/licenses/by/4.0/ Open Access Attribution 4.0 International Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors Digital twin Floating offshore wind turbine IoT platform Reduced-order models (ROMs) Modal response amplitude operators (MRAOs) Real-time structural response Fatigue analysis Real-time digital twin for structural health monitoring of floating offshore wind turbines Article