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   <dc:title>Real-time digital twin for structural health monitoring of floating offshore wind turbines</dc:title>
   <dc:creator>Pastor Sánchez, Andrés</dc:creator>
   <dc:creator>García Espinosa, Julio</dc:creator>
   <dc:creator>Capua, Daniel di</dc:creator>
   <dc:creator>Serván Camas, Borja</dc:creator>
   <dc:creator>Berdugo Parada, Irene</dc:creator>
   <dc:subject>Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors</dc:subject>
   <dc:subject>Digital twin</dc:subject>
   <dc:subject>Floating offshore wind turbine</dc:subject>
   <dc:subject>IoT platform</dc:subject>
   <dc:subject>Reduced-order models (ROMs)</dc:subject>
   <dc:subject>Modal response amplitude operators (MRAOs)</dc:subject>
   <dc:subject>Real-time structural response</dc:subject>
   <dc:subject>Fatigue analysis</dc:subject>
   <dcterms:abstract>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.</dcterms:abstract>
   <dcterms:abstract>Postprint (published version)</dcterms:abstract>
   <dcterms:issued>2025-10-01</dcterms:issued>
   <dc:type>Article</dc:type>
   <dc:relation>https://www.mdpi.com/2077-1312/13/10/1953</dc:relation>
   <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
   <dc:rights>Open Access</dc:rights>
   <dc:rights>Attribution 4.0 International</dc:rights>
   <dc:publisher>Multidisciplinary Digital Publishing Institute (MDPI)</dc:publisher>
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