Real-Time Digital Twin for Structural Health Monitoring of Floating Offshore Wind Turbines

Guardado en:

Detalles Bibliográficos
Publicado en:	Journal of Marine Science and Engineering vol. 13, no. 10 (2025), p. 1953-1979
Autor principal:	Pastor-Sanchez, Andres
Otros Autores:	Garcia-Espinosa, Julio, Di Capua Daniel, Servan-Camas Borja, Berdugo-Parada Irene
Publicado:	MDPI AG
Materias:	Scotland United Kingdom > UK Deep water Finite element method Offshore Wind power Reduced order models Deep learning Internet of Things Inspection Estimates Steel structures Adaptive control Physics Computer applications Submersibles Semisubmersible platforms Submersible platforms Stresses Structural health monitoring Turbines Simulation Materials fatigue Digital twins Sensors Fluid-structure interaction Mathematical models Wind turbines Composite structures Real time Floating Embedding Decision making Structural dynamics Turbine engines Environmental
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Resumen:	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.
ISSN:	2077-1312
DOI:	10.3390/jmse13101953
Fuente:	Engineering Database