A novel Digital Twin-based Structural Health Monitoring solution for offshore wind turbine platforms

J. García-Espinosa, B. Servan Camas, A. Pastor, D. Di Capua

Abstract

This work introduces an innovative Structural Health Monitoring (SHM) solution for offshore wind platforms, featuring an advanced Digital Twin (DT) built on a fully-coupled aero-servo-hydro-elastic model. Our approach utilizes a detailed Finite Element model of the structure, meeting the requirements of the main assessment/certification standards. The application of the unique Enriched Modal Matrix Reduction technique leads substantially reduces CPU time, enabling near real-time calculations without compromising accuracy compared to the original FE model.

The SHM system is completed with an optimized sensors setup to monitor the most relevant deformation modes. Additionally, it enables precise fine-tuning of the DT model using machine learning, resulting in an accurate Hybrid Analysis Model.

Our modular and flexible DT-based SHM solution can be customized for any offshore wind platform concept, covering substructure, towers, mooring, and umbilicals. The solution is demonstrated through sea trials on Enerocean’s W2Power prototype.

Keywords:

offshore wind
Structural Health Monitoring
Digital Twin

How to cite

J. García-Espinosa, B. Servan Camas, A. Pastor and D. Di Capua, A novel Digital Twin-based Structural Health Monitoring solution for offshore wind turbine platforms, Julio García-Espinosa's Presentations at Congresses and Seminars (2024). 1 URL https://www.scipedia.com/public/Garcia-Espinosa_et_al_2024a

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