Session: 02-12-01 Digital Twins of Marine Structures 1

Submission Number: 180664

A Physics-Based Digital Twin for Wave Elevation and Seabed Moment Estimation of Offshore Monopiles 

Offshore energy systems such as wind turbines, wave energy converters, and floating oil and gas platforms operate in harsh and variable marine environments. These systems experience complex dynamic loads driven by wind, waves, and currents, which affect their reliability, safety, and long-term performance. Monitoring and predicting these loads is critical for reducing operational and maintenance (O&M) costs and ensuring safe operation. However, the use of physical sensors for continuous load measurement remains challenging and costly due to marine exposure, accessibility constraints, and data reliability issues.

This project investigates preliminary, physics-based digital twin models that aim to estimate key operational quantities relevant to structural health monitoring. The focus is on developing and testing virtual sensing approaches for mooring line tension, sea-state estimation, and sectional loads in floating substructures. These virtual sensors will rely on reduced-order dynamic models and data assimilation techniques to infer unmeasured quantities from commonly available measurements such as accelerations, motions, and control signals. 

The models will be integrated within an open-source digital twin environment designed to facilitate cross-sector applications across offshore wind, marine energy, and oil and gas systems. Their performance will be evaluated using synthetic datasets and, when available, experimental data. The goal is to assess the feasibility, accuracy, and computational efficiency of these preliminary estimations and to identify conditions under which physics-based virtual sensing provides meaningful insights for structural health monitoring.

In parallel, we will explore how such estimations can be linked to early indicators of structural integrity and O&M decision-making, such as fatigue loading, environmental severity, and maintenance scheduling. The outcomes will form the foundation for future digital twin implementations capable of real-time monitoring and reliability assessment, supporting safer and more efficient offshore energy operations through open-source and physics-based methodologies.

Presenting Author: Cameron Brown Goldwind Energy ApS

Presenting Author Biography: Emmanuel Branlard is an associate professor of Mechanical Engineering at the University of Massachusetts (UMass) Amherst, focusing on multiphysics (aero-hydro-servo-elastic) modelling of wind turbines, using mid-to-low fidelity models, and open-source tools such as OpenFAST. Before that, Emmanuel worked at the National Renewable Energy Laboratory, Orsted, and the Technical University of Denmark.

Authors:

Emmanuel Branlard University of Massachusetts Amherst
Jason Jonkman National Renewable Energy Laboratory
Jonah Hanak University of Massachussetts Amherst
Leah Sirkis National Renewable Energy Laboratory
Imad Abdallah RTDT Laboratories AG
Cameron Brown Goldwind Energy ApS