Transatlantic Collaboration

Tufts University, the University of Rhode Island, the Norwegian Geotechnical Institute, the National Renewable Energy Laboratory, and the UK's Offshore Renewable Energy Catapult have assembled an international team of researchers to advance OWT digital twin technology based on measurements from an array of OWTs at the Block Island Wind Farm (BIWF), the Coastal Virginia Offshore Wind (CVOW) Pilot Project, and the North Sea.
This work has been funded by the U.S. Bureau of Safety and Environmental Enforcement (BSEE), the Rhode Island Coastal Resources Management Council, the National Offshore Wind Research and Development Consortium (NOWRDC) and the Massachusetts Clean Energy Center (MassCEC), and the National Science Foundation, and Innovate UK. It has been conducted in partnership with Ørsted, GE, Dominion Energy, and Siemens.
Recent Scholarship
Structural instrumentation and monitoring of the Block Island Offshore Wind Farm
Renewable Energy, 2023
System identification and finite element model updating of a 6 MW offshore wind turbine
Renewable Energy, 2023
(Photo credit: Chris Baxter, 2023)
Sensitivity analysis of modal parameters of a jacket offshore wind turbine in operational conditions
Journal of Marine Science and Engineering, 2023
One year monitoring of an offshore wind turbine
Engineering Structures, 2023
Joint parameter-input estimation for digital twinning of the Block Island wind turbine using output-only measurements
Mechanical Systems and Signal Processing, 2023
(Photo credit: Gerrit Wolken-Moehlmann)
Inverse modeling of wind turbine drivetrain from numerical data using Bayesian inference
Renewable and Sustainable Energy Reviews, 2023
Joint parameter-input estimation for virtual sensing on an offshore platform using output-only measurements
Mechanical Systems and Signal Processing, 2022
(Photo credit: Gerrit Wolken-Moehlmann)
Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost
Mechanical Systems and Signal Processing, 2022