Dr Nina Dethlefs
FF2021 -1049 - Physics -informed machine learning for rapid fatigue assessments in offshore wind farms
People Involved
Dr Agota Mockute
Project Description
14 UK offshore wind farms (1.2 GW) are approaching their designed lifetime by 2030, and around 700 monopiles are to be decommissioned every five years onwards. Understanding the actual accumulated fatigue and the ability to predict fatigue risk in the future are critical for well-informed design, maintenance and end-of-life decisions. However, significant wave-induced fatigue is omitted with industry-standard wave loading methods as the needed fully nonlinear wave loading models are too time-consuming for numerous realisations needed for fatigue estimation in the stochastic offshore environment. To answer this urgent need, this project will develop a reliable and industry-compatible grey-box fatigue risk model for monopile-supported offshore wind turbines. It will for the first time incorporate nonlinear wave loading in aero-hydro-servo-elastic simulations with machine learning models. The developed grey-box model will aid the prediction of fatigue accumulation at any point of the wind turbine’s lifetime, feeding into the maintenance optimisation and remaining useful life assessment methods, allowing to optimise the offshore wind sector and achieve net zero carbon goals.
| Status | Project Complete |
|---|---|
| Value | £99,915.00 |
| Project Dates | Jun 1, 2021 - Oct 6, 2022 |
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