Developing reduced-order models for long-term wave loading to enable accurate fatigue estimation in offshore wind turbines

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience; a partnership between the Universities of Durham, Hull, Loughborough and Sheffield. The scholarship is co-funded, and co-supervised by, HR Wallingford. HR Wallingford are a global expert in water-related challenges, providing research, consultancy, and physical and computational modelling that supports the offshore wind sector internationally.

The studentship funds four years full time study (part time options available), including: six-months of multi-disciplinary training, delivered by the University of Hull; and a research programme, based between Loughborough University and HR Wallingford. Offshore wind energy is a cornerstone of the European Union’s energy strategy. Europe continues to deliver new wind projects, adding 2.6 GW of offshore capacity in 2024, with a projection to triple capacity by 2030.

This doctoral studentship is partnered with industry to directly tackle a critical sector challenge: continuous offshore infrastructure operations. The studentship will address the challenge of accurately predicting the fatigue life of offshore wind turbines, a critical issue in both deployment and long-term maintenance. A key aspect of this work involves resolving the complex, long-term interaction of wave and wind loads, which generate combined stress cycles and contribute to material fatigue.

This interaction requires advanced coupled simulation models that account for both wind and wave dynamics to ensure reliable fatigue predictions. Current methodologies have well-documented limitations in accurately capturing the effects of complex flow scenarios and nonlinear drag dominated flow regimes on structural loads. By leveraging advanced computational fluid dynamics models and utilising real sea state data from major offshore wind farm locations, this project aims to provide a step change is our ability to predict fatigue life.

The student will develop new datasets and apply machine learning-based analytical tools that offer value across the wind energy sector. The outcomes are expected to enhance the long-term economic viability of offshore wind turbines globally. Training and development You will benefit from a taught programme, giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs.

This begins with an intensive six-month programme at the University of Hull for the new student intake, drawing on the expertise and facilities of all four academic partners. It is supplemented by Continuing Professional Development (CPD), which is embedded throughout your 4-year research scholarship. The successful applicant will have the opportunity to undertake CDT funded placements at HR Wallingford during their PhD programme.

These include extended collaboration with their industry supervisor and potential use of industry accredited test facilities. Beyond academia, these placements open pathways to a ca