The hidden cost of waves: Mitigating wake-induced power loss in floating wind farms

As the most suitable UK near-shore sites for fixed foundation turbines are taken up, further growth requires moving into deeper waters. Industry estimates suggest that around 80% of global offshore wind resources are located in deep waters where Floating Offshore Wind Turbines (FOWTs) are the only viable solution. This shift presents economic potential, highlighted by the Celtic Sea floating wind farms, which The Crown Estate estimates could add £1.4 billion to the UK economy and support over 5,000 jobs.

Operating in these environments requires deploying large assets featuring turbines exceeding 190 metres in tip-height on moored floating platforms. However, deep ocean conditions introduce operational challenges. As turbines extract wind, they leave behind a turbulent, low energy air shadow called a wake, which can reduce a wind farm’s energy production by 10-20% if not managed through proper FOWT positioning and optimal control. Furthermore, the continuous motion of the floating platform caused by ocean waves and blade pitch control systems displaces and distorts this wake, making it harder to predict and adding to mechanical wear across the entire farm.

This PhD project will combine aerodynamic modelling with optimal control to mitigate wake induced power loss, alleviate structural stress, and inform FOWT positioning. Using computational wind farm datasets and high-fidelity simulations, the researcher will model the wave induced motion of upstream turbines and design algorithms that coordinate turbine yaw angles in real time to steer the air wake safely past trailing turbines. The 50MW Kincardine floating farm off the coast of Aberdeen powers over 50,000 homes. At the scale of the new 1.5GW Celtic Sea farms, recovering even 2% of the energy lost to wakes could power around 25,000 additional households per farm.

This project supports government projections of up to 100,000 jobs in offshore wind by 2030, helping revitalise coastal economies. The student will be allocated office space and continuous support from experts. Weekly meetings with a diverse supervisory team from multiple schools will expand their university network. Access to the Apollo High-Performance Computing facility, and research group CPU and GPU computing resources will support their high-fidelity simulations and control optimisation algorithms.

The programme includes a structured training component delivered through compressed teaching weeks during the six-months, with CDT funding covering travel costs for students based at partner universities. In addition to the essential taught component of the CDT, training will be available from relevant Master’s level modules at Loughborough including content on AI/ML, Model Predictive Control, and Wind Energy.

The student will be supported to publish their findings as first author in leading journals, such as Elsevier’s Renewable Energy and IEEE Control Systems Letters, and to present at international conferences including the IEEE Conference on Decision and Control and The TORQUE conference on wind energy. They will engage with the FOWT community via GitHub to explore introducing their optimisation methods into state-of-the-art simulation tools, namely OpenFAST and FAST.Farm.

The programme includes a structured training component delivered through compressed teaching weeks during the six-months, with CDT funding covering travel costs for students based at partner universities.