Phd Studentship: Optimising LCOE for gigawatt scale offshore wind farms using high fidelity wake models

UK only

This 4-year PhD project is fully funded and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

Funds to support this studentship project will be delivered through an EPSRC iCASE (Industrial Doctoral Landscape Award) 2026 in collaboration with Arup Group Ltd.

We recommend that you apply early as the advert may be removed before the deadline.

Optimising wind turbine layout and foundation design is critical to enhancing the economics of large-scale offshore wind energy projects. Prediction of the localised turbulent wind conditions onset to each turbine location is a key input to determine turbine energy yield, operation point and foundation costs. However, such predictions are complicated by disturbances to the mean- and unsteady-flow introduced by the presence of other turbines. This issue is addressed within the current generation of design codes through simplified analytical or empirical models. It is known that these can produce conservative outcomes. Improved prediction tools offer the opportunity for increased confidence in energy yield forecasts and reduction of capital costs. As wind turbines increase in size and wind farms move towards multi-gigawatt scale, use of advanced flow models has potential to unlock critical savings in the design process. This project will allow this potential to be explored and quantified, making use of University of Manchester's existing world class high fidelity modelling frameworks to provide detailed characterisations of design conditions for large scale turbine arrays. The developed models and understanding will be used alongside standard flow models and as input to structural design models to quantify the potential impact on key drivers of offshore wind farm economics. Specifically the impact of advanced wake models on wind farm energy yield forecasts, hence revenue, and turbine foundation design, and costs will be assessed.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

To apply, please contact the main supervisor, Prof Tim Stallard - Tim.Stallard@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

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