Large-scale Wind Farm Optimisation: Fully Funded PhD Studentship in Modelling and Optimisation of Large-Scale Wind Turbine Arrays

Large-scale wind turbines, specifically located offshore, present very specific technical challenges. Structural design and optimisation of performance in energy conversion are dependent on a correct estimation of the effects of flow, but scale and interaction between devices have a significant impact.  

In this project you will work on and develop numerical modelling capabilities for arrays of turbines with non-homogeneous flows and wake effects. The modelling technology will be leveraged to develop optimisation techniques for the operation of wind turbine arrays.  

The specific challenges will include understanding and modelling turbine wakes and their effects, understanding the interaction mechanisms and producing a reduced order model deployable for multiple devices.  

In this project, you will work with the Zienkiewicz Institute for Modelling, Data and AI and the Dyson Institute. The work is expected to result in publishable research works and funding is available to attend conferences. To maximise the impact of the work, the work is anticipated to be made available as open-source software (subject to intellectual property review).  

The work is a collaboration with the Dyson Institute, sponsored by a UKRI IDLA Award. You will receive academic supervision from Swansea University and the Dyson Institute of Engineering and Technology. The position includes a minimum six-month industrial placement at Dyson, offering experience in both academic research and industry-focused R&D environments. This project also offers opportunities to engage in teaching activities and mentoring, both in Swansea University and Dyson Institute providing a comprehensive training experience that prepares the student for successful academic and industrial careers. 

To be successful, the applicants are expected to demonstrate a strong interest in flow modelling techniques and experience in programming.  

Proficiency in clear scientific writing and in data analysis is important. 

The successful candidate will be expected to demonstrate a level of self-determination and planning commensurate with career stage in their CV and through their application document.  

Funding Comment

Covers full tuition, £20,780 stipend (2025/26), plus an annual stipend top up at a minimum of £2,000

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