Lifecycle Optimisation of Wind Farms using Machine-Learning Models Enhanced with Numerical Modelling

Project Description:

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 successful applicant will undertake six-month of training with the rest of the CDT cohort at the University of Hull before continuing their PhD research at Durham University.

To enhance the efficiency and extend the operational lifespan of wind turbines, it is essential to develop a thorough understanding of their aerodynamics, particularly their interactions with the surrounding environment. Wind turbines operate as part of a complex, multi-phase engineering system. Current approaches to analysing wind turbine aerodynamics typically rely on either high-fidelity or reduced-order numerical methods. While these methods provide valuable insights, they generally treat each turbine in isolation and prioritise maximising individual power output, which can result in sub-optimal performance at the wind farm level. A more effective strategy is to evaluate the aerodynamics of turbine clusters as an integrated system, a process that demands rapid computational methods to enable real-time control and optimisation. 

This project aims to advance the understanding of wind farm aerodynamics by employing cutting-edge artificial intelligence (AI) techniques for modelling and analysis of large wind turbine clusters. The approach integrates two key components: (i) an evolving class of AI methods known as granular computing, which is designed for knowledge representation and the analysis of large-scale datasets; and (ii) spatially informed machine learning techniques, including two- and three-dimensional convolutional neural networks (2D and 3D CNNs), to capture the relationships between environmental factors and the collective aerodynamic behaviour of wind farms treated as a single, integrated system. 

By providing more accurate predictions of turbine aerodynamics in large clusters, this project will enable: 

• Data-driven decision-making to maximise the production efficiency of large-scale wind farms. 
• Extension of turbine lifespan through improved understanding and management of aerodynamic interactions at the farm level. 
• Optimisation of wind farm layouts that balance energy efficiency with long-term structural resilience.

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.

You will also be able to access the following training courses offered by Durham University’s DCAD team: (i) Using Hamilton 8 Supercomputer, (ii) Data Wrangling and Graphics in R, and (iii) Hands-on Infographics (Data Visualisation).

Entry requirements

If you have received a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or the international equivalents) in Engineering, Environmental Sciences, or Physics, we would like to hear from you.

If your first language is not English, or you require Tier 4 student visa to study, you will be required to provide evidence of your English language proficiency level that meets the requirements of the Aura CDT’s academic partners. This course requires academic IELTS 7.0 overall, with no less than 6.0 in each skill.

If you have any queries about this project, please contact Dr Majid Bastankhah via majid.bastankhah@durham.ac.uk

You may also address queries about the CDT to auracdt@hull.ac.uk.

Watch our short video to hear from Aura CDT students, academics and industry partners:

Funding

The Offshore Wind CDT is funded by the EPSRC, allowing us to provide scholarships that cover fees plus a stipend set at the UKRI nationally agreed rates. These are currently £20,780 per annum at 2025/26 rates and will increase in line with the EPSRC guidelines for the subsequent years (subject to progress).

Eligibility

Research Council funding for postgraduate research has residence requirements. Our CDT scholarships are available to Home (UK) Students. To be considered a Home student, and therefore eligible for a full award, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the scholarship (with some further constraint regarding residence for education). For full eligibility information, please refer to the EPSRC website.

We also allocate a number of scholarships for International Students per cohort.

Guaranteed Interview Scheme

The CDT is committed to generating a diverse and inclusive training programme and is looking to attract applicants from all backgrounds. We offer a Guaranteed Interview Scheme for home fee status candidates who identify as Black or Black mixed or Asian or Asian mixed if they meet the programme entry requirements. This positive action is to support recruitment of these under-represented ethnic groups to our programme and is an opt in process.

Find out more

How to apply

Applications for this project will open in Autumn 2025 for September 2026 entry.

Interviews will be held online with an interview panel comprising of project supervisory team members from the host university where the project is based.  Where the project involves external supervisors from university partners or industry sponsors then representatives from these partners may form part of the interview panel and your supplementary application form will be shared with them (with the guaranteed interview scheme section removed).

If you have any queries about this project, please contact Dr Majid Bastankhah via majid.bastankhah@durham.ac.uk

You may also address queries about the CDT to auracdt@hull.ac.uk.

References & Further Reading

[1] Bastankhah, M., & Porté-Agel, F. (2014). A new analytical model for wind-turbine wakes. Renewable energy, 70, 116-123.

[2] Bastankhah, M., & Porté-Agel, F. (2019). Wind farm power optimization via yaw angle control: A wind tunnel study. Journal of Renewable and Sustainable Energy, 11(2).

[3] Han, J. M., & Malkawi, A. (2025). Airvox: efficient computational fluid dynamics prediction using 3D convolutional neural networks for building design. Journal of Building Performance Simulation, 18(1), 1-16.

[4] Pedrycz, W. (2018). Granular computing: analysis and design of intelligent systems. CRC press.