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 are open until 5 January 2026.

Please note, you may only apply for ONE project offered through the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience.

Please ensure that you familiarise yourself with the Aura CDT website before you apply to give you a good understanding of what a CDT is, our CDT’s research focus and the training and continuing professional development programme that runs alongside the CDT. The Frequently asked questions page and Candidate resources page are essential reading prior to applying.

Applications to this project are made via the Durham University admissions system. If you have not applied to Durham University before, you will need to set up an account to enable you to track the progress of your application and upload supporting documents.

As part of the recruitment process, we ask that you submit a short film of you delivering a presentation, of up to 5 minutes in length, on “How do your experiences and qualities provide a background to contribute to research and innovation for the project you have applied for”.

You will be assessed on the content of your presentation, not your film editing skills, but please be mindful of filming in an appropriate, quiet location. Please film the presentation in whatever way you feel most comfortable with. For example, it could be a slide presentation with voice over, or you may wish to present simply talking to the camera.  Please use the tools and technology that are accessible to you and that you feel comfortable with e.g. your mobile phone, or the built-in ‘Record Slide Show’ on Keynote (macOS, iOS, iPadOS) or Powerpoint etc.

We also ask that you complete a Supplementary Application Form. This includes space for you to provide a link where the shortlisting panel may view your film.

Follow this link to apply for CDT projects at Durham University: Home  · Application Portal

For CDT projects based at Durham University you need to select “PhD in Aura CDT” as your course and “H1A701” as your course code. Please make sure you select “October” intake (although the CDT PhD will start in September).

With your application, you need to upload copies of the following supporting evidence:

• Complete transcripts (and final degree certificate(s) where possible). If your qualification documents are not in English, you will need to supply copies of your original language documents as well as their official translation into English.
• Your Curriculum Vitae (CV).
• A completed Supplementary Application Form (upload when asked for your Personal Statement. Please note that you also do not need to upload a Research Proposal).

Uploading the form 

When you have completed the form, please save it as a pdf format and labelled as follows:

Last name_first name PhD application form

Upload the form as part of your application documents through the Durham University student application portal, when asked to add your Personal Statement. The Form replaces the Personal Statement and so you do not need to complete the Personal Statement section. Please do not send your form directly to the Offshore Wind CDT.  Please also note that you do not need to upload a Research Proposal as the project is already predetermined – an automated email will be sent to request this, however please do ignore this request.

Interviews

First-round interviews will be held online during early to mid-February 2026. The interview panel will comprise the project supervisory team members from the host university where the project is based, plus a representative of the CDT.  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 application documents will be shared with them (with the guaranteed interview scheme section of the supplementary application form removed).

If you are successful, you will progress to a second interview towards the end of February 2026. This will be with key academics from the CDT from across our four partner institutions (Durham University, University of Hull, Loughborough University, University of Sheffield) and your application documents will be shared with them (with the guaranteed interview scheme section removed from the supplementary application form).

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.