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

About the Project

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.

Supervisors

1. Dr Majid Bastankhah, Durham University majid.bastankhah@durham.ac.uk
2. Dr Nima Gerami-Seresht, Durham University

Training & Skills

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.

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 to the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience are made to the University where the PhD project is based. You will find full instructions and links on the CDT website

Applications for September 2026 entry will be considered on a rolling basis and applicants are therefore strongly encouraged to apply as early as possible.

Shortlisted candidates may be invited to interview as applications are received.

As offers may be made before all applications are reviewed, early application is strongly encouraged.

Funding Notes

The Offshore Wind CDT is funded by EPSRC, allowing us to provide scholarships for Home students that cover fees plus a stipend set at the UKRI nationally agreed rates, circa £20,7980 per annum at 2025/26 rates (subject to progress).