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

Durham University Offshore Wind CDT
✓ Fully Funded 🎓 Aerospace Engineering 🎓 Computational Mathematics 🎓 Energy Technologies 🎓 Engineering Mathematics 🎓 Environmental Sciences 🎓 Mathematical Modelling 🎓 Mechanical Engineering 🎓 Mechanics 🎓 Offshore Engineering machine learning aerodynamics numerical modelling AI modelling convolutional neural networks energy efficiency granular computing structural resilience wind farm optimisation wind turbine clusters

This fully-funded PhD focuses on enhancing wind farm performance and lifespan using machine-learning models integrated with numerical simulations to optimise turbine cluster aerodynamics and operational efficiency.

Project Description

Wind turbines operate as part of complex, multi-phase systems where aerodynamic interactions between turbines impact overall performance. Traditional modelling approaches treat turbines individually, often leading to sub-optimal farm-level efficiency. This PhD will: Apply machine learning (ML) and artificial intelligence (AI) techniques, including granular computing, 2D and 3D convolutional neural networks (CNNs), to model large-scale wind turbine clusters Integrate spatial and environmental data to predict collective aerodynamic behaviour of wind farms Enable data-driven optimisation for production efficiency, layout design, and turbine lifespan extension Combine AI models with numerical simulations for enhanced predictive accuracy and real-time control strategies Candidates will gain expertise in advanced computational modelling, AI/ML for engineering systems, and data-driven optimisation for offshore wind applications. Research outcomes will have direct industrial relevance for sustainable, high-efficiency wind energy generation.

Entry Requirements

First-class Honours degree, 2:1 plus Masters, or Masters with Distinction in Engineering, Environmental Sciences, or Physics
English proficiency: IELTS 7.0 overall (min 6.0 in each skill)
Open to international applicants
Guaranteed interview scheme for eligible home students from underrepresented ethnic backgrounds

How to Apply

Applications submitted to Durham University via the Offshore Wind CDT website. Rolling application for September 2026 entry; early submission encouraged.

Eligibility

UK/Home
EU
International

Supervisor Profile

DM
Dr Majid Bastankhah, Dr Nima Gerami-Seresht
Durham University, Offshore Wind CDT

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