UOH
Digital Twinning for Smart Resin Infusion and Curing in Wind Turbine Blades via Embedded Fibre Optic Sensors and Physics-Informed Machine Learning
✓ Fully Funded
🎓 Artificial Intelligence
🎓 Computational Mathematics
🎓 Engineering Mathematics
🎓 Fluid Mechanics
🎓 Machine Learning
🎓 Manufacturing Engineering
🎓 Mechanical Engineering
🎓 Polymers
🎓 Software Engineering
🎓 Structural Engineering
digital twin
wind turbine blades
AI for engineering
curing process
defect prediction
fibre optic sensors
offshore wind manufacturing
physics-informed neural networks
process monitoring
resin infusion
This fully-funded PhD develops a digital twin of the resin infusion and curing process in wind turbine blade manufacturing using embedded fibre optic sensors and physics-informed machine learning. The project aims to predict manufacturing defects, enable real-time process control, and improve quality and sustainability.
Project Description
Manufacturing wind turbine blades involves complex resin infusion into fibre-reinforced composites. Variations in flow, curing, and environmental conditions can reduce product quality and efficiency. This project will integrate advanced monitoring and modelling to create a digital twin of the process, providing predictive insights and real-time control capability.
Key research activities include:
Development of digital twin models combining physics-informed neural networks with sensor data
Real-time imaging and monitoring of resin infusion in sample composites
Prediction and mitigation of manufacturing defects such as dry spots
Collaboration with the University of Sheffield and industry partners for model validation and process optimisation
The student will receive intensive training in numerical modelling, machine learning, and the broader offshore wind manufacturing sector through the CDT’s six-month induction and ongoing Continuing Professional Development (CPD).
Entry Requirements
First-class Honours degree, or 2:1 Honours plus a Masters, or Masters with Distinction in a relevant discipline (engineering, mathematics, computer science, or related)
English proficiency: IELTS 7.0 overall (min 6.0 in each component)
Open to international applicants
Guaranteed interview scheme for eligible home students from underrepresented ethnic backgrounds
English proficiency: IELTS 7.0 overall (min 6.0 in each component)
Open to international applicants
Guaranteed interview scheme for eligible home students from underrepresented ethnic backgrounds
How to Apply
Applications are submitted to the University of Hull via the Offshore Wind CDT website. Applications are considered on a rolling basis for September 2026 entry, with early submission strongly recommended.
Eligibility
UK/Home
EU
International
Supervisor Profile
PJ
Prof James Gilbert (University of Hull), Dr Hatice Sas (University of Sheffield)
University of Hull, Offshore Wind CDT
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