PhD Studentship: Digital Twinning for Smart Resin Infusion and Curing in Wind Turbine Blades via Embedded Fibre Optic Sensors and Physics-Informed Machine Learning

Supervisor(s)

• 1) Professor James Gilbert, University of Hull
• 2) Dr Hatice Sas, University of Sheffield

Increasing productivity and yield in the manufacture of wind turbine blades is a key priority for the UK offshore wind sector, as set out in the Offshore Wind Industrial Growth Plan. The manufacturing process involves the infusion of resin into a mould to form a composite structure with glass or carbon fibre reinforcement. This is a complex thermos-chemical-flow process which is difficult to model and to monitor which has a major impact on production time and product quality.

We have developed techniques for modelling and monitoring the infusion and curing process and this PhD will bring these elements together to form a digital twin of the process. This digital twin will be used to predict manufacturing defects, such as dry spots, but also enable the development of real time control methods to adjust process parameters to maximise productivity and product quality.

Working closely with the University of Sheffield and with industry partners, you will develop and optimise the modelling techniques. This includes the development of Physics Informed Neural Networks and combine this with real-time imaging and monitoring of resin infusion in sample composite structures to build the digital twin and then explore methods for defect prediction and real time process control.

The improved process performance that this offers will have a major impact in manufacturing processes, improve the sustainability of the industry and strengthen the UK’s position as a leader in the sector.

Training and development

You will receive project-specific training in numerical modelling tools and techniques and in machine learning. 

Eligibility 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 Computer Science, Engineering, 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.

Closing date: 5 January 2026

Advert information