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

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 in Hull with co-supervision from the 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 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 receive project-specific training in numerical modelling tools and techniques and in machine learning. 

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 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.

If you have any queries about this project, please contact Professor James Gilbert, j.m.gilbert@hull.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 for this project will open in Autumn 2025 for September 2026 entry.

Interviews will be held online with an interview panel comprising of project supervisory team members from the host university where the project is based.  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 supplementary application form will be shared with them (with the guaranteed interview scheme section removed).

If you have any queries about this project, please contact Professor James Gilbert, j.m.gilbert@hull.ac.uk

You may also address queries about the CDT to auracdt@hull.ac.uk.

References & further reading

Ivanov, Oleg V., Kaushal Bhavsar, Oliver Morgan-Clague, and James M. Gilbert. 2024. “Monitoring of Curing Process of Epoxy Resin by Long-Period Fiber Gratings” Sensors 24, no. 11: 3397. https://doi.org/10.3390/s24113397 

Thomas D. Allsop, Mohammad W. Tahir, Kaushal Bhavsar, Lin Zhang, David J. Webb, Jim M. Gilbert, “Fibre Bragg gratings: monitoring of infusion process in liquid composite molding manufacturing,” Proc. SPIE 12139, Optical Sensing and Detection VII, 1213913 (17 May 2022); https://doi.org/10.1117/12.2621757