This EngD project explores how probabilistic digital twins can be developed to better represent the real-world behaviour of engineering components used across the nuclear sector, including both fission and fusion applications. Instead of focusing on fundamental materials science, the work uses engineering-scale stochastic and random-field finite element methods to capture how natural variability in material properties influences structural response and reliability.
You will perform practical materials testing to quantify the variation introduced by manufacturing routes or in-service conditions. These measurements will be used to calibrate random-field models, which will then be embedded within computational simulations to create digital twins that explicitly account for uncertainty.
The aim is to enhance the predictive capability, credibility, and decision-making value of digital twins used throughout the nuclear engineering lifecycle. This project is ideal for a student who enjoys mechanics, simulation, and hands-on testing and who wants to contribute to advanced digital engineering tools for next-generation nuclear technologies.
This EngD project is funded by the Fusion Engineering CDT and hence the student will be based at The University of Manchester, and should expect to engage fully with the 3-month training programme within the Fusion Engineering CDT at the start of the course, which will involve some overnight stays at other universities. CDT training will be delivered across the CDT partner universities at Sheffield, Manchester, Birmingham and Liverpool.
For further information about the CDT programme, please visit the website or send an email to hello@fusion-engineering-cdt.ac.uk.
For informal queries, reach out to Professor Lee Margetts [Lee.Margetts@manchester.ac.uk].
Apply for this project here.
fusion_cdt