Trustworthy Digital Twins for Nuclear Manufacturing
Not stated
- Funding
- Funded PhD Project (UK Students Only)
- Application deadline
- Year-round applications
About the project
About the Project How do we build and maintain trustworthy-by-design digital twins for nuclear manufacturing environments, with integrated VVUQ, assumption management and twin-drift monitoring? Lead Supervisor: TBC (JC Chaplin) University of Nottingham Second Supervisor: To be confirmed Industry Partner: Sought Project Start: October 2026 Target Background: Mechanical Engineering, Digital Manufacturing, possibly Computer Science Industrial Funding: Sought Advert Close Date: TBC Programme: 4 year Engineering Doctorate (EngD) with industry placement Digital twins you can rely on for decades. Digital twins (DTs) are increasingly used to improve monitoring, prediction and optimisation. In safety-critical settings such as nuclear manufacturing, the value of the DT relies on its fidelity and trustworthiness: how closely it matches the physical twin, and how much its insights can be relied upon. This challenge is more significant when the DT operates over long periods, where the manufacturing system may undergo equipment upgrades, process changes and evolving requirements. DTs must therefore have a plan covering verification, validation and uncertainty qualification (VVUQ) over time. Applying a comparable level of rigour to a nuclear manufacturing process would allow a credible twin that offers reliable insights, reducing build time and facilitating auditing and record keeping. Aims and objectives Aim: how do we build and maintain trustworthy-by-design digital twins for nuclear manufacturing, integrating VVUQ, assumption and uncertainty management, and monitoring drift over time? Objectives: Digital twin credibility assessment: how much VVUQ does a twin need for the intended use and risk, and how is it monitored as the model evolves? Lifecycle governance: how are assumptions, limits, data provenance and context of use recorded and preserved over time? Twin drift: how do we formalise and automate synchronisation, and understand the trade-off between frequent and infrequent synchronisation? AI for digital twins: how far must VVUQ extend into any AI used to build or synchronise the twin? Quantifying trust and efficacy: what KPIs measure fidelity and trustworthiness? Alignment to STAND-UP impact targets >50% reduction in overall build or decommissioning process time >40% reduction in maintenance time (not applicable) >30% reduction in person hours on builds Apply for this project Contact the lead supervisor or programme team to discuss your interest. Full application instructions are on the How to Apply page. How to apply Email the team