Cohesive powder flow in confined spaces

As the UK accelerates its ambition to establish a secure, resilient supply of High-Assay Low Enriched Uranium (HALEU) for next-generation reactors, a new and urgent scientific challenge has emerged. When traditional deconversion processes, transforming uranium hexafluoride (UF₆) into uranium oxide (U₃O₈), are scaled down to meet stricter criticality safety requirements, the behaviour of the resulting powder changes dramatically. These confined conditions lead to unpredictable flow interruptions, problematic material build-up, and inconsistent product quality.

This PhD project, funded by the United Kingdom National Nuclear Laboratory (UKNNL), aims to optimise both process design and material properties, to enable reliable powder handling, and to directly support the development of next generation of advanced nuclear reactors. 

The research will adopt a combined experimental, numerical, and machine learning approach to investigate cohesive powder flows in confined spaces, targeting unit operations relevant to HALEU deconversion. 

The PhD candidate will work closely with the UKNNL team, gaining direct operational insights into nuclear reactor design and operation. This ensures that the research stays rooted in practical nuclear environments while simultaneously cultivating valuable industry connections.

Open to candidates who pay UK/home rate fees. See UKCISA for further information.

Applicants must have (or be on track to obtain) a first-class or upper second-class honours degree (2.1), or a distinction/merit at master's level, in engineering, physics or a related field. 

We are looking for a curious and motivated candidate who is comfortable working across academic and industrial environments. Experience with, or a strong interest in, one or more of the following areas is particularly welcome:

• AI and machine learning
• Computational modelling (DEM, CFD, or FEM)
• Particle technology.