Thermomigration of Hydrogen in Reactor Fuel Cladding Materials University of Oxford in United Kingdom

This fully funded PhD project at the University of Oxford, part of the EPSRC Centre for Doctoral Training (CDT) in Developing National Capability for Materials 4.0 with the Henry Royce Institute, focuses on the thermomigration of hydrogen in reactor fuel cladding materials. Thermomigration, the transport of solutes through metals driven by temperature gradients, is a critical but underexplored phenomenon that can significantly impact hydrogen embrittlement in structural metals. This effect is especially relevant in environments with steep thermal gradients, such as nuclear fuel cladding, hydrogen fuel systems, and fusion reactor armour.

The project aims to develop a machine-learning-enhanced digital material twin to accurately capture hydrogen diffusion and trapping in zirconium (Zr) alloys under complex conditions, including stress, temperature, hydrogen concentration, and irradiation. The research will leverage new experimental measurements of the heat of transport (Q*) across broad temperature ranges, using a custom-built rig with precise temperature gradient control and mass spectrometry for sensitive hydrogen flux detection. The digital twin will enable rapid inversion of experimental data into Q* values, supporting the calibration and testing of physically-based models for heat-of-transport in Zr.

Key challenges include understanding the interplay between hydrogen thermomigration and hydride precipitation at low temperatures, as well as the evolution of microstructure. The project will also address limitations in molecular dynamics simulations by employing machine learning descriptors of atomistic configurations to predict heat-of-transport evolution. The resulting digital material twin will be integrated into Rolls-Royce's comprehensive material model for fuel cladding design and optimisation, ensuring direct industrial impact.

Studentship funding covers tuition fees, a tax-free stipend at the UKRI rate, and a generous research and training support grant. Applicants should have a strong academic background in materials science, physics, chemistry, chemical engineering, or related fields, with desirable skills in machine learning, data science, and computational modelling. English language proficiency is required for non-native speakers.

Applications are accepted year-round. For technical queries, contact Professor Felix Hofmann ([email protected]). For general or application-related queries, email [email protected] or Safa Najar ([email protected]). Please note that each CDT partner may have its own application process. For more information and to apply, visit the University of Oxford postgraduate application portal.