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Thermomigration of Hydrogen in Reactor Fuel Cladding Materials

✓ Fully Funded 🎓 Artificial Intelligence 🎓 Materials Science 🎓 Physical Chemistry digital twin machine learning fuel cladding nuclear materials zirconium alloys hydrogen embrittlement thermomigration heat of transport

Investigate the role temperature gradients play in hydrogen diffusion within nuclear fuel cladding. Develop advanced experimental and machine learning approaches to build digital twins capturing hydrogen transport and embrittlement, with direct industrial relevance in nuclear materials safety and optimization.

AI-generated overview

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Why This Research Matters

This research addresses critical gaps in understanding hydrogen transport mechanisms that govern embrittlement in nuclear fuel cladding. By providing high-fidelity experimental data and predictive modelling tools, it will enhance nuclear reactor safety and reliability, and support the development of materials resilient to hydrogen damage in various energy technologies.

Mechanics Micro-deformation X-ray diffraction Spectroscopy

Project Description

Project Overview

This project is part of the EPSRC CDT in Developing National Capability for Materials 4.0 with the Henry Royce Institute. It aims to study thermomigration, the transport of hydrogen in metals driven by temperature gradients, which impacts hydrogen embrittlement and hydride precipitation in reactor fuel cladding materials. The project will focus on zirconium alloys where steep temperature gradients arise from internal heating and water-side cooling.

The lack of experimental clarity regarding thermomigration driving forces and heat of transport values limits predictive capabilities. This project will construct a new experimental rig to measure heat of transport (Q*) robustly across broad temperature ranges using permeation cells with precise temperature control and mass spectrometry to detect hydrogen flux. A digital twin of the experiment will allow inversion of data to determine Q* values precisely.

What You Will Do

You will develop and operate the new measurement apparatus, collect heat of transport data in Zr alloys, and develop a digital material twin to capture hydrogen transport under complex conditions. Experimental results will be compared against physically-based models recently proposed to describe temperature dependence of heat of transport. You will also integrate machine learning approaches to relate atomistic configurations to thermomigration behavior, addressing electronic effects that challenge classical molecular dynamics simulations.

Expected Outcomes

The project will deliver high-fidelity experimental data and validated digital twins to predict hydrogen transport and embrittlement in nuclear cladding materials. The new insights and modelling tools will be integrated into Rolls-Royce’s comprehensive fuel cladding material design frameworks, enhancing industrial capability in safe and optimized nuclear fuel management.

Why This Matters

Understanding and accurately modeling hydrogen thermomigration is vital for nuclear fuel cladding safety and longevity. Thermomigration strongly influences hydrogen concentrations that cause embrittlement and microstructure evolution, making it critical for design and optimization in hydrogen-exposed structural metals. The project addresses a unique knowledge gap with direct implications for nuclear energy and hydrogen fuel technologies.

How to Apply

Application instructions can be found on the 'How to Apply' tab at https://www.ox.ac.uk/admissions/graduate/courses/materials-4-0. For general enquiries, contact doctoral-training@royce.ac.uk. For application queries, contact graduate.admissions@materials.ox.ac.uk. For scientific queries about this PhD, contact Prof Felix Hofmann at felix.hofmann@eng.ox.ac.uk.

Eligibility

UK/Home
EU
International

Supervisor Profile

PF
Prof Felix Hofmann
University of Oxford
3718 Citations
35 h-index
Google Scholar

Prof Felix Hofmann leads research on materials modelling with a focus on integrating physics-based and machine learning approaches to understand complex materials behavior under extreme conditions. Based at the University of Oxford, he specializes in nuclear materials and computational materials science, contributing significantly to the development of digital material twins for industrial applications.

Key Publications

2015 177 citations
Lattice swelling and modulus change in a helium-implanted tungsten alloy: X-ray micro-diffraction, surface acoustic wave measurements, and multiscale modelling
2018 157 citations
Consistent determination of geometrically necessary dislocation density from simulations and experiments
2017 143 citations
3D lattice distortions and defect structures in ion-implanted nano-crystals
2020 132 citations
Dislocation density distribution at slip band-grain boundary intersections
2015 123 citations
Measurements of stress fields near a grain boundary: Exploring blocked arrays of dislocations in 3D

Research Contributions

Studied lattice swelling and modulus changes in helium-implanted tungsten alloys using advanced X-ray micro-diffraction and modeling.
Provided insights into material behavior under irradiation, important for nuclear materials engineering.
Developed methods for consistent determination of geometrically necessary dislocation density combining simulations and experiments.
Enhanced understanding of plastic deformation mechanisms, aiding materials design and engineering.
Characterized 3D lattice distortions and defect structures in ion-implanted nanocrystals to reveal damage mechanisms.
Improved knowledge of defect formation in materials, relevant to semiconductor and radiation damage fields.
Measured stress fields near grain boundaries and explored blocked dislocation arrays in three dimensions.
Advanced the understanding of microstructural stress distribution influencing material strength and failure.

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