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Prof Ping Xiao

Supervisor

This PhD project will accelerate the development of novel environmental barrier coatings required for ceramic matrix composite components for the next generation of higher temperature aero-engines in collaboration with Rolls-Royce through machine learning. Silicon carbide-based ceramic matrix composites (SiC-SiC CMCs) are being developed to transform aeroengine design by replacing nickel-based superalloys in the hot section of an aero-engine, due to their superior high-temperature mechanical properties and lower density e.g. density of CMCs is about one third density of superalloys. The use of the lighter weight CMCs would allow engine operating at higher temperature, using less cooling air, and therefore fuel. However, environmental barrier coatings (EBCs) are required to protect SiC-SiC CMCs from steam corrosion in engine environment. Without EBCs, the underlying substrates rapidly degrade in the hostile gas-turbine environment, so the failure of the EBCs is life limiting for the entire component. To introduce EBCs into aero-engine service, extensive research on manufacture, characterization and testing of EBCs has been carried out over decades. A range of materials have been investigated, but the current state-of-the art EBCs are based on ytterbium disilicate deposited via the air plasma spraying (APS) process.

Host Institution: University of Manchester

Dr Enzo Liotti

University of Oxford

Hydrogen embrittlement (HE) is an unsolved industrial challenge posing a significant constraint on the development of storage, distribution and end-use hydrogen technologies. The specific origin of HE has long been a topic of debate and no precise proposed mechanism exists that is consistent with all the observed HE consequences. In particular, progress in HE mechanisms understanding is hindered by the lack of experimental data regarding hydrogen trapping, the phenomena taking place during cracking and the interaction of hydrogen with microstructural features. Furthermore, to evaluate the behaviour of materials in real-world conditions, the validity of these mechanisms need to be assessed in-situ and dynamically over a broad range of environments – e.g. temperature (from cryogenic to combustion), high pressure, gaseous mixture and varying load. Are existing HE mechanisms still valid at liquid hydrogen temperatures? How is diffusion affected by dynamic conditions, for example when changing temperature and pressure? None of the currently available characterization methods can be used to run in-situ and operando experiments which allow simultaneous mapping of hydrogen within the microstructure whilst assessing the mechanical response under dynamic conditions of load and environment.

Host Institution: University of Oxford

Dr Robert Menzel

Supervisor

The studentship will investigate advanced microfluidic techniques to create unique functional aerogel materials, in close collaboration with our industrial partner AWE. The project will follow-on from a previous PhD studentship that successfully developed a microfluidics-based platform for the synthesis of polymer foams with highly controlled internal structures. This studentship will translate the microfluidics synthesis technology to the fabrication of metal aerogels (gold, silver, copper), metal oxide aerogels (tantalum oxide, titanium oxide) and related core-shell materials. The aim of the project is to utilise the high level of control provided by the microfluidic synthesis to produce aerogel materials with unique, bespoke internal microstructures, currently not accessible by other fabrication approaches.

This opportunity is open to UK nationals only.

Host Institution: University of Leeds

Prof Chris Race

CDT Co-Director

In fusion reactors, materials experience extreme temperatures, stresses, and radiation damage. Safe operation requires identification of deformation patterns that are early warning signs of materials failure. These characteristic patterns result from the interaction of deformation mechanisms across multiple scales making detection via traditional analytical methods extremely challenging. This project will apply pattern recognition and machine learning techniques to a large database of experimental data to reveal early-stage fingerprints for damage hidden in the data.

Materials 4.0 Theme: Data-informed metrology for materials science; Materials informatics, data-focused approaches and AI for materials discovery; “Smart” characterisation methods, e.g.

Royce Research Areas: Advanced Metals Processing;

Host Institution: University of Sheffield

Dr Theodosia Stratoudaki

Supervisor

This PhD study aims to develop the world’s first laser ultrasound array system for real-time, remote and couplant-free ultrasonic microstructural characterisation of metallic parts using Artificial Intelligence (AI). This system will enable the development, deployment and experimental validation of real-time, in-process, microstructural monitoring of manufacturing processes, a key step towards sustainable and reliable manufacturing of high-value, safety-critical components. In the long term, this material characterisation capability will form the basis of a feedback loop with manufacturing parameters, enhancing control over material properties of parts and enabling bespoke material microstructures.

Host Institution: University of Strathclyde