Magnetically Engineered Austenitic Steels for Low Thermal Expansion and High-Temperature Strength
Not stated
- Location
- Leicester, United Kingdom
- Funding
- Competition Funded PhD Project (Students Worldwide)
- Application deadline
- 15 January 2027
About the project
About the Project A steel can be strong at high temperature and still be difficult to use if it expands too much. In large components and joints between different alloys, repeated heating and cooling can create stresses that shorten service life. Austenitic steels offer good high-temperature strength, but their relatively high thermal expansion can limit their use in advanced energy systems. This fully funded PhD is based in the Alloy Design & Phase Transformations Research Group at the University of Leicester, led by Dr Gebril El-Fallah. The project examines whether magnetic behaviour can reduce the expansion of austenitic steels while preserving the strength and stability needed in service. Invar alloys show that magnetism can strongly influence thermal expansion. Whether this behaviour can be used in steels intended for structural service remains to be established. You will investigate how alloy chemistry and microstructure affect magnetic response, thermal expansion and mechanical behaviour. The work will use physical metallurgy, computational thermodynamics and machine learning alongside experiments. Predictions will be checked against thermal, magnetic and mechanical measurements. Depending on the direction of the research, you may also use X-ray diffraction, electron microscopy and high-temperature testing. These results will help determine which changes reduce expansion and what they do to the steel’s strength. Candidate profile We welcome applicants with a degree in materials science, metallurgy, mechanical engineering, physics or a related field. You should be interested in the behaviour of metals, comfortable working with numerical data and able to explain your results clearly. Experience with a particular instrument matter less than careful analysis and sound interpretation. Experience with CALPHAD, Python, machine learning, diffraction, microscopy, dilatometry, magnetic measurements or mechanical testing would be useful. You do not need to know all these methods; training will be provided. Informal enquiries Project enquiries should be emailed to the PhD supervisor Dr Gebril El-Fallah gmae2@leicester.ac.uk