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The research in this doctoral opportunity will numerical assess single/multiple point wear in mechanical contacts and its relation with airborne friction noise. Friction noise and wear phenomena are
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partners from the wider aviation sector Access to world class research and education facilities The student will gain significant insight in numerical methods, performance engineering and engine
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novel methods to optimise de-ice ETIPS, which will replace current manual and slow processes. It is envisaged that the PhD research will develop a range of novel agent based AI/ML techniques and neural
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provides rich datasets with well resolved spatial and temporal measurements. The overall aim of this PhD project is to develop the next generation of advanced data analysis and characterisation methods for
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arc energy distribution becomes particularly complex in scenarios like parallel-pass deposition, thin-wall deposition, and off-centre or out-of-position deposition. Additionally, FEA models are focused
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. Analytical, numerical and empirical studies clearly indicate that the dissipated strain energy during crack propagation affects the modal values of the structure. However, there is still need to perform
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, and more efficient hydrogen storage tanks. These materials will be designed to address current limitations in conventional storage systems, such as weight, safety, and energy density. In parallel
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel the
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discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in the Project element or equivalent with a minimum 60% overall
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Jennions Entry requirements A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum