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model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas film flow within the microscopic seal gap. Couple CFD with Structural Models: Study the fluid-structure
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direction could be to use the technique of Inverse Reinforcement Learning (IRL) [2], [3]. IRL is an AI-based technique that supports imitation of the preferred system behaviour by using its behavioural
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explore the challenges of pumping cryogenic hydrogen in multi-phase flows, particularly under off-design, low-flow conditions, through structured design, advanced modelling, and experimental validation
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environments for data sharing and reuse across various domains. You will also contribute to the development of a predictive maintenance tool to train asset degradation models using asset data from a building
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model to understand the dominant physics in the drying process. Develop a well-documented open-source code to simulate a suitable reduced problem of the drying process. Generate a database quantifying
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experimental and modeling approaches. Experimental investigations will be conducted at the University of Manchester, utilizing established rigs to simulate a spectrum of conditions, from single-phase heat
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under desiccation using ion mobility coupled to mass spectrometry (IM-MS) as a structural biology technique along with molecular dynamics and protein modelling. The successful candidate will join a new