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Field
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Discipline: Engineering & Technology, Fluid Dynamics, Mechanical Engineering, Other Engineering Research area and project description: Droplets are ubiquitous in nature, industry, and our everyday
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on the use of state-of-the-art Computational Fluid Dynamics (CFD) to diagnose the air quality status of those spaces (presence of pollutants, ventilation, humidity) and to propose measures to improve it. Such
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Computational Fluid Dynamics (CFD) and Conjugate Heat Transfer (CHT) modelling, which captures both the fluid & solid domains, as required to develop this understanding for engine-representative geometries and
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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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Application deadline: All year round Research theme: Applied Mathematics, Mechanical and Aerospace Engineering, Fluid Dynamics How to apply:uom.link/pgr-apply-2425 How many positions: 1 This 3.5
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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Applications are invited for a funded PhD position at the ABCE at Loughborough University. In this position, you will be an active member of the Loughborough-based Fluid Dynamics Group, as
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. It will involve the use of open-source computational fluid dynamics (CFD) codes, turbulence modelling and the application of different near-wall treatments. It will also require the development of good
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nanosheets, nanotubes, etc) or hybrid (e.g. boron carbon nitride). Similarly, while water is the most studied coolant liquid, realistic applications involve dielectric fluids (e.g. benzene, pentane). Molecular
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, therapy resistance driven by tumour heterogeneity continue to limit the long-term success of current treatments. The candidate will analyse patient biopsies, cerebrospinal fluid (CSF), blood samples