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Field
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing modelling capabilities for the prediction of energy
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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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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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Establishment). Recent work by the group (leading to REF 4* rated outputs and several Keynotes) has contributed to bridging the gap between Computational Solid and Fluid Dynamics, with a unified computational
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models for multiphase flows, which are crucial for various industrial processes. The successful candidate will develop advanced physics-based methods in fluid dynamics and heat transfer to study multiphase
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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A funded PhD position is available at the ABCE, Loughborough University, offering the successful candidate the opportunity to join both the Loughborough Fluid Dynamics Group and the Leeds Turbidites
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processes around fixed bottom offshore wind foundations, (2) Computational fluid dynamics modelling based on Lattice Boltzmann (LB) framework to simulate sediment transport surrounding offshore infrastructure
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a first or second-class UK honours degree (or equivalent) in aerospace/mechanical engineering, physics, or a related field. Experience in numerical fluid dynamics is helpful but not essential