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to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers
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PhD Studentship: Optimisation of Liquid Metal Filtration and Cleanliness in Nickel Based Superalloys
-supervision by Dr Mark Hardy. The industry aligned EPSRC DigitalMetal CDT offers a four year training programme on integrating data driven with physics-based models of products equipping students with
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reduces computational cost and enables large-scale reactor simulations, current porous approaches, based on Reynolds-averaged Navier-Stokes models, rely on empirical correlations and assumptions that may
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with a first class or upper second-class degree in engineering, physics, applied mathematics or a related field. A solid foundation in fluid dynamics and heat transfer, and experience with computer
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.), including full fees and bursary. Main Copy (advised structure) Green hydrogen has been presented as an important aspect of the renewable energy future not only to decarbonise many industries including steel
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, this project aims to apply the innovative machine learning MACE framework. MACE allows for more efficient simulations by using machine learning to capture the underlying physics of gas transport, offering a
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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic
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and high-incidence operation. Using cutting-edge experimental data and high-fidelity unsteady CFD simulations, your research will enhance the understanding of flow physics, reduce risk in future designs
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in a degree, ideally at Masters level, in an Engineering subject, Physics, Mathematics, Computer Science or other quantitative background. Knowledge in fluid mechanics, ocean waves, numerical methods
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) approaches, along with Large Eddy Simulation, have demonstrated maturity in the prediction of many buoyancy-driven flows but require extensive validation. Two- and three-dimensional Computational Fluids