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through Model-Based Systems Engineering (MBSE) and multi-fidelity simulations. Use experimental and computational approaches to improve fuel system confidence and reliability. Support the aviation
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simulations, exploring novel aspects of numerical modelling and expanding the computational mechanics capabilities of the group. This project offers the opportunity to join a vibrant research group and
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explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers the opportunity for the PhD student to lead the development of innovative simulation tools
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of data from particle physics experiments and their simulations. Contribute to other activities of the Particle Physics and Particle Astrophysics group in the School of Mathematical and Physical Sciences
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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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, 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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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