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Research theme: Nuclear thermal-hydraulics How to apply:uom.link/pgr-apply-2425 How many positions: 1 This 3.5 year PhD is fully funded, home students and students with settled status are eligible
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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Supervisory Team: Prof Neil Sandham PhD Supervisor: Neil Sandham Project description: This project is focused on scale-resolving simulations (large-eddy and direct numerical simulation) combined
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to test data, which are therefore only useable within a narrow range of scenarios. These limitations result in the requirement of large number of high-cost experiments being conducted to populate the models
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the “Finances/Funding” field write “QIST PhD” Upload all required documents (CV, personal statement, degree certificates and transcripts). Contacts For further information about the programme at Sussex contact
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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University. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Cranfield University, in the area of gas turbine performance, diagnostics and prognostics
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learning models of quantum chemistry can achieve fast and accurate predictions, but comprehensive data sets for reaction barriers of large molecules simply do not exist. Several recent works have attempted
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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distribution. This process often takes place in large scale driers where the material is heated and broken up mechanically with mixing blades. However, under certain conditions the process can break down as the