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Field
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flow visualisation and measurement techniques to study droplet impact under icing conditions to improve icing codes that aid in design and development of ice detection and mitigation system
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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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for analysing complex materials, structures and model validation. The DIC community has developed guidelines to ensure robust measurements, continually advancing standards through ongoing challenges. In
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perpetuation (or maintenance/persistence); to build ML models that include the heart’s physical properties to find patterns in the data and predict which areas in the heart drive AF. This project will explore
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renewables (wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the decarbonization of existing maintenance and decommissioning of assets. Year 1: You will spend
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renewables (wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the decarbonization of existing maintenance and decommissioning of assets. Year 1: You will spend
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will engage with national and international policy contexts, drawing on integrated assessment model (IAM) outputs to explore trade-offs and policy levers for guiding strategies for near- and long-term
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influenced by its unique thermodynamic properties and the low flow off design performance. The design methods and multi-phase models would be validated by experiments on lab scale components. This position is
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on numerical aspects of the network model analysis. Being part of the wider Mathematical Neuroscience research theme within the School of Mathematical Sciences which currently includes 7 members of academic
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resilient, long-term cost-benefit case for modernising and protecting our grid. The project will complement the ISS initiative and model data-driven grid optimisation services that ISSs can enable. Firstly