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heat transfer in subcooled flow boiling within bends and complex geometries in fusion-relevant cooling channels. Develop and validate an advanced boiling model with a specific focus on Critical Heat Flux
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phase change phenomena to achieve high heat transfer rates at the cell and pack level. By harnessing latent heat, such systems deliver far more efficient cooling than conventional air or single-phase
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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methods that are data-efficient, reliable, and deployment-ready. Aims Advance operator-learning architectures for transient heat transfer in complex geometries, integrating physical priors (PDE structure
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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mechanics and heat transfer. How to apply Please send an email with subject “CDT studentship: Adaptive Haptic Skill Transfer for Human-Robot Collaboration in Nuclear Teleoperation” to Ayse Kucukyilmaz
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This exciting project aims to experimentally and theoretically investigate the fundamental nucleation and heat transfer mechanisms in high-pressure flow boiling, in particular Critical Heat Flux
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mitigation (IEA, 2023). Technological retrofitting, like installing heat pumps and insulation, is key for reducing energy use and CO2 emissions. Its effectiveness depends on occupant behaviours, such as
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processes. Small oceanic flows on the 1-10 km range (submesoscale) have attracted lots of attention for their role in heat mixing, energy transfer, and air-sea interactions. They have been related to a