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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
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(for plasma catalysis). Computational fluid dynamics & kinetic modelling of plasma reactor design. You will publish scientific articles related to the research project. You will carry out a limited number of
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& further information Informal queries: Project page: www.tcd.ie/mecheng/research/fluids-acoustics--vibration/projects/noise-2050 Shape Ireland’s soundscape of the future - apply now and make your research
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models for multiphase flows, which are crucial for various industrial processes. The successful candidate will develop advanced physics-based methods in fluid dynamics and heat transfer to study multiphase
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early 2030s. One prominent HTGR configuration is the pebble-bed reactor, in which spherical fuel elements (pebbles) are densely packed within the core, creating a complex and heterogeneous thermal-fluid
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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the role of stress-controlled structural permeability in hydrogen migration. The project will also investigate the petrophysical and fluid-flow properties of fault rocks and interrogate paleo-episodes