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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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. Focusing on adaptive intelligence, which blends human creativity and machine intelligence, the project will develop Multi-Intelligence Agents (MIAs) to facilitate the seamless integration of social factors
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using alternative fuels and/or increasing the efficiency of the gas turbines. Additively manufactured materials, could help in increasing the temperature at which GTs will run, with a consequent increase
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will dynamically adjust turbine parameters such as yaw, pitch, and torque to maximize Annual Energy Production (AEP) while minimizing component stress. Additionally, a hybrid predictive maintenance model
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has a dramatic effect on the fate of a component—a part may survive millions of cycles in vacuum and last only a few hundreds of thousands in air and much lower in actively corrosive environments
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complimentary computational studies to predict the intake aerodynamic characteristics and aid in the experiment design. This position is part of the CDT in Net Zero Aviation, which offers a modular, cohort-based
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(conductivity, heat capacity, flame resistance). Advanced finite element modelling will then correlate microstructural features to heat-transfer performance. The candidate will design and build a burner-rig test
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substrate, enabling the layer-by-layer construction of complex 3D objects. The temperature field created by the interaction between the electric arc and the material is a critical factor influencing the
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energy efficiency. Surface treatments and engineered coatings will be explored to improve inter-material interfaces, reduce optical losses, and enhance detector robustness, critical factors to advance
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simulations and finite element analysis, with high-heat flux electron beam experiments. The research will simulate and replicate steady, cyclic, and transient thermal loads to better understand PFM behaviour