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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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cyclic loading, varied surface conditions, and exposure to gaseous impurities, and advanced numerical modelling (Finite Element Analysis), this project aims to significantly enhance our understanding
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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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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
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fields, and risk damaging the part during fabrication. Finite element analysis (FEA) models, while capable of delivering detailed spatiotemporal distributions of thermal variables, suffer from limited
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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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scintillator-based radiation sensors combining multiple materials with complementary functions, offer a promising route to overcome these limits and achieve unprecedented timing resolution (sub-70ps), enabling
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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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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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costs. Condition monitoring (CM) of rolling element bearings, hereafter called bearings, has been the main point of attention for many decades in the industry for maintenance. This is because bearings