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understanding of the flow features and behaviours which develop within NCLs and provide highly valuable validation data for the development of effective and efficient simulation tools. The student will be
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models to simulate mobility scenarios, assess policy interventions, and guide sustainable transport strategies. Redesigning Urban mobility in 15 minutes context – Designing a systems approach and AI
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to support condition-based predictive maintenance for gas turbine engines. Cranfield has developed unique physics-based technologies on gas turbine performance simulations, diagnostics, prognostics and lifing
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resolve the turbulent dynamics. These simulated dynamics will then be interpreted using theoretical modelling frameworks, with a view to develop of these frameworks into predictive models capturing the full
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explore the challenges of pumping cryogenic hydrogen in multi-phase flows, particularly under off-design, low-flow conditions, through structured design, advanced modelling, and experimental validation
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effectively and accurately at long range. Research Objectives: Model and simulate the effects of poor weather on high-energy propagation. Analyse frequency and sensing modality impacts on transmission using EO
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Your research will focus on: Modelling and simulating how poor weather affects high-energy propagation. Exploring how signal transmission varies with frequency and sensing modality. Investigating the use
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multidisciplinary CDT cohort with shared training, collaboration, and networking opportunities. Gain hands-on experience in aero-engine aerodynamics, flow measurement, modelling, and simulation. Benefit from
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through Model-Based Systems Engineering (MBSE) and multi-fidelity simulations. Use experimental and computational approaches to improve fuel system confidence and reliability. Support the aviation
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direction could be to use the technique of Inverse Reinforcement Learning (IRL) [2], [3]. IRL is an AI-based technique that supports imitation of the preferred system behaviour by using its behavioural