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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott facility
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physics background. Experience of experimental and computational modelling of icing physics, instrumentation and imaging techniques would be an advantage. Funding The Centre of Propulsion and Thermal
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in our CDT program, and warmly encourage applications from students of all backgrounds, including those from underrepresented groups. We particularly welcome students with disabilities, neurodiverse
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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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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and brain tissue mechanics to improve stroke treatment. Stroke is a leading cause of death and disability worldwide, making advancements in its diagnosis and treatment highly relevant. Computational