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, development of dynamic physical models that allow counterfactual reasoning of highly complex systems such as plane engines, automated robotics that allow fully autonomous exploration and modelling of unknown
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the baseline mechanical/physical material properties and joining technologies at the micro- and macro-scale of High Hard Steel. To develop a criteria of failure for the materials selected. This will involve
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date is 1st October 2023. The PhD student will be physically located at Cranfield University, in the Centre for Electronic Warfare Information and Cyber at Cranfield Defence and Security (CDS). CDS
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developed Physics-informed Neural Network (PINN) technique will be first explored, tailored, and extended into the PdM context of high-value critical assets. It is expected that combining the domain knowledge
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fitting for reduced order electrochemical models. Early detection of thermal anomalies in battery packs. Physics-based models and state of health estimation in lithium-sulfur batteries. Collecting data and
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additional details. However, in many cases it is difficult to obtain the demanded HR images/videos due to the high cost and inherent physical constraints of the high precision optics and sensors
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) prediction is a process using prediction methods to forecast the future performance of components or systems and obtain the time left before them loses its operation ability. Knowing the RUL of a system is
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rapid source noise prediction of integrated helicopter-engine systems. The research will be sponsored by the Engineering and Physical Sciences Research Council (EPSRC) and the Defence and Science
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of representative failure models for gear failures causes difficulties in their useful lifetime prediction. Critical operational parameters such as loading, speed and lubrication affect the physics of gear meshing
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, technology and process demonstrators to provide the capability for the concept design of high value engineering systems based on design and manufacturing for through-life engineering services. The student will