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pollutants released into the atmosphere in the wake of passenger cars are still poorly understood. An enhanced understanding of these physical processes involved in the dispersion of pollutants is essential
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for a Condition-based maintenance (CBM) which holds the promise of predicting machinery maintenance requirements based on process performance measurements. Diagnostics and Prognostics are essential
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A funded PhD studentship is available within the Autonomous and Cyber Physical Systems Centre at Cranfield University, Bedfordshire, UK. As aerospace platforms go through their service life, gradual
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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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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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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