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Field
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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understand and characterise the real-world variability of conditions for chain drives in high performance sport. Improving the emulation of race conditions improves the optimisation of components for riders
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is intended to function and demonstration of the system’s safe operation within these regions, usually requiring the system to abort, or go to a default safe state, when the intended conditions are not
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used to train surrogate models that can instantly predict quantities required by component scale CFD wall boiling models for different flow conditions and heat transfer surfaces. Key milestones
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community-led model. Investigate how knowledge is co-created and used across different scales (individual, organisational, systems). Compare the Isles of Scilly CRN with eight other CRNs across the UK, each
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and heat transfer in geothermal systems under high-pressure and high-temperature conditions relevant to AGS. • Developing high-fidelity direct numerical simulation (DNS) models to map
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expertise and facilities in electrochemistry, materials chemistry, advanced characterisation techniques (including a variety of spectroscopy, microscopy,) modelling and battery and fuel cell construction and
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: Coordination Layer: Formulate passivity-based conditions that guarantee agents—modelled as general nonlinear systems—synchronize their outputs or follow desired collective patterns purely through local
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of developing a computational model to simulate hydro-sedimentary dynamics and morphology between restored patches to explore conditions favourable for restoration. Validated with data on vegetation cover and
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to develop a simple and reproducible cell-based model to investigate how the changes in blood flow associated with pre-eclampsia damage the syncytiotrophoblasts leading to the detrimental release of factors