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We have 7 Solid Mechanics PhD Projects, Programmes & Scholarships in Bristol
Solid Mechanics PhD Projects, Programmes & Scholarships in Bristol
We have 7 Solid Mechanics PhD Projects, Programmes & Scholarships in Bristol
Modelling and experiments of drying-driven gelation of colloidal fluids
The project. Colloidal fluids are liquids that contain solid particles. The drying of colloidal fluids is crucial to countless industrial processes, such as ink-jet printing, pesticide spraying, and the fabrication of battery electrodes. Read more
ML-enhanced multi-scale modelling of hydrogels
The project. Hydrogels are soft, fluid-filled solids. Many hydrogels can change their size and shape in response to their environment. Read more
Digital Tool Chain For Defect Prediction And Optimisation In Thermoset Composites Lay Flat And Form
The project. This project will create new optimisation tools that can help the development of the manufacturing process of large-scale, high-value composite parts. Read more
Early-stage detection and characterisation of hydrogen-induced defects using ultrasonic arrays for enhanced structural integrity assessment
The project. Motivation. The development of hydrogen energy technologies is central to the UK government's ten-point plan for a green industrial revolution, targeting a 40% reduction in gas consumption by 2030 and carbon-zero emissions by 2050. Read more
Mathematical models of contact mechanics
The project. This project aims to resolve the computational difficulties that arise in Finite Element modelling of mechanical structures involving joints and surfaces that impact or slip against each other. Read more
Modelling, simulations and machine learning of the dynamics of human swallowing
The project. A normal swallow is an impressively complex sequence of movements, requiring the coordination of muscles located within the oral cavity, pharynx, larynx and oesophagus. Read more
Physics-informed machine learning
The project. Many modern machine-learning architectures require significant amounts of data for training and validation. In the physical sciences, experimental data is often sparse and hence machine learning models must be supplemented by constraints that go beyond observational data alone. Read more
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