About the project
Polymer matrix composites, such as carbon fibre-epoxy, constitute a core light-weighting technology across virtually all transport sectors (aerospace, automotive, marine), providing a vital element of net-zero carbon objectives for the future.
Optimisation of advanced composite structures is however fundamentally prohibited by current testing, simulation and certification approaches. As part of a £6.7M EPSRC Programme Grant we seek to break this impasse by holistically reshaping the traditional building-block approach to design and the associated 'test-pyramid'.
Combining world-class expertise from the Universities of Southampton, Bath, Bristol and Exeter, the project will promote a rigorous and practical shift to virtual testing, enabling numerous benefits in mass savings, reduced empiricism, expansion of the design envelope, and reduction of design costs/time.
A key element of this will be to validate novel non-destructive evaluation (NDE) tools for component-scale composite samples, and linking the associated results to mechanical performance.
Collaborating with a team of researchers across the universities, the current PhD will particularly explore and optimise the role of high-resolution X-ray computed tomography (XCT) in meso-scale feature quantification in large composite components. This will require adaptation of conventional XCT scanning methods, specifically the large-scale use of laminography for flat/laterally extended objects, building on many years’ experience within the nationally and internationally recognised Southampton µ-VIS lab facility (www.muvis.org and www.nxct.ac.uk).
The project will involve sophisticated experimental (X-ray imaging) and computational aspects (3D image reconstruction).
Whilst primarily working with established software packages and toolkits, candidates are expected to demonstrate interest in coding and experience in Python, MATLAB or similar scientific computing environments.
The project is thus suitable for engineering, material science or physics graduates with strong computational skills.
