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Characterising the microstructure of composite explosives by mining X-ray CT scan datasets PhD


Project Description

The cook-off response of explosives of the same type varies quite markedly, even though current characterisation techniques (such as X-ray CT scanning) seem to show them to have the same properties. It is suspected that variations in the microstructure are responsible for this variation, but the analysis of CT-scan data is not well understood. This PhD opportunity will investigate techniques to reduce and analyse CT-scan datasets, to reveal microstructural differences.

Experimental work, particularly in cook-off, has often shown that different explosiveness is observed in experiments using ostensibly the same explosive material and test geometry. For example in the EMTAP tube test, it is common to observe mild pressure bursts and ‘detonations’ within the same ten-shot series. This shows that among the specimens presented for test there are important differences that drive hazard response, which are not revealed by the existing characterisation techniques (measurement of density, estimation of void content, etc).

High-resolution CT scanning can reveal the internal structure of explosive composites non-destructively, particularly those that consist of explosive/binder systems where the two components are of markedly different densities.

It is hypothesised that 3D datasets may contain structures, or possibly populations of structures, whose presence or absence provides a mechanism by which poor hazard responses are realised in finished pellets. This project therefore proposes to classify the different types of features that are found in 3D scans of a set of apparently similar explosive pellets, and to attempt to relate these to the responses observed in a suitable experimental method.

Several appropriate experimental methods are available and mature, and this is not the important aspect of the research. Instead the research should focus on data handling and software techniques that can be used to automatically extract and classify the features contained in 3D scan datasets.

Funding Notes

Sponsored by AWE, EPSRC and Cranfield University, this studentship will provide a bursary of up to £17,000 (tax free) plus fees* for 4 years.

NOTE: If a student is in receipt of government funding for their degree course the advert must state that they will not eligible to apply for a Postgraduate Doctoral Loan.

Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit an applicant with a computing or materials science background.

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