The aim of this project is to develop a system for the stand-o detection (typically ten metres) of concealed body-worn explosives. The system must be capable of detecting a layer of explosive material hidden under clothing and distinguishing explosives from everyday objects. Millimetre wave radar is suitable for this application. Millimetre Waves are suitable because they are not signi cantly attenuated by atmospheric con- ditions and clothing textiles are practically transparent to this radiation. Detection of explosive layers from a few mm in thickness to a few cm thickness is required. A quasi optical focussing element is required to provide su cient antenna directivity to form a narrow, highly directional beam of millimetre waves, which can be directed and scanned over the person being observed. A system of antennae and focussing optics has been modelled and built using designs from nite element analysis (FEA) software. Using the developed system, represen- tative data sets have been acquired using a Vector Network Analyser (VNA) to act as transmitter and receiver, with the data saved for processing at a later time. A novel data analysis algorithm using Matlab has been developed to carry out Fourier Transforms of the data and then perform pattern matching techniques using arti cial neural networks (ANN's). New ways of aligning and sorting data have been found using cross-correlation to order the data by similar data slices and then sorting the data by amplitude to take the strongest 50% of data sets. The signi cant contribution to knowledge of this project will be a system which can be eld tested and which will detect a layer of dielectric at a stando distance, typically of ten metres, and signal processing algorithms which can recognise the di erence between the response of threat and non-threat objects. This has partially been achieved by the development of focussing optics to acquire data sets which have then been aligned by cross-correlation, sorted and then used to train a pattern matching technique using neural networks. This technique has shown good results in di erentiating between a person wearing simulated explosives and a person not carrying simulated explosives. Further work for this project includes acquiring more data sets of everyday objects and training the neural network to distinguish between threat objects and non-threat objects. The operational range also needs increasing using either a larger aperture optical element or a similarly sized Cassegrain antenna. The system needs adapting for real time use with the data processing techniques developed in Matlab. The VNA is operated over a band of 14 to 40 GHz, future work includes moving to a stand-alone transmitter and receiver operating at w-band (75 to 110 GHz).

In the field of explosive science, the existence of the ‘hot-spot’ is generally accepted as essential to any theory on shock initiation. Continuum-based shock initiation models only account for ‘hot-spots’ implicitly, and the majority of these models use pressure-dependent reaction rates. The development of a simple but physically realistic model to predict desensitisation (double shock) effects within the confines of an existing pressure-based model is described and simulations compared with experimental data with mixed results. The need to invoke a separate desensitisation model for double shocks demonstrates that reaction rates are not substantially dependent on local pressure. The newly developed continuum, entropy-dependent, CREST model has been implemented and validated in a number of hydrocodes. However, the move to entropy-based reaction rates introduces a number of computational problems not associated with pressure-based models. These problems are described, in particular, an entropy-dependent model over-predicts the rate of energy release in an explosive adjacent an impact surface, and requires a finer mesh than a pressure-dependent model to achieve mesh converged results. The CREST model, fitted only to onedimensional data of the shock to detonation transition, is shown to be able to accurately simulate two-dimensional detonation propagation data. This gives confidence in the predictive capability of the model. To account for ‘hot-spots’ explicitly, a simple model to describe ‘hot-spot’ initiation has been developed. The simple model is presented where ‘hot-spots’ are formed as a result of elastic-viscoplastic stresses generated in the solid explosive during pore collapse. Results from the model compare well with corresponding results from direct numerical simulations, and both are consistent with observations and commonly held ideas regarding the shock initiation and sensitivity of heterogeneous solid explosives. The results also indicate that viscoplastic ‘hot-spot’ models described in the literature are built on an invalid assumption.

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