Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Composites have been used for a number of years in different sectors, including the aerospace, marine and construction industries. It is generally acknowledged that, compared to some traditional materials such as steel or aluminium, their design should aim for a higher utilisation ratio and ensure that proper attention is given to detailing, partly in order to offset higher material costs but also because of increased sensitivity to inhomogeneities, defects and, more generally, deviation from nominal properties. Coupled with the higher number of alternative designs that may be produced owing to directionality of properties, and the availability of many different material systems, the design and analysis tasks for composite structures need to be based on advanced and refined methods and tools. In this respect, finite element analysis is one such tool that can furnish the required information on structural response subject to general loading conditions. However, finite element analysis of composite structures is often undertaken by adopting some idealisations that are more appropriate to homogeneous materials, for example by making conservative assumptions regarding mechanical properties and geometric tolerances, in the absence of procedures that can account realistically with the spatial variation of such parameters within a component or structure.The aim of the proposed combined project is to develop a robust software tool for the design and analysis of composite plates, shells and sandwich panels, taking account of the random variability in geometric tolerances and mechanical properties, in other words accounting for random stiffness and strength influences on the predicted response of composite structures. The project combines experimental work, analytical and numerical modelling, in developing the required input models and algorithms for stochastic finite element analysis of composite structures. It brings together engineering materials technology, structural engineering and life cycle design, and strongly links these fields with topics in applied mathematics, such as optimisation and uncertainty modelling. In terms of academic partners it brings together two groups with distinct track records in relevant fields who can only achieve the overall project objective by working closely together. Hence, in addition to the deliverables to the engineering community, it is believed that the project will enhance significantly the research capability of the two groups, and will allow further research to be undertaken on the foundation that will be created through the execution of this project.

The drive towards lighter ships and thinner plate is restricted by the significant increase in distortion as the plate thickness decreases. Although welding has been the preferred process for metal joining for the last fifty years, distortion of the welded structures remains a major problem - typically, for a 6 mm thick plate distortions can be on the order of 60 mm. A recent study by the US Naval Sea Systems Command has estimated that the cost of distortion can up be to $3.4 million (approx. 2 million) per ship. While it is not expected that distortion can be eliminated completely, a reduction in the magnitude of the distortion will reduce significantly the costs to UK industry.In this work, the neural network approach, in conjunction with experimental measurements and the finite element method will be used to study the relationship between distortion of welded ferritic steel plates and the design parameters. The two key aspects of the problem, which will be investigated, are the interaction of process and production parameters in causing distortion and the influence of pre-existing (residual) stresses in the plate. By modelling the distortion process using material, design and welding parameters, the parameters can be optimised to minimise the resulting distortion. An existing artificial neural network (ANN) will be extended to allow examination of the distortion of the welded plate. The ANN will be trained, using results from measured plate and those obtained from a finite element code, and validated by the experimental work undertaken in the research program, to enable it to estimate plate distortion under a wide range of conditions. The combined effort will thus identify the parameters which cause distortion, assess the significance of each parameter and propose techniques to reduce distortion in welded plate.

Small unmanned aerial vehicles (SUAVs), or drones, have become incredibly popular due to advances in affordable technology which allow them to be very easily controlled, and fully autonomous GPS-guided SUAVs are now publicly available. However, the nefarious use of SAUVs is also increasing and they are now regarded as a potential threat to public privacy, safety and security. There have been many news reports of unwanted SUAV incursions in recent years including Angela Merkel being buzzed at an event in 2013, several mystery drones being sighted over Paris in early 2015, and two cases of SUAVs landing in the White House grounds in 2015. What if a SUAV-borne camera is being used for reconnaissance at a government building prior to a terrorist attack? What if a SUAV is flown into the path of an aircraft taking off at an airport? What if a larger SUAV equipped with a weapon or explosives is used to attack a large crowd or a nuclear power station? Sadly, these are all credible threats which we cannot ignore and this demands that we have the capability to detect intruding SUAVs and ultimately intervene to neutralise the threat. The successful detection of the intrusion into controlled spaces by small UAVs depends on being able to detect them reliably at sufficient range, i.e. in sufficient time, to be able to take appropriate evasive action whilst discriminating them from other harmless objects. Any sensor has to achieve this in locations containing complex structures and confusing background signals. Radar is one of the best techniques to achieve SUAV detection because it is capable of measuring the characteristic Doppler signatures of the rapidly rotating propellers and thus has the potential to distinguish SUAVs from other moving objects such as birds. However, conventional microwave radar struggles to detect the reflections from SUAVs due to their small size and low metal content. Our previous STFC-funded research has shown that shorter wavelength, millimetre wave radars are particularly well-suited to detecting low reflectivity, slowly moving targets. In that work we developed a radar to measure the Doppler signatures of tiny cloud particles moving high up in the atmosphere. In this project we propose to apply those same techniques to develop a low power, compact, millimetre wave radar demonstrator which will be capable of detecting SUAVs with high probability, and low false alarms, over a wide search area. We believe there are significant market opportunities for compact, low cost millimetre wave radars which may be deployed rapidly and as part of an ad hoc network that can be easily reconfigured to meet the demands of a wide range of site installations. We anticipate that variants of the radar will be suitable for both fixed, permanent installations (e.g. Centres of National Infrastructure (CNI), power stations, airports, government buildings, and data centres) and flexible, temporary deployments (e.g. VIP appearances, music festivals, motor sport events, sports arenas, parades / demonstrations). Our Project Partner, BAE Systems Applied Intelligence Labs, is very well placed to capitalise on these markets given their track record and experience. Beyond the direct economic opportunities of supplying equipment to this potentially large market, there are significant indirect economic benefits to being able to prevent SUAV intrusion. Preventing delays, evacuations or ultimately crashes at airports caused by SUAV intrusion will protect the enormous contribution these transport hubs make to the UK economy. Furthermore, preventing the unauthorised recording/filming of new movies and major sporting events from SUAV-borne cameras will protect valuable intellectual property. Of course, the threat from SUAVs is not limited to the UK: if a reliable system is developed from this research, it could be exported to foreign nations that share the same concerns, bringing further wealth to the UK economy.