The overall aim of this project is to develop modelling techniques and tools for improving railway capacity while ensuring that safety standards are maintained.The railway domain has been identified as a grand challenge for computer science. Due to its safety-critical nature, various formal methods have been applied in this domain, where, most prominently, the B method has been successfully used to verify several lines, including those in Paris and San Juan Metro. Solely concerned with safety, most approaches have, however, ignored the aspect of time. Furthermore, a rigorous treatment of time is widely recognised as a challenge in its own right by the computer science community.And yet the capacity of a rail network node is highly dependent on time: moving a point or a train through a node takes time, and sighting and braking distances are functions of time. This is why we propose extending Event-B, a modern variant of the B method, with reasoning about time and underpinning it with various tools for simulation, analysis and verification. To this end, we will integrate Event-B with process algebra CSP. This will make it possible to re-use proof support developed for CSP. Overall, our approach will allow an integrated view of rail networks, within which capacity can be investigated without compromising safety.In our project, we will handle time precisely, i.e. without any rounding errors. In simulations, this can be achieved by using the rationals in the language Haskell; in proofs, the theorem prover Isabelle/HOL includes proper real numbers (as well as rationals). We will extend the interactive proof tool CSP-Prover and build a new tool support. By relying on such tool support, the railway engineer will be able to model and evaluate the impact on capacity of altering track layouts, signalling principles, driving rules and control algorithms. By integrating our tool into the Event-B tool environment, our project will deliver a software development platform that would allow engineers to model, simulate, analyse and verify railway network nodes (both junctions and stations) in an integrated way, combining reasoning about capacity and safety.To achieve our overall aim of improving railway capacity, we intend to meet the following technological (T) and scientific (S) and objectives:1) To integrate proof-based reasoning about time in state-based models, exemplified by Event-B and CSP-Prover, and to provide an open tool support for verifying timed systems (S).2) To develop an intuitive graphical domain-specific language for the railway domain with a tailored tool support based on the Rodin framework (T)3) To identify and validate design patterns for improving capacity by altering route design, track layout, signalling principles and driving rules (T)Throughout the project, our industrial partner Invensys Rail will provide the project team with track plans and control software, which will be used as case studies in order to challenge our approach with realistic data sets. Regular meetings and workshops involving Invensys Rail will give the practical feedback necessary to come up with solutions which are viable for the rail industry. Invensys Rail's successful experience of improving the capacity of metro railways by using smarter control solutions will be an invaluable contribution to this work.The results of the project will be used to evaluate the viability of approaches to improving railway capacity and to prepare the deployment of the developed solutions in the railway industry.

It is often considered that there are two types of scientist: theoretical and experimental. Theoretical scientists invent/discover new theories to explain the natural world. Experimental scientists invent experiments to test these theories. For example, the theoretical physicist Albert Einstein invented the theory of General Relativity, and astronomers tested General Relativity by inventing the idea of observing the position of stars near the Sun during a solar eclipse. Artificial Intelligence (AI) is increasingly being used in scientific research. Almost all of this effort is in the formation of new hypotheses to explain data. This corresponds to what theoretical scientists do. Far less effort has been put into automating what experimental scientists do. This is what the proposed research focus on. We propose to develop a 'Robot Experimentalist'. This will be an AI system that when given a hypothesis to be tested, and a description of a set of laboratory equipment, will be able to plan an experiment to test the hypothesis. To make the problem tractable we will focus on experiments using the yeast S. cerevisiae. This is the organism used to make bread, beer, and wine; but its main role in biology is as a model for human cells. Surprisingly, most of what is true for S. cerevisiae is also true for H. sapiens. We will test the Robot Experimentalist using laboratory robots in the UK and Japan.

Radio frequency glow discharges are been used in microelectronic device fabrication, ozone generation and in gas laser excitation. Such discharges operating at atmospheric pressure have been shown to produce jet flows to be used for flow control. Some of recent results obtained from our laboratory clearly confirmed these claims. Surface plasma actuators are simple device with no moving parts or ducting, which have high frequency response and thus have a realistic possibility for aeronautical applications. Already, tests have been conducted for airfoils and turbine blades for possible control of transition, skin-friction drag and flow separation in the last year of so. However, there is still a lack of information on surface plasma physics and associated fluid dynamics to fully utilise the devices for flow control. The production mechanism of wall jets by surface plasma is not well understood, nor is the optimum condition for plasma excitation in flow control. These are precisely the reasons why we propose this research, so that we can advance our understanding on surface plasma for many aeronautical applications, flow separation control in particular.In this investigation we would like to study active control of flow separation during static and dynamics stall. Control of static stall can be investigated by placing surface plasma actuators before the separation point over a circular cylinder with a view to delay flow separation. Here, the time averaged lift and drag forces should indicate the effectiveness of separation control. Control of dynamics stall over a lifting surface can be carried out by reducing the area of separation region or even to recover from separation by using surface plasma actuator. Novelty of this approach is that a real-time detection of flow separation over the body surface is not required, as the vortices are periodically shed from the cylinder surface. Besides, the flow around a circular cylinder is a subject that has been studied by many researchers, therefore there are enough database to help validate our baseline measurements.PIV (Particle Image Velocimetry) system is becoming a common flow measurement technique in fluid dynamic research in recent years, where an entire velocity field in a light-sheet plane can be obtained. With PIV system, small particles in the flow shone by the laser light sheet are photographed in a short interval with a digital camera. The distance and direction of movement of each flow particle gives the velocity vector, thereby globally mapping the velocity field. In our study, flow images will be captured at 1 kHz at a full camera resolution of 1600x1200 pixels for 8 seconds, with 20 mJ of energy being produced by the laser. All of these equipments will be made available from EPSRC Engineering Instrument Loan Pool for this study. The PIV measurements will be complimented by other techniques, such as hot-wire measurements and flow visualisation, which will give confidence in our results, add insight into vortical structures during flow separation and provide better understanding of the mechanisms in which flow separation control with surface plasma can be carried out.

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.

Current photovoltaic (PV) technologies rely on physical principles that fundamentally limit the maximum solar cell efficiency, i.e. first and second generation technologies cannot produce efficiencies above ~31%. Both silicon-based and non-silicon devices are progressively approaching this limit with improved stability and device performance at reduced costs. It follows that significant improvement in device efficiency can be achieved only by deploying technologies that rely on new physical principles, so called third generation PV; this has been clearly highlighted in relevant UK and international PV roadmaps. In third generation solar cells quantum dots (QDs) often represent an important component and therefore methods to produce QDs that are low-cost, non-toxic and environmentally friendly are required. Currently the most efficient third generation solar cells use elements such as lead (Pb), cadmium (Cd), Selenium (Se) and tellurium (Te) which are either toxic or rare or expensive. This research program deals with the synthesis and study of novel, low-cost, non-toxic and sustainable QDs from a combination of elements such as silicon, nitrogen, carbon and a range of low-cost, non-toxic and abundant metals. Furthermore the research will produce QDs with processes based on atmospheric pressure plasmas that are highly suitable to produce tailored properties and lead to material compositions not achievable with other methods. These proposed plasma processes can also be easily integrated in manufacturing lines for the production of full third generation solar cells.