Living standards in the UK are at significant risk from the rising costs of energy and the increasing gap between demand and the UK's generating capacity. Plugging this gap requires technological innovations which are affordable and can be implemented over reasonably short time-scales. An important area where efficiency gains can be achieved quickly is improving the management of heat released from industrial processes. All industrial and power generation processes produce heat which is often released into the environment in the form of high temperature exhaust products. New technologies are being developed to recover this otherwise wasted energy for use elsewhere, such as electricity, heating or cooling. If applied across the UK manufacturing sector, these technologies could save the energy output of around 20 power stations. Heat-recovery technologies are also used for renewable power from biomass, geothermal, solar-thermal sources and in de-centralized power generation. The development of heat recovery technology is therefore important in terms of cutting our carbon footprint as well as increasing UK energy security. Heat recovery systems work by transferring heat into a high-pressure working-fluid, using a heat exchanger. In order to produce electricity, the working fluid drives a turbine which is connected to an electrical generator. Heat recovery systems often use working fluids which are refrigerants or long-chain hydrocarbons. The properties of these working fluids differ greatly from those which have traditionally been used within turbines (such as air within aero-engines/gas-turbines or water vapour within steam turbines) and can be made up of several components including mixtures of gases and liquids. There is very little known about the behaviour of these unconventional working fluids within turbines largely due to a lack of experimental data with which to test current theories. This is important because turbine designers require accurate models in order to develop high performance machines, and uncertainties in the modelling can have a detrimental impact on both the development costs and the overall performance of a heat recovery system. There is also a potential to exploit the unusual behaviour of these working fluids, such as their ability to change from liquid to gas across the turbine, which can be exploited to increase system power to size ratios (power density) in ways not possible using normal working fluids like water. The project will explore how the behaviour of multi-component fluids can be used to increase turbine performance. In order to achieve this, the work will involve developing methods to simulate multi-component fluids within turbines. The project will use experiments and computational techniques to model these flows and use the results from this work to improve current computational methods. The project involves a collaboration with GE who are global leader in the design, manufacture and supply of heat recovery systems. GE will incorporate the results of this work into their design systems. In doing so, the results from this project will accelerate the development of heat-recovery technologies which will be used world-wide.

A small reduction in NOx emission per kilo-watt of generated power will have a significant reduction in environmental impact of combustion used for power generation. The MILD (Moderate or Intense Low-Oxygen Dilution) combustion technique offers an opportunity to drastically reduce emissions while improving thermal efficiency of furnaces and boil-ers. In gas turbines, though overall fuel-air mixture is fuel-lean and MILD combustion is not directly applicable, fuel-rich regions in the primary zone of the combustor exhibit localised MILD regimes, particularly for liquid fuel operation How-ever, the physical and chemical intricacies of this novel technique are not well understood and thus identifying key con-trol parameters for using this technique for power generation and industrial processes over wide range of conditions is challenging. This project aims to provide a step change in physical understanding and modelling of this combustion technique and to identify key control parameters. The aim is to investigate MILD combustion of high calorific value gaseous and liquid fuels for practical application using Direct Numerical Simulations (DNS) and Large Eddy Simula-tions (LES), with high-fidelity mathematical description for physical and chemical processes involved. The droplets of liquid fuel spray will be tracked using the Lagrangian approach while the gas phase is treated using the Eulerian ap-proach for the simulations. The effects of droplet diameter, equivalence ratio (both for gaseous and liquid fuels), extent of dilution by combustion products, volatility (by considering different fuels), turbulence intensity and its length scale on the burning rate, flame structure (in terms of chemical reaction pathways analysis and flame and flow topologies) and pollutants formation will be analysed based on a judicious parametric analysis based on three-dimensional detailed chemistry DNS data. In this project, the fundamental physical understanding extracted from DNS data will be utilised to develop high-fidelity models for engineering Computational Fluid Dynamics (CFD)-based simulations to identify key control parameters using LES after validating these models against the available experimental results. This project will provide (1) a ro-bust modelling framework for MILD combustion technique, which would be a cost-effective reliable tool for designing energy-efficient and clean gas turbines and industrial furnaces and (2) the key control parameters identified can help to design retro-fit "greener" combustion systems.

The market for photovoltaic (PV) solar modules is experiencing astonishing growth due to increasing energy demand, security of supply issues, increasing cost of fossil fuels and concerns over global warming. The world market for photovoltaics grew by 139% to 21GW in 2010. Although this extraordinary pace of growth is unlikely to be maintained in the short term it will advance rapidly again at the point where grid parity is achieved. It is important that the UK retains a strong research presence in this important technology. It is proposed that the SUPERSOLAR Hub of Universities be set up to co-ordinate research activities, establish a network of academic and industrial researchers, conduct cross-technology research and provide a focus for international co-operation. SUPERSOLAR is led by CREST at Loughborough University and supported by the Universities of Bath, Liverpool, Oxford, Sheffield and Southampton. This group is active in all of the PV technologies including new materials, thin film chalcopyrite, c-Si, thin film a-Si, dye sensitised solar cells, organic PV, concentrator PV, PV systems performance and testing. SUPERSOLAR will set up a solar cell efficiency measurement facility for the benefit of the PV community in the UK. The consortium contains a deliberate balance of expertise, with no bias towards any one technology.

The Programme is organised into two themes. Research theme one will develop new computer vision algorithms to enable efficient search and description of vast image and video datasets - for example of the entire video archive of the BBC. Our vision is that anything visual should be searchable for, in the manner of a Google search of the web: by specifying a query, and having results returned immediately, irrespective of the size of the data. Such enabling capabilities will have widespread application both for general image/video search - consider how Google's web search has opened up new areas - and also for designing customized solutions for searching. A second aspect of theme 1 is to automatically extract detailed descriptions of the visual content. The aim here is to achieve human like performance and beyond, for example in recognizing configurations of parts and spatial layout, counting and delineating objects, or recognizing human actions and inter-actions in videos, significantly superseding the current limitations of computer vision systems, and enabling new and far reaching applications. The new algorithms will learn automatically, building on recent breakthroughs in large scale discriminative and deep machine learning. They will be capable of weakly-supervised learning, for example from images and videos downloaded from the internet, and require very little human supervision. The second theme addresses transfer and translation. This also has two aspects. The first is to apply the new computer vision methodologies to `non-natural' sensors and devices, such as ultrasound imaging and X-ray, which have different characteristics (noise, dimension, invariances) to the standard RGB channels of data captured by `natural' cameras (iphones, TV cameras). The second aspect of this theme is to seek impact in a variety of other disciplines and industry which today greatly under-utilise the power of the latest computer vision ideas. We will target these disciplines to enable them to leapfrog the divide between what they use (or do not use) today which is dominated by manual review and highly interactive analysis frame-by-frame, to a new era where automated efficient sorting, detection and mensuration of very large datasets becomes the norm. In short, our goal is to ensure that the newly developed methods are used by academic researchers in other areas, and turned into products for societal and economic benefit. To this end open source software, datasets, and demonstrators will be disseminated on the project website. The ubiquity of digital imaging means that every UK citizen may potentially benefit from the Programme research in different ways. One example is an enhanced iplayer that can search for where particular characters appear in a programme, or intelligently fast forward to the next `hugging' sequence. A second is wider deployment of lower cost imaging solutions in healthcare delivery. A third, also motivated by healthcare, is through the employment of new machine learning methods for validating targets for drug discovery based on microscopy images