To meet the 2020 renewable energy target the UK is going to need biomass, and lots of it. DECC has an aspiration for an additional 20-38TWh of biomass electricity by 2020 and this will require around 12-23 million dry tonnes of biomass. This is a huge quantity of material, the vast majority of which would be imported as pellets from Canada and the USA and burnt in converted coal fired power plants. Other imported feedstocks for liquid fuels might include Brazilian ethanol from sugar cane and oils from palm oil in Southeast Asia. The UK is not alone in wanting to use more biomass. The Netherlands, Belgium, Denmark, and Sweden all expect to use more, and estimates of future EU demand for wood pellets alone, for example, range from 23-80 million tonnes. One single coal power station in the UK is looking to source up to 10 million tonnes of biomass each year. If the UK wants biomass power on a large scale it is clear that the power generators will need to become major players in the transatlantic wood pellet trade. Against this background of increased demand, there remains significant uncertainty on whether the use of biomass for energy is environmentally sustainable. Any type of managed land use can incur a carbon 'debt' - a net loss of carbon or other greenhouse gases to the atmosphere that contributes to global warming. Other greenhouse gases include methane and the oxides of nitrogen. But quantifying the net impact of a bioenergy crop relative to what it might replace (sometimes called the counterfactual), is less than straightforward. This has led to many claims and counter-claims from commercial interests, environmental groups and academics, on the real greenhouse gas impact of land use change to bioenergy systems, where there still remains much disagreement and controversy. The project described here is aimed at addressing this controversial issue - quantifying the real GHG balance of different land use transitions to bioenergy crops, for both UK and imported bioenergy feedstocks. We will deploy sophisticated state-of-the-art instrumentation that is able to measure GHGs very rapidly, to gain a better insight into the dynamic range of GHG emissions that can occur in such systems, including when fields are ploughed and planted and when fertilisers are added. Following data collection, we will extend our analysis by modelling a wide geographical range across the UK and for biomass feedstock sourced from other areas of the world. The models we use should work if we can utilise available datasets, globally, for weather, soils and yield data of the range of crops of interest. The GHG data in such systems are usually used to develop emissions factors that are inputted into whole life cycle assessments (LCAs) of carbon (or C equivalent) costs, but these in the past have often been unverified data. We will assess the quality of past data and from our measurement and model campaigns we can test the effectiveness of emissions factors and how they might be improved from our work, including for overseas feedstocks. Finally, in an allied project we have developed a value chain model to optimise the technology options for the UK for bioenergy, depending on how cost, GHG balance and land availability are defined. We will run this model to identify the best bioenergy chains, in terms of GHG balance, for the UK and test scenarios ('what if' questions), to determine how much imported feedstock might be sustainable in the future.

The efficiency, safety and reliability of a wide range of engineering systems in the energy sector rely strongly on the performance of their structural components. Increasing energy efficiencies, achieved by maximising operating temperatures, will drive down CO2 emissions and is therefore essential to meet stringent legislation and the UK's and international short and long-term energy goals. Engineering components operate under adverse conditions (stress, temperature and harsh environments) causing their degradation and failure by deformation and fracture processes. Existing energy facilities are aging beyond design life and require life extension to secure short-term energy supplies. Reliable component lifetime assessment is therefore vital to ensure safe operation. New build nuclear reactors will soon be developed and future reactors designed for very high temperature operation and superior performance. Plans are also advanced for the construction of the next generation of conventional power stations with excess operating temperatures and efficiencies. Opportunities are now emerging to exploit a novel collection of innovative techniques, at micro and macro length scales, to obtain a fundamental understanding of material failure mechanisms. These will enable advanced materials and component designs with predictable in-service behaviour, which are crucial to innovation in the energy sector and the key for overcoming the outstanding challenges.Emerging experimental techniques can now reveal the processes, and quantify the extent of deformation and damage in a material as it occurs. High-energy X-ray tomography measurements will give detailed quantitative 3D volumetric insights of damage development, coalescence and failure mechanisms in the bulk of specimens at micro-length scales, during deformation under stress at temperature. In addition, complimentary non-destructive tools will be innovated for practical monitoring of large scale component degradation. At a range of length scales, a digital image correlation technique will be used to measure 3D surface strains on various geometries, and will provide evidence of the influence of defects and material inhomogeneities due to welding processes on strain fields and their evolution with time.High performance computing now facilitates advanced models to simulate material behaviour and structural components' response under various operating conditions. Experimental results will provide the basis for validated mechanistic models of material deformation and failure behaviour, which will be developed and incorporated into 3D computational models that can also include various regions of inhomogeneous material behaviour. This novel collection of advanced experimental techniques, combined with the verified computational models, will provide new powerful tools that are essential to understand and predict component failure, advance designs and optimise their operation.Initially, power plant steels will be examined. However, the methodologies developed can be extended to a wide range of materials relevant to e.g. aerospace, heat and power generation, marine and chemical technologies. The outcomes will lead to methods for component on-line monitoring, predictive multi-scale modelling of materials' initial and through-life properties and the development of accurate assessment procedures for component lifetime predictions that leads to the required plant life extension. Social and economical benefits include minimised environmental impacts, secure supplies, reduced maintenance costs and increased safety. The close collaboration with industry (including partners British/EDF Energy and E.ON) will provide an effective knowledge transfer mechanism between industry and academia, ensure industrial relevance and provide inspiration to a new generation of researchers. This fundamental, timely research is therefore valuable across industrial sectors in addition to the scientific community.

Tides occur due to the changing gravitational movement of the Moon and Sun relative to the Earth. As astronomical movements are highly predictable the tides should also be predictable. This is one of the key advantages of tidal stream energy (a rapidly developing source of renewable energy). The existing methods which are used to predict tidal movements perform very well for predicting water levels and slow moving currents, but often perform very badly on fast flowing tidal streams of the type in which we areinteresting in placing tidal turbines. This project will address this by applying methods from the machine learning community to the analysis of fast flowing tidal streams. This will produce an algorithm which will allow users from the oceanographic and tidal energy community to greatly improve the prediction of tidal currents at any point indefinitely far into the future. Thus a robustprediction of the performance of tidal stream turbines can be obtained. In the rapidly growing area of tidal stream energy, accurate knowledge of the tidal currents is vital for: robust predictions of energy yield; for the calculation of loads and the design of the turbine; and to give confidence to investors.

Fossil fuels are society's major energy sources and the primary raw materials for the chemicals industry. However, there are significant concerns associated with their sustainability, depletion and cost. In particular, many of the UK's North Sea reserves will soon become uneconomic / depleted, so we need to find alternatives urgently. Furthermore, the combustion of fossil fuels, e.g. during energy conversion, releases carbon dioxide and other greenhouse gases that contribute to global warming. The UK is already committed to an 80% reduction in greenhouse gas emissions by 2050, but significantly greater reductions (85%) are likely to be necessary in order to prevent devastating climate change (>2 degree C increase in temperature). Energy conversion for electricity and transport is responsible for 74% of CO2 emissions; new sustainable energy sources are essential. These new energy sources must be CO2 neutral or, even better, CO2 depleting. One solution is to use carbon dioxide itself as the fuel and feedstock material. Our solution is to react CO2 with H2 or water, using chemical, photochemical or electrochemical catalysts, to produce liquid transport fuels, such as methanol. Flue gases from power stations and/or industrial process, such as metal/alloy manufacture, are major contributors to UK CO2 emissions and will be abundant sources of CO2 for the foreseeable future. Many other industrial emissions also contain considerable concentrations of CO2 including those derived from biological processes, e.g. fermentation. The hydrogen required will be produced by water electrolysis powered by solar or other renewable source of energy. The key economic issue lies in decreasing the energy required for the processes. We aim to achieve this via the development of new, highly active metal/metal oxide nano-structured catalysts, which offer superior performance due to their high surface areas, reduced loadings, low overpotentials and which can be synthesised controllably. We shall use three parallel, yet complementary, approaches to energise the process: direct chemical (thermal) hydrogenation, electrochemical and photochemical reductions of carbon dioxide and water.Our team comprises scientist, engineers and environmental policy researchers at Imperial College London and University College London. We have expertise in chemical catalysis, electrochemistry, photochemistry, reactor engineering, materials science, nanotechnology, sustainable chemistry and environmental science. We have a significant track record in the activation and use of carbon dioxide as a resource. The project will also involve collaborations with, and be support by, the Imperial College London Centre for Carbon Capture and Storage (CCS), the Energy Futures Lab and the Grantham Institute for Climate Change.

This project is concerned with the development of ultrasonic techniques for the detection of fatigue and creep damage in materials. This development will allow the detection of damage at earlier stages in power plants and aero engines, resulting in the ability to operate these systems safely for much greater periods. The noncollinear interaction of ultrasound with material nonlinearity will be developed and employed due to its great potential for practical applications. The development and comparison of nonlinear inspection techniques, through modelling and experiment, will provide academic and industrial users with a clear, unambiguous description of the relative performance levels and usefulness of nonlinear ultrasonic inspection techniques, helping future users make the best decisions as to which approach to apply. Finally the testing of this approach on real world samples will confirm its practical applicability. The result will be an understanding of how nonlinear ultrasonic techniques can be used to detect previously undetectable damage in specimens and predict the remaining life in components.