The ambitious targets in the United Kingdom for increasing the share of renewable energy sources integrated to the network, and the need for providing affordable, resilient and clean energy, call for a paradigm shift in energy systems operations. Electric vehicles offer the means to address these challenges and achieve uninterrupted operation by deferring their demand in time and acting as dynamic storage devices. As a result, their number is expected to increase rapidly over the next years, leading to a "green car revolution". This constitutes an opportunity for modernizing energy systems operation, but will unavoidably give rise to coordination and scheduling issues at a population level so that cost savings are achieved and reliability is ensured. The latter is of significant importance to prevent from undesirable disruptions of service. This project will address this problem using tools at the intersection of control theory, optimization and machine learning, allowing for a decentralized computation of the electric vehicle charging strategies, while preventing vehicles from sharing information about their local utility functions and consumption patterns that is considered to be private. We will develop algorithms capable of dealing both with cooperative and non-cooperative vehicle behaviours in large fleets of vehicles, and immunize the resulting strategies against uncertainty due to unpredictability in the vehicles' driving behaviour and due to the presence of renewable energy sources. The presence of an algorithmic tool with these features will allow for scalable charging solutions amenable to problems of practical relevance, will provide insight on the mechanism driving the response of large populations of electric vehicles, and embed robustness in the resulting charging schedules. As such, the proposed project will offer the means for reliable system operation and facilitate the integration of higher shares of renewable energy sources.

With increasing concerns over current CO2 levels and their association with climate change, research needs to establish a way to prevent further CO2 from reaching the atmosphere. Power production is the highest contributor of CO2 emissions to the atmosphere following by industrial process and transportation. Therefore, establishing technologies that extract the CO2 from these emissions before it reaches the atmosphere is considered the most viable solution. Since various types of CO2 capturing technologies have been developed over the past decade or so, one might ask, why is it that we are still not seeing these technologies rolled out yet? Here are a couple of reasons: - Expensive: There are various capture types but each of them consumed up to 40% of the power that is generated within the plant itself. This reduces the available energy for end-users, e.g., the general public, which is problematic since we are a nation that is increasingly dependent on technology. Longer power plants operation could top up energy lost to maintain increasing demands but this would increase the cost of energy to cover the additional production costs. - Size: Different technologies have different size requirements. A number can be retrofitted to existing plants, so space needs to be available for this, and other can only be applied to large plants to takes time for development and construction and is an all-round expensive route to take. - What about the CO2?: Capturing the CO2 is one thing but what to do with it after is another issue. Researchers continue to focus on its storage in underground depleted gas/oil reservoirs yet there are significant cost implications which occur in the run up to its storage, i.e., transport and injection, etc. Conversion of CO2 into a valuable and reusable product which subsequently closes the cycle would be the best option. This proposal brings together leading chemists, physicists and engineers at Southampton to develop a novel state-of-the-art technology that not only converts CO2 into a synthetic fuel but does so using solar energy. Optimised catalytic active sites incorporated into photonic fibres promote photochemical conversion of CO2 directly into synthetic fuel. Alongside this, computational models and simulations will provide physical insight to evaluate and optimise photonic-fibre catalytic converter technology for synthetic fuel generation. This will subsequently support the development of a lab-scale reactor which will demonstrate the scalability of this state-of-the-art technology. Engagement across the academic, industrial and public sectors will promote further opportunities for expansion and encourage development of early career researchers involved with the programme. The outcomes of the programme will lead to the development of not only new knowledge, but more importantly opportunities for impact within the energy sector.

In the next decade our economy and society will be revolutionised by ubiquitous Autonomous, Intelligent Machines and Systems, which can learn, adapt, take decisions and act independently of human control. They will work for us and beside us, assist us, and interact and communicate with us. The UK has the opportunity to become a world-leader in developing these technologies for sectors as diverse as energy, transport, environment, manufacturing and aerospace. This CDT directly addresses the present need to train future leaders capable of accelerating innovation in autonomy, and promoting it to some of the UK's largest sectors. This requirement can be met by cohorts of highly-trained individuals versed in the underpinning sciences of robotics, embedded systems, machine learning, wireless networks, control, computer vision, statistics & data analysis, design and verification. These disciplines are intimately related via the application and development of mathematical models and techniques implemented on computers to make predictions, take optimal decisions, perform inference and actions that are robust in the face of uncertainties at all levels. The synthesis of a range of disciplines is absolutely essential to train individuals in all aspects of autonomy, who will then be able to credibly communicate with large technical teams, and pioneer disruptive technologies into industrial labs. This CDT is focused on student training in algorithms, devices, and data feeds inherent to autonomous, intelligent machines & systems. To create and understand these complex systems, students need to be trained to program, embed and design software, to implement established and novel algorithms efficiently and correctly and to develop and apply models and decompositions which lie at the core of approaches to control, communicate, learn from, interpret and distil the large volumes of data endemic to autonomous systems. We believe that for a training centre to achieve its full potential in the AIMS area, it must recognise and respond to the synthesis of a number of component technologies. Students belonging to this CDT will be trained in both the fundamentals of autonomous systems engineering and the latest approaches and perspectives. The UK is faced with an increasing technology skills shortage, with a recent (2012) large-scale survey reporting that half of all key UK industries surveyed suffer from a worsening skills shortage (net.org.uk/news, June 2012). This is even more acute in high-tech industry and requires core investment in teaching highly-qualified cohorts. More specifically, the commercial potential of Autonomous Systems for the UK is tremendous, as demonstrated by the recent AAD KTN (Aerospace, Aviation & Defence Knowledge Transfer Network) study. Their research indicates "an untapped short term market value of circa £7bn per annum just for relatively low level autonomy products and services". Developing skills in designing and deploying autonomous systems will offer significant opportunities for growth to high priority sectors, as diverse as manufacturing, energy, smart buildings, intelligent transport systems, and defence. These sectors are in need of rapid change to reach targets of national importance, while still being able to compete in the global market. One of the main targets is the reduction of greenhouse gas emission (by 80% by 2050), which calls for energy-aware autonomous systems to become a cross-cutting technology in our society. Another driver of change is the growing and ageing population, which advocates the need for autonomous telecare, transport, efficient usage of public/private infrastructure, safety and security. Changing demographics, combined with strict emissions targets and budget cuts, raise unique challenges and opportunities for revolutionising key UK sectors.

With the Kigali Amendment coming into force in 2019, the Montreal Protocol on Substances that Deplete the Ozone Layer has entered a major new phase in which the production and use of hydrofluorocarbons (HFCs) will be controlled in most major economies. This landmark achievement will enhance the Protocol's already-substantial benefits to climate, in addition to its success in protecting the ozone layer. However, recent scientific advances have shown that challenges lie ahead for the Montreal Protocol, due to the newly discovered production of ozone-depleting substances (ODS) thought to be phased-out, rapid growth of ozone-depleting compounds not controlled under the Protocol, and the potential for damaging impacts of halocarbon degradation products. This proposal tackles the most urgent scientific questions surrounding these challenges by combining state-of-the-art techniques in atmospheric measurements, laboratory experiments and advanced numerical modelling. We will: 1) significantly expand atmospheric measurement coverage to better understand the global distribution of halocarbon emissions and to identify previously unknown atmospheric trends, 2) combine industry models and atmospheric data to improve our understanding of the relationship between production (the quantity controlled under the Protocol), "banks" of halocarbons stored in buildings and products, and emissions to the atmosphere, 3) determine recent and likely future trends of unregulated, short-lived halocarbons, and implications for the timescale of recovery of the ozone layer, 4) explore the complex atmospheric chemistry of the newest generation of halocarbons and determine whether breakdown products have the potential to contribute to climate change or lead to unforeseen negative environmental consequences, 5) better quantify the influence of halocarbons on climate and refine the climate- and ozone-depletion-related metrics used to compare the effects of halocarbons in international agreements and in the design of possible mitigation strategies. This work will be carried out by a consortium of leaders in the field of halocarbon research, who have an extensive track record of contributing to Montreal Protocol bodies and the Intergovernmental Panel on Climate Change, ensuring lasting impact of the new developments that will be made.

The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.