We live in the age of data. Technology is transforming our ability to collect and store data on unprecedented scales. From the use of Oyster card data to improve London's transport network, to the Square Kilometre Array astrophysics project that has the potential to transform our understanding of the universe, Big Data can inform and enrich many aspects of our lives. Due to the widespread use of sensor-based systems in everyday life, with even smartphones having sensors that can monitor location and activity level, much of the explosion of data is in the form of data streams: data from one or more related sources that arrive over time. It has even been estimates that there will be over 30 billion devices collecting data streams by 2020. The important role of Statistics within "Big Data" and data streams has been clear for some time. However the current tendency has been to focus purely on algorithmic scalability, such as how to develop versions of existing statistical algorithms that scale better with the amount of data. Such an approach, however, ignores the fact that fundamentally new issues often arise when dealing with data sets of this magnitude, and highly innovative solutions are required. Model error is one such issue. Many statistical approaches are based on the use of mathematical models for data. These models are only approximations of the real data-generating mechanisms. In traditional applications, this model error is usually small compared with the inherent sampling variability of the data, and can be overlooked. However, there is an increasing realisation that model error can dominate in Big Data applications. Understanding the impact of model error, and developing robust methods that have excellent statistical properties even in the presence of model error, are major challenges. A second issue is that many current statistical approaches are not computationally feasible for Big Data. In practice we will often need to use less efficient statistical methods that are computationally faster, or require less computer memory. This introduces a statistical-computational trade-off that is unique to Big Data, leading to many open theoretical questions, and important practical problems. The strategic vision for this programme grant is to investigate and develop an integrated approach to tackling these and other fundamental statistical challenges. In order to do this we will focus in particular on analysing data streams. An important issue with this type of data is detecting changes in the structure of the data over time. This will be an early area of focus for the programme, as it has been identified as one of seven key problem areas for Big Data. Moreover it is an area in which our research will lead to practically important breakthroughs. Our philosophy is to tackle methodological, theoretical and computational aspects of these statistical problems together, an approach that is only possible through the programme grant scheme. Such a broad perspective is essential to achieve the substantive fundamental advances in statistics envisaged, and to ensure our new methods are sufficiently robust and efficient to be widely adopted by academics, industry and society more generally.

The main aim of the work is to gain a better insight into the operation of near-future advanced internal combustion engine strategies. Such understanding is vital for the development of high-efficiency, ultra-low-emissions engines to meet environmental regulations. For example, the European automotive manufacturers have committed to reduce fleet average CO2 emissions to 140g/km by 2008, with 120g/km projected by 2012. Hybridized SI-HCCI-SI engine technology is a potential solution towards achieving such targets in improving fuel consumption and developing near-zero emissions vehicles. Such hybridized operation could enable a reduction in UK CO2 levels of ~0.7million metric tons per annum (for a representative 2.0 l gasoline engine size). Furthermore, the benefits of 99% reduction (c.f. SI) in NOx emissions and virtually no soot emissions during HCCI mode of operation can be realised with this technology. In addition to experimental research, computational modelling has been utilized by the research community to gain insight into the transients associated with such a hybridized engine operation. However, the existing models are empirical in nature and rely on profiles from experiments. This may also be the reason for the absence of numerical analysis to investigate the effect of the complex and dynamic transient phenomena on the regulated emissions. The proposed research involves the development of an advanced, predictive phenomenological model to simulate the SI-HCCI-SI engine transients. The model will be validated against measurements and further improved with the help of some new experiments suggested in this proposal. The proposed work comprises of three parts: 1) Development of a novel computational model to account for spontaneous multi point ignition (HCCI-like) as well as premixed flame propagation (SI-like) during the transients. The model includes detailed chemical kinetics description and accounts for inhomogeneities in composition and temperature, thus proving beneficial in understanding the impact of the transient processes on CO, HC and NOx emissions. 2) Understanding transient-like operation by carrying out cost-effective experiments involving operating conditions representative of the complex transient phenomena. These measurements will also be used in validating the formulated model. 3) Model validation against experimental results obtained from fully variable valve timing (FVVT) capable SI-HCCI-SI transient engine operation. Overall, this congruent experimental and modelling approach involves sharing the know-how and expertise between academic research and industrial partners aimed at realising ultra-low emissions engine performance.

In the search for renewable and carbon-free fuels, the use of ammonia is considered an attractive solution for engine and gas turbine applications. When compared to other carbon-free fuels, such as hydrogen, it has significant advantages. Not only is it easy to produce from renewable sources of nitrogen and hydrogen, it is safer to store and transport and has a higher energy content. Furthermore, it can be produced, transported and distributed without changing the infrastructure already deployed by industries. However, for the successful application of ammonia as a fuel, one main challenge related to its combustion needs to be overcome: its low reactivity requires a high ignition energy, a narrow flammability range and low burning velocity. This complicates the stabilisation of the combustion flame and thus inevitably causes unreliable ignition and unstable combustion. The combustion of clouds of fuel droplets (or aerosol clouds) is of practical importance in gas turbines, diesel and spark ignition engines, furnaces and hazardous environments. There is experimental evidence that, contrary to expectations, flame propagation in aerosol clouds, under certain circumstances, is higher than that in a fully vaporised homogeneous mixture (possibly by up to a factor of 3). Also, the presence of fuel droplets is shown to enhance the generation of flame wrinkling instabilities. With richer mixtures and larger droplets, it is possible for droplets to enter the reaction zone and further enhance existing gaseous phase instabilities through the creation of yet further flame wrinkling. Therefore, the flame experiences periodic deceleration and acceleration with these oscillations lasting for several cycles within 100ms. Surely, the burning velocity enhancement may be advantageous in giving more rapid burning when burning ammonia in a gas turbine. As ammonia aerosol combustion has not been extensively studied yet, it is necessary to make clear to what extent ammonia aerosol flames inherit this oscillating behaviour as this oscillation of the flame will couple with thermo-acoustic oscillations and damage the turbine blades. While some theoretical research has studied flame propagation in aerosol clouds, the processes governing flame oscillations are still unclear, especially for ammonia. We will use the numerical techniques and hydrodynamics codes developed at the University of Leeds for STFC-funded astrophysical research to increase our comprehension of this phenomenon and advance ammonia as a carbon-free fuel.

Moore's Law states that the number of active components on an microchip doubles every 18 months. Variants of this Law can be applied to many measures of computer performance, such as memory and hard disk capacity, and to reductions in the cost of computations. Remarkably, Moore's Law has applied for over 50 years during which time computer speeds have increased by a factor of more than 1 billion! This remarkable rise of computational power has affected all of our lives in profound ways, through the widespread usage of computers, the internet and portable electronic devices, such as smartphones and tablets. Unfortunately, Moore's Law is not a fundamental law of nature, and sustaining this extraordinary rate of progress requires continuous hard work and investment in new technologies most of which relate to advances in our understanding and ability to control the properties of materials. Computer software plays an important role in enhancing computational performance and in many cases it has been found that for every factor of 10 increase in computational performance achieved by faster hardware, improved software has further increased computational performance by a factor of 100. Furthermore, improved software is also essential for extending the range of physical properties and processes which can be studied computationally. Our EPSRC Centre for Doctoral Training in Computational Methods for Materials Science aims to provide training in numerical methods and modern software development techniques so that the students in the CDT are capable of developing innovative new software which can be used, for instance, to help design new materials and understand the complex processes that occur in materials. The UK, and in particular Cambridge, has been a pioneer in both software and hardware since the earliest programmable computers, and through this strategic investment we aim to ensure that this lead is sustained well into the future.

Along the well-to-wake value chain from upstream processes associated with fuels production and supply, components manufacture, and ships construction to the operation of ports and vessels, the UK domestic and international shipping produced 5.9 Mt CO2eq and 13.8 Mt CO2eq, respectively in 2017, totalling 3.4% of the UK's overall greenhouse gas emissions. The sector contributes significantly to air pollution challenges with emissions of nitrogen oxide, sulphur dioxide and particulate matters, harming human health and the environment particularly in coastal areas. The annual global market for maritime emission reduction technologies could reach $15 billion by 2050. This provides substantial economic opportunities for the UK. The Department for Transport's Clean Maritime Plan provides a route map for action on infrastructure, economics, regulation, and innovation that covers high technology readiness level (TRL 3-7). There is a genuine opportunity to explore fundamental research and go beyond conventional marine engineering and naval architecture and exploit the UK's world-leading cross-sectoral fundamental research expertise on hydrodynamics, fuels, combustion, electric machines and power electronics, batteries and fuel cells, energy systems, digitization, management, finance, logistics, safety engineering, etc. The proposed UK-MaRes Hub is a multidisciplinary research consortium and will conduct interdisciplinary research focussed on delivering disruptive solutions which have tangible potential to transform existing practice and reach a zero-carbon future by 2050. The challenges faced by UK maritime activity and their solutions are generally common but when deployed locally, they are bespoke due to the specifics of the port, the vessels they support, and the dependencies on their supply chains. Implementation will be heavily dependent on the local community, existing infrastructure, as well as opportunities and constraints related to the supply, distribution, storage and bunkering of alternative fuels, in decarbonising port handling facilities and cold-ironing, with the integration of renewable energy, reducing air pollution, to land-use and increased capacity and capability, and the local development of skills. The types of vessels and the cargoes handled through UK ports varies and are related to several factors, such as geographical location, regional industrial and business activity and wider transport links. Therefore, UK-MaRes Hub aims to feed into a clean maritime strategy that can adapt to place-based challenges and provide targeted technical and socio-economic interventions through a novel Co-innovation Methodology. This will bring together Research Exploration themes/work packages and Responsive Research Fund project activity into focus on port-centric scenarios and assess possibilities to innovate and reduce greenhouse gas emissions by 2030, 2040 and 2050 timeframes, sharing best practice across the whole maritime ecosystem. A diverse, and inclusive Clean Maritime Network+ will ensure wider dissemination and knowledge take-up to achieve greater impact across UK ports and other maritime activity. The Network+ will have coordinated regional activity in South-West, Southern, London, Yorkshire & Lincolnshire, Midlands, North-West, North-East, Scotland, Wales, and Northern Ireland. An already established Clean Maritime Research Partnership has vibrant academic, industrial, and civic stakeholder members from across the UK. UK-MaRes Hub will establish a Clean Maritime Policy Unit to provide expert advice and quantitative evidence to enable rapid decarbonisation of the maritime sector. It will ensure that the UK-MaRes Hub is engaging with policymakers at all stages of the hub activities.