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.

Fluid dynamics underpins large areas of engineering, environmental and scientific research, and is becoming increasingly important in medical science. At Leeds, we possess research expertise across each of these domains and we have an established record of working across disciplinary boundaries. This proposal builds upon this record through the establishment of a multidisciplinary CDT in Fluid Dynamics. Research techniques that will be applied, and developed, will encompass: mathematical modelling & theory; numerical methods, CFD & high performance computing (HPC); and measurement & experimentation. Engineering application areas to be addressed include: reacting flows; carbon capture, transport & storage; flow of polymer melts; mixing problems; particulate flows; coating & deposition; lubrication; medical devices; pathogen control; heat transport; wind turbines; fluid-structure interaction; and nuclear safety. Environmental application areas will consist of: groundwater flow; river/estuary flows; tidal flows; oceanography; atmospheric pollution; weather forecasting; climate modelling; dynamics of the Earth's interior; and solar & planetary flow problems. Facilities available to undertake this research include: the University's HPC system which, combined with the N8 regional facility that is hosted at Leeds, provides ~10000 computational cores, an extensive suite of licensed software and dedicated support staff; flow measurement techniques (including Particle Imaging Velocimetry (PIV), 2-component Laser Doppler Anemometry (LDA), Phase Doppler Anemometry (PDA) and Ultrasonic Doppler Velocity Profiling (UDVP)); techniques for measuring fluid concentration (Ultrasonic High Concentration Meter (UHCM) and Optical Backscatter Probes (OBS)) and a range of optical metrology systems (e.g. pulsed and continuous wave lasers). The UK has a substantial requirement for doctoral scientists and engineers who have a deep understanding of all aspects of fluid dynamics from theory through to experimental methods and numerical simulation. In manufacturing and process engineering, for example, many processes depend critically on fluid flows (e.g. extrusion of polymer melts, deposition of coatings, spray drying, etc.) and it is essential to understand and control these processes in order to optimize production efficiency and reliability (see letter of support from P&G for example). In large-scale mechanical engineering there is a demand for expertise in reacting turbulent flows in order to optimize fuel efficiency and engine performance, and in wetting and surface flows for the design and manufacture of pumps and filters. There is also a need for a wide variety of skilled experts in environmental fluid flows to support the growing need to understand and predict local pollution and threats to safety (atmospheric, surface water, ocean and sub-surface flows), and to predict weather, climate and space weather for satellite technology. We will train a new generation of researchers who will have a broad range of skills to transfer into industry and environmental agencies, hence our approach will be multi-disciplinary throughout. All students will undertake both modelling and experimental training before embarking on their PhD project - which will be co-supervised by academics from different Schools. The MSc component of the programmee will be specifically tailored to develop expertise in the mathematical background of fluid dynamics, in CFD/HPC, and in experimental techniques. Team-based projects will be used to develop the teamwork and communication skills we believe are essential. Finally, engagement with industry will be a key feature of this CDT: all students will undertake an industrial placement, a large number of projects will be industrially sponsored, and our non-academic partners will contribute actively to our management, implementation and strategic development.