• 2013-2022close
• UKRI|EPSRC close
• 2015 close

Nuclear power has great potential as a future global power source with a small carbon footprint. To realise this potential, safety (and also the public perception of safety) is of the utmost importance, and both existing and new design nuclear power plants strive to improve safety, maintain availability and reduce the cost of operation and maintenance. Moreover, plant life extensions and power updates push the demand for the new tools for diagnosing and prognosing the health of nuclear power plants. Monitoring the status of plants by diverse means has become a norm. Current approaches for diagnosis and prognosis, which rely heavily on operator judgement on the basis of online monitoring of key variables, are not always reliable. This project will bring together three UK Universities and an Indian nuclear power plant to directly address the modelling, validation and verification changes in developing online monitoring tools for nuclear power plant. The project will use artificial intelligence tools, where mathematical algorithms that emulate biological intelligence are used to solve difficult modelling, decision making and classification problems. This will involve optimizing the number of inputs to the models, finding the minimum data requirement for accurate prediction of possible untoward events, and designing experiments to maximize the information content of the data. We will then use the optimised system to predict potential loss of coolant accidents and pinpoint their specific locations, after which we will progress to prediction of possible radioactive release for various accident scenarios, and, in order to facilitate emergency preparedness, the post release phase will be modelled to predict the dispersion pattern for the scenarios under consideration. Finally, all of the models will be validated, verified and integrated into a tool that can be used to monitor and act as an early warning device to prevent such scenarios from occurring.

This project will explore the potential for novel base drag reduction technologies for cars. Physical (reduced-scale wind tunnel) and numerical (Computational Fluid Dynamics) simulations will be used to understand the structure of the flow, identify opportunities for flow control, propose new approaches and determine their likely performance. This could include the use of passive jets, exploiting rear wheel wake interaction or manipulating base surface roughness. Investigating the role of flow unsteadiness in base drag generation is central to this work. Manipulating flow unsteadiness for base drag reduction is poorly understood, as are rear wheel wake interactions. The academic challenge arises from the complexity of vehicle base and wheel wakes; they are intrinsically unsteady, requiring the use of high-resolution time-resolved measurement and simulation techniques.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

While the theory of minimal and constant mean curvature (CMC) surfaces is a purely mathematical one, such surfaces overtly present themselves in nature and are studied in many material sciences. This makes the theory more exciting. If we take a closed wire and dip it in and out of soapy water, the soap film that forms across the loop is in fact a minimal surface and the physical properties of soap films were already studied by Plateau in the 1850s. The air pressure on the sides of soap films is equal and constant. However, if we change the pressure on one side, for instance by blowing air on it, the new surface that we obtain is what we call a soap bubble. A soap bubble is a CMC surface. More precisely, minimal and CMC surfaces are, respectively, mathematical idealisation of soap films and soap bubbles. The mean curvature of a soap film and bubble is a quantity that is proportional to the pressure difference on the sides of the film. The value of the pressure difference, and therefore of the mean curvature, is zero for a soap film/minimal surface and it is non-zero constant for a soap bubble/CMC surface. Since the pressure inside a small bubble is greater than the pressure inside a big one, the constant mean curvature of a small bubble is greater than the constant mean curvature of a big one. Minimal and CMC surfaces also enjoy crucial minimising properties relative to area. Among all surfaces spanning a given boundary, a soap film/minimal surface is one with locally least area; soap bubbles/CMC surfaces locally minimise area under a volume constraint. This project aims to investigate several key geometric properties of minimal and CMC surfaces. Roughly speaking, I intend to prove several results about CMC surfaces embedded in a flat three-dimensional manifold, including area estimates when the surfaces are compact with bounded genus and the ambient manifold is compact. I also plan to study the limits of a sequence of minimal or CMC surfaces embedded in a general three-dimensional manifold.

Synthetic Biology is the engineering of biology. In this spirit, this Fellowship aims at combining control engineering methodology and expertise with synthetic biology current know-how to solve important real-world problems of high industrial and societal importance. Anticipated high-impact applications of synthetic biology range from cell-based diagnostics and therapies for treating human diseases, to efficiently transforming feedstocks into fuels or biochemicals, to biosensing, bioremediation or production of advanced biomaterials. Central to tackling these problems is the development of in-cell automatic feedback control mechanisms ensuring robust functionality and performance of engineered cells that need to operate under uncertain and changing environments. The availability of methods for designing and implementing feedback control mechanisms that yield improved robustness, efficiency and performance is one of the key factors behind the tremendous advances in engineering fields such as transportation, industrial production and energy. As in these and other engineering disciplines, systems and control engineering will accelerate the development of high-impact synthetic biology applications of societal, commercial and industrial importance. In particular, through this Fellowship, I propose a comprehensive engineering approach to push forward the robustness frontier in synthetic biology towards reliable cell-based biotechnology and biomedicine. This ambitious goal requires: (1) the development of feedback mechanisms to reduce the footprint of engineered metabolic pathways on their cell "chassis", (2) the development of system-level feedback mechanisms to robustly and efficiently manage one or more synthetic devices in the context of whole-cell fitness, and (3) the development of synthetic cell-based systems designed to restore and maintain the extra-cellular concentration of some biomolecules within tight homeostatic bounds. These three aspects define three work packages in my Fellowship. Each work package on its own tackles important synthetic biology challenges for real-world applications, while their combination in WP4 aims towards robust cell-based biotechnology and biomedicine. The corresponding work packages are: *WP1*: Automatic management of fluxes for robust and efficient metabolic pathways (through genetic-metabolic feedback control) *WP2*: Automatic management of cellular burden for robust and efficient whole-cell behaviour (through host-circuit feedback control) *WP3*: Automatic management of extra-cellular concentrations for robust homeostatic regulation of environmental conditions (through cell-environment feedback control) *WP4*: System integration and combination of the feedback control mechanisms developed in WP 1-3 The first two work packages address device robustness to cellular context, while the third addresses robust adaptation to and control of changing environmental conditions. WP4 will use and further develop the systems and control engineering framework developed in WP 1-3 to explore the synergistic combination of the proposed feedback control mechanisms. By providing systematic engineering solutions that endow engineered biosystems with robust functionalities, we will enable the enhancement of existing biotechnological processes and the reliable development of industrial applications to improve health and quality of life. Through the above, this Fellowship will foster strong and long-lasting economic and societal impact in the UK and globally and promote knowledge-based UK leadership.

The NanoESCA is an Ultra High Vacuum (UHV) photoemission spectroscopy system with sub-micron spatial resolution for real-space and k-space (reciprocal space) mapping from areas of a few microns of flat material surfaces, and the capability to perform quantitative chemical state mapping at the nanoscale. The system is designed for installation on a synchrotron beam line end-station or in a Nano Science Laboratory. The system has various modes of operation. UV photon sources are used for Ultra-violet Photoemission Spectroscopy (UPS), Angle Resolved UPS, and Photo Emission Electron Microscopy (PEEM), soft X-rays are used for X-ray Photoemission Spectroscopy (XPS). UPS gives energy filtered information on weakly bound states and the valence band structure in real space and the surface electronic band structure in k-space. XPS is used to probe core level spectra to obtain quantitative information on the chemical composition of a surface. In contrast to the high resolution Scanning Electron Microscope, PEEM directly images surface areas emitting photoelectrons in real time without scanning. By energy filtering PEEM images it is possible to obtain quantitative maps of surface work function. The field of view is adjustable from millimetres to microns allowing high resolution imaging of features as small as 30nm (UPS modes) and 480nm (XPS mode). The NanoESCA machine being requested represents the next generation in imaging and spectroscopic PEEM. It uses an extension of the established parallel imaging technique, to simultaneously image and filter photoelectrons, by using a double hemispherical (aberration corrected) electron analyser in combination with high photon flux VUV and X-ray sources, to realise nano scale imaging and spectroscopy in real space and in k-space; the latter mode allows information on the electronic band structure of materials to be visualised and compared directly with theoretical models. Uniquely, this capability to obtain nanoscale spectroscopy using either X-ray or VUV sources is obtained by parallel imaging through the same PEEM 'column' which also acts as the entrance lens of the imaging spectrometer.

Measure the metal thickness of sheet metal whilst it is being rolled. Prevent the metal from being damaged or marked. Provide feedback to a control system to influence the metal rollers during the rolling process. The research involves using ultrasound to investigate the strip thickness of sheet metal whilst it is being rolled. Other parameters will also be investigated to see what can be observed on a pilot rolling mill as well as a full sized industrial rolling mill. The impact this research will have is to the metal rolling industry as being able to control the rolling process during rolling allows for time to be saved and other value to be added. To this end the research is sponsored by Tata steel. The ultrasound method being used is pitch-catch oblique ultrasound and the pulses are sent via a Picoscope. Previous measuring methods have used radiation or have relied on physical contact, however these methods have limitations that the ultrasound method is believe to mitigate. This project aligns to the manufacturing goal of the EPSRC.

Cloud computing has significantly changed the IT landscape. Today it is possible for small companies or even single individuals to access virtually unlimited resources in large data centres (DCs) for running computationally demanding tasks. This has triggered the rise of "big data" applications, which operate on large amounts of data. These include traditional batch-oriented applications, such as data mining, data indexing, log collection and analysis, and scientific applications, as well as real-time stream processing, web search and advertising. To support big data applications, parallel processing systems, such as MapReduce, adopt a partition/aggregate model: a large input data set is distributed over many servers, and each server processes a share of the data. Locally generated intermediate results must then be aggregated to obtain the final result. An open challenge of the partition/aggregate model is that it results in high contention for network resources in DCs when a large amount of data traffic is exchanged between servers. Facebook reports that, for 26% of processing tasks, network transfers are responsible for more than 50% of the execution time. This is consistent with other studies, showing that the network is often the bottleneck in big data applications. Improving the performance of such network-bound applications in DCs has attracted much interest from the research community. A class of solutions focuses on reducing bandwidth usage by employing overlay networks to distribute data and to perform partial aggregation. However, this requires applications to reverse-engineer the physical network topology to optimise the layout of overlay networks. Even with perfect knowledge of the physical topology, there are still fundamental inefficiencies: e.g. any logical topology with a server fan-out higher than one cannot be mapped optimally to the physical network if servers have only a single network interface. Other proposals increase network bandwidth through more complex topologies or higher-capacity networks. New topologies and network over-provisioning, however, increase the DC operational and capital expenditures-up to 5 times according to some estimates-which directly impacts tenant costs. For example, Amazon AWS recently introduced Cluster Compute instances with full-bisection 10 Gbps bandwidth, with an hourly cost of 16 times the default. In contrast, we argue that the problem can be solved more effectively by providing DC tenants with efficient, easy and safe control of network operations. Instead of over-provisioning, we focus on optimising network traffic by exploiting application-specific knowledge. We term this approach "network-as-a-service" (NaaS) because it allows tenants to customise the service that they receive from the network. NaaS-enabled tenants can deploy custom routing protocols, including multicast services or anycast/incast protocols, as well as more sophisticated mechanisms, such as content-based routing and content-centric networking. By modifying the content of packets on-path, they can efficiently implement advanced, application-specific network services, such as in-network data aggregation and smart caching. Parallel processing systems such as MapReduce would greatly benefit because data can be aggregated on-path, thus reducing execution times. Key-value stores (e.g. memcached) can improve their performance by caching popular keys within the network, which decreases latency and bandwidth usage compared to end-host-only deployments. The NaaS model has the potential to revolutionise current cloud computing offerings by increasing the performance of tenants' applications -through efficient in-network processing- while reducing development complexity. It aims to combine distributed computation and network communication in a single, coherent abstraction, providing a significant step towards the vision of "the DC is the computer".

The overall goal is to synthesise, characterise and test Metal- Organic Frameworks (MOF) for Gas separations of interest in the petrochemicals industry. Specifically, the work will initially focus on producing several members of a family that have been shown to exhibit good potential in certain gas separations of interest. MOFs containlng single- or mixed metals will be investigated in order to try to fine tune this MOF for specific gas separations. Subsequently, other MOF systems wlll be explored based on the results of modeling studies. Overall, the materials will be tested through dynamlc and equilibrium adsorption tests to estimate their performance. This project relies on: (i) developing materials relevant to the energy sector and (ii) studying their surface interactions with gas molecules. lt is thus directly llnked to the EPSRC research areas of Materials Engineerlng, Surface Science and Energy Efficiency.

The increasing global population places an ever-increasing pressure on the agricultural industry to meet the demands of food supply in a sustainable manner. This PhD CASE award in collaboration with Syngenta, a world-leading agri-business provides a great opportunity for a graduate from the physical sciences who wishes to help feed the world. Syngenta develop agrochemical products that are applied to plant materials in order to protect crop from pests, increasing yield with minimal environmental impact. This project aims to apply a cutting-edge laser imaging technique (stimulated Raman scattering) as a novel analytical technique that will allow in-situ analysis of agrochemicals in living plant tissues at the cellular level. The ability to visualise agrochemical products on a leaf surface to reveal interactions between the materials of the product and with the leaf surface will enable a step change in agrochemical design, through determination of the spatial distribution of the materials and their roles within the applied products. The technology developed in this project will ultimately lead to the development of future agrochemical products that are tailored to maximise efficacy and minimise environmental impact. This fully funded EPSRC industry CASE award is especially well suited to graduates from a chemistry or physics background, who will benefit from training and experience in industrial research and development.