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The Open University
Country: United Kingdom
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843 Projects, page 1 of 169
  • Funder: UKRI Project Code: EP/M018466/1
    Funder Contribution: 30,890 GBP
    Partners: OU

    Background: Advanced engineering components for modern sustainable society require not only better materials but also new joining or welding processes. A critical topic for the advent of the next generation of nuclear reactors is the development of improved methods for joining dissimilar alloys. Due to the high temperatures inherent in fusion welding processes, the use of these methods for joining certain dissimilar alloys, e.g. Titanium and Stainless Steels, has proved unsuccessful. Gallium-assisted diffusion bonding is an award-winning new method, invented by the proposer (PI), for joining the advanced alloys and composites that cannot be welded using conventional processes. Previous results: The outcome of research carried out under the EPSRC's Indo-UK Civil Nuclear Programme research project "JOINT" (Grant EP/I01215X/1) has proved very promising. High strength joints between Titanium and Stainless Steels were produced using gallium-assisted diffusion bonding as well as active brazing processes leading to publication of several joint papers by the UK and India partners. Proposed research: The quality of Titanium to Stainless Steel joints made using Gallium-assisted diffusion bonding processes will be studied where the process conditions are systematically varied. This will allow the bonding conditions to be optimised for strength and integrity. The bonded samples will be subjected to careful microstructural characterisation in India and larger samples made for mechanical testing. Results from the research programme will be published in peer-reviewed journals.

  • Funder: UKRI Project Code: 2739878
    Partners: OU

    Due to the extreme conditions seen in a fusion reactor, material performance is a critical factor in the future production of fusion power plants. Structural materials must survive high operating temperatures and severe irradiation damage for long periods of time, and so creep is a critical failure mechanism that requires rapid development in fundamental understanding. Materials for fusion are low technology readiness level, novel and only available in small volumes. Therefore, there is a need to extract as much information from a given amount of material as possible. The aim of this project is to work towards doing just that for material behaviour in the creep regime, primarily using Grade 91 steel as a model material for EUROFER 97. This work will utilise the inherent scalability of the image processing technique Digital Image Correlation (DIC) to quantify creep deformation at multiple length scales concurrently on the same sample, using a combination of visible light and electron imaging using a scanning electron microscope (SEM). The macroscale DIC will facilitate the sample to be designed to experience a spatial stress gradient, reducing the amount of material required to characterise creep behaviour when compared to uniaxial testing. The microstructural scale DIC will be used to observe grain scale phenomena across the range of stress states experienced by the sample. This combination of test scales will enable macroscale behaviour to be explained by grain-scale behaviour, developing a more nuanced insight into the material's creep mechanisms and failure modes. This is achievable due to the unique DIC-monitored creep testing facilities at the Open University and the existing expertise at Open University and UKAEA in microstructural-scale DIC.

  • Funder: UKRI Project Code: NE/P019331/1
    Funder Contribution: 108,417 GBP
    Partners: OU

    The Earth's climate is currently changing rapidly, primarily due to emissions of greenhouse gases caused by human industrialisation. These emissions are projected to increase through this century, and under some scenarios atmospheric carbon dioxide (CO2) concentrations could reach more than 1000 parts per million (ppm) by the year 2100, compared with 280 ppm prior to industrialisation. In order to predict the sociological, environmental, and economic impacts of such scenarios, and thus to better prepare for them, the only tool at our disposal is climate modelling. In order to assess our confidence in predictions from climate models, they are routinely tested under conditions of known climate. However, this testing (and associated tuning of the models) is almost exclusively carried out under modern climate conditions, and relative to recently observed climate change, for which CO2 concentrations are less than 400 ppmv. As such, our state-of-the-art climate models have never been tested under the high CO2, super-warm climate conditions to which they are primarily applied, and upon which major policy decisions are made. However, there exist time periods in Earth's deeper past (for example the Eocene, about 50 million years ago) when CO2 concentrations were similar to those expected by the end of this century; but climatological information from these time periods is currently sparse and is associated with large uncertainties, and the exact concentrations of CO2 are only poorly known. Recent changes in our understanding of how the geological record preserves climate signals, and developments in laboratory techniques, mean that for the first time there exists a new and exciting opportunity to remedy this situation and provide a much-needed evaluation of our very latest climate models in a super-warm world. In SWEET, we will apply these emerging techniques, and develop new methodologies and tools, to produce a global dataset of Eocene temperatures. Coupled with new and high-fidelity reconstructions of Eocene CO2 concentrations, and state-of-the-art maps of the 'palaeogeograpy' (continental positions, mountain ranges, ocean depths etc.), we will use this dataset to test a state-of-the art climate model under high atmospheric CO2, Eocene conditions. The model, UKESM, is identical to that being used by the UK Met Office in the international 'CMIP6' project, which itself will be the primary input to the next Intergovernmental Panel on Climate Change (IPCC) assessment report. We will also use our data and additional model simulations (running at high spatial resolution) to investigate the relative importance of the various mechanisms which determine the response of the Earth system to high CO2 and to changes in palaeogeography. A characteristic of SWEET is that we will take full account of uncertainties in the geological data and the modelling, and our model-data comparisons will be underpinned by a statistical framework which incorporates these uncertainties. We will also adopt a 'multi-proxy' approach by using several independent geological archives to reconstruct temperature. For one of these archives, namely the oxygen isotopic composition of the fossilised shells of microscopic marine creatures from the Eocene, we will apply a particularly innovative approach which will enable us to 'resurrect' previously discredited data, by using an extremely fine-scale 'ion probe' to investigate how these isotopic signatures of past climate change are recorded in individual fossils. SWEET has strong links to UK Met Office, and to the international DeepMIP project, which is part of the 'Palaeoclimate Modelling Intercomparison Project', itself part of CMIP6. We expect our results to feed into the next IPCC assessment reports and therefore to ultimately inform policy.

  • Funder: UKRI Project Code: MR/L009005/1
    Funder Contribution: 252,947 GBP
    Partners: OU

    The self-controlled case series (SCCS) method is a statistical technique to quantify the association between an exposure, such as a drug, and an outcome, such as an adverse event that might or might not be related to the exposure. The method is an alternative to standard techniques such as cohort studies and case-control studies which are commonly used to quantify such associations. The SCCS method can be attractive in certain circumstances because, unlike standard techniques, it is not subject to bias due to confounding by variables that are fixed in time for the duration of the study (for example, genetic factors, socio-economic context, or underlying health condition). Confounding can occur when a variable is associated with both the exposure and the outcome, and distorts the apparent association of interest. The method is particularly attractive for use with data from large databases that may have been assembled for reasons unrelated to the study, as is the case with administrative databases, for example. In such databases, information on important confounders, such as whether a person smokes or not, or their socio-economic background, or general state of health, may not be available. This means that the effect of these variables can't be allowed for in standard methods - whereas in the SCCS method, this is achieved automatically. The SCCS method has gained in popularity over the past decade, and is now often used in the area of medicine that deals with the effects of pharmaceutical drugs in populations, known as pharmacoepidemiology. However, the advantages of the SCCS method come at a price in the form of strong assumptions, that may or may not be valid in any particular setting. Over the past years, extensions of the SCCS method have been developed to cater for situations in which some of these assumptions are violated - while still retaining the essential feature of controlling automatically for fixed confounders. However, most of these extensions are a lot more complicated to apply than the basic SCCS method, and are not available in standard commercial or academic software packages. This greatly limits the use of these extensions of the SCCS method by researchers whose primary expertise is not statistics. The aim of the project is to provide programs and documentation within such software packages (notably R, STATA and SAS) to enable researchers to make use of these techniques more readily. We aim to provide comprehensive online resources including software programs, examples of their application including suitable data, examples of what might go wrong when assumptions are not met, together with documentation to describe how to run the programs, and information on how others have used the method. All materials and programs will be provided free of charge. We will also develop new methodology, as required, to plug gaps in the methods available, and to help users make informed choices about the SCCS models they should use. We expect the results to be of direct use to medical researchers and applied statisticians working in pharmacoepidemiology, and in epidemiology more widely. Our experience with the resources we have so far made available for the basic SCCS method shows that they are well used. The major impact we expect from this work is to improve the quality and quantity of studies undertaken with the SCCS method, and hence to contribute to providing better evidence underpinning medical decisions.

  • Funder: EC Project Code: 207292
    Partners: OU