Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Over 95% of used nuclear fuel is uranium and plutonium, which can be recovered and reused. However, because used fuel is intensely radioactive, this requires very complex processes. These processes can also be adapted to the separation of high hazard materials from the residual radioactive wastes, to simplify radioactive waste management. However, industrial reprocessing of used fuel primarily relies on a 50 year old solvent extraction process (Purex), which was originally developed for much simpler fuels. As a result, modern fuels can prove difficult to reprocess. We will therefore explore two different approaches to nuclear fuel separation in parallel, one based on the established Purex technology and the other on a much more recent development, ion selective membranes (ISMs). ISMs are porous, chemically reactive membranes which can bind metals from solutions then release them again, depending on conditions, thus allowing highly selective separations.In the solvent extraction system, we will focus on a common problem in solvent extraction, third phase formation, and on separation of a group of long lived, high hazard waste isotopes (the fission product technetium and the minor actinides). With the ISMs, we will first prove their utility in uranium/plutonium separation, then extend these studies to the minor actinides. Throughout, we will work with the elements of interest, rather than analogues or low activity models and in realistic radiation environments. In both strands of the project, we will explore the underlying physical and chemical processes then, building on this understanding, we will develop a series of quantitative models, building from phase behaviour to unit operations and finally to process flowsheet models. We wil use the resulting models to explore different options for fuel reprocessing, based on scenarios defined with our industrial partners.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Our society is increasingly reliant upon engineered systems of unprecedented and growing complexity. As our manufacturing and service industries, and the products that they deliver, continue to complexify and interact, and we continue to extend and integrate our physical and digital infrastructure, we are becoming increasingly vulnerable to the cascading and escalating effects of failure in highly complex and evolving systems of systems. Consequently, it is becoming increasingly critical that we are able to understand and manage the risk and uncertainty in Complex Engineering Systems (CES) to provide reliant and optimal design and control solutions. Research on natural complex systems is helping us to understand the implications of inter-dependencies within and between complex adaptive systems. However, unlike natural ecosystems, which may become more robust through diversifying, man-made complex systems tend to become more fragile as their complexity increases. If we are to deal with the challenge presented by complex engineered systems, we will need to exploit and synthesise our current understanding of natural and engineered systems, our current theories of complexity more generally. The ENgineering COmplexity REsilience Network Plus (hereafter called ENCORE) addresses the Grand Challenge area of Risk and Resilience in CES. Our vision is to identify, develop and disseminate new methods to improve the resilience and sustainable long-term performance of complex engineered systems, initially including Cities and National Infrastructure, ICT and Energy Infrastructure, Complex Products: Aerospace (both Jet Engines and Space Launch and Recovery Systems) and later to explore the inclusion of Nuclear Submarines, Power Stations and Battlefield Systems. We have chosen these particular CES domains as they strike a balance between the challenges and opportunities that the UK faces for which complexity science can have a significant impact for our citizens and businesses whilst spanning sufficiently diverse fields to present cross-domain learning opportunities. Our approach is to create shared learning from [1] the manner in which naturally complex systems cope with risk and uncertainty to deliver resilience (ecosystems, climate, finance, physiology, etc.) and how such strategies can be adapted for engineering systems; [2] how the tools and concepts of complexity science can contribute towards developing a greater understanding of risk, uncertainty and resilience, and [3] distilling world-class activity within individual CES domains to provide new insights for the design and management of other engineering systems. Examples of the potential for the application of this field and which will be considered for inclusion in the feasibility studies include: - Predicting equipment failures and their consequences in critical infrastructure systems; - Developing a management heuristic that plays the same role as a "risk register", but addresses systemic resilience; - Optimising the deployment of instrumentation required to manage cities and other CES effectively; - Increasing the resilience of interdependent digital systems; - Advancing models of cascading failure on networks such that they take account of node heterogeneity and in particular the different failure/recovery modes of different types of node. - Improving the number of contexts in which CES can be deployed with replicable performance; - Decreasing the likelihood of human behavioural errors in operating CES. - Identifying the critical elements that constrain/define system performance most strongly; - Extending system lifetimes and functionality; - Mapping the relationship between complex system complexity and fragility; - Characterising uncertainty and defining the inference process to transition from one phase to the other in the control of CES and in complex decision making processes.