This proposal sets out the terms for the continuation funding for the IMRC at Imperial College. All objectives, research plans and beneficiaries information has previously been approved though the 3rd year review of the existing Centre.

Materials characterisation is critical to the understanding of key processes in a range of functional and structural materials that have applications across several industrial sectors. These sectors include strategic priorities such as discovery of functional materials, energy storage and conversion and materials manufacturing, and healthcare. Materials characterisation is increasing in complexity, driven by a need to understand how materials properties evolve in operando, over their full lifetimes and over all levels of their hierarchy to predict their ultimate performance. The new generation of materials characterisation techniques will require: 1. Greater spatial and chemical resolution; 2. Correlated information that bridges nano- and centimeter -length scales, to relate the nanoscale chemistry and structure of interest to their intrinsically multi-scale surroundings, and 3. Temporal information about the kinetics of materials behaviour in extreme environments. The CDT will train students in a range of complementary techniques, ensuring that they have the breadth and depth of knowledge to make informed choices when considering key characterisation challenges. Our CDT will use an integrated training approach, to ensure that the technical content is well aligned with the research objectives of each student. This training in specific research needs will be informed by our industry partners and will reflect the suite of research projects that the students will undertake. Our portfolio of research projects will provide an innovative and ambitious research and training experience that will enhance the UK's long-term capabilities across high value industrial sectors. Additionally, our students will receive training in a range of topics that will support their research progress including in science communication, research ethics, career development planning and data science. These additional courses will be distributed throughout the 4-year PhD programme and will ensure that a cohesive training plan is in place for each student, supported by cohort mentors. Each student graduating from the CDT-ACM will leave will a through understanding of the key challenges presented by materials characterisation problems, and have the tools to provide creative solutions to these. They will have first hand experience of collaborating with industry partners and will be well placed to address the strategic needs of the UK Industrial Strategy. Our training will be developed in collaboration with leading partner organisations, and include international collaboration with the AMBER centre, a Science Foundation Ireland centre, as well as national facilities such as Diamond Light Source. Innovative on-line and remote instrument access will be developed that will enable both UK and Irish cohorts to interact seamlessly. Industry partners will be closely involved in designing and delivering training activities including at summer schools, and will include entrepreneurship activities. Overall the 70 students that will be trained over the lifetime of the CDT will receive excellent tuition and research training at two world leading institutions with unique characterisation abilities.

The nuclear industry has some of the most extreme environments in the world, with radiation levels and other hazards frequently restricting human access to facilities. Even when human entry is possible, the risks can be significant and very low levels of productivity. To date, robotic systems have had limited impact on the nuclear industry, but it is clear that they offer considerable opportunities for improved productivity and significantly reduced human risk. The nuclear industry has a vast array of highly complex and diverse challenges that span the entire industry: decommissioning and waste management, Plant Life Extension (PLEX), Nuclear New Build (NNB), small modular reactors (SMRs) and fusion. Whilst the challenges across the nuclear industry are varied, they share many similarities that relate to the extreme conditions that are present. Vitally these similarities also translate across into other environments, such as space, oil and gas and mining, all of which, for example, have challenges associated with radiation (high energy cosmic rays in space and the presence of naturally occurring radioactive materials (NORM) in mining and oil and gas). Major hazards associated with the nuclear industry include radiation; storage media (for example water, air, vacuum); lack of utilities (such as lighting, power or communications); restricted access; unstructured environments. These hazards mean that some challenges are currently intractable in the absence of solutions that will rely on future capabilities in Robotics and Artificial Intelligence (RAI). Reliable robotic systems are not just essential for future operations in the nuclear industry, but they also offer the potential to transform the industry globally. In decommissioning, robots will be required to characterise facilities (e.g. map dose rates, generate topographical maps and identify materials), inspect vessels and infrastructure, move, manipulate, cut, sort and segregate waste and assist operations staff. To support the life extension of existing nuclear power plants, robotic systems will be required to inspect and assess the integrity and condition of equipment and facilities and might even be used to implement urgent repairs in hard to reach areas of the plant. Similar systems will be required in NNB, fusion reactors and SMRs. Furthermore, it is essential that past mistakes in the design of nuclear facilities, which makes the deployment of robotic systems highly challenging, do not perpetuate into future builds. Even newly constructed facilities such as CERN, which now has many areas that are inaccessible to humans because of high radioactive dose rates, has been designed for human, rather than robotic intervention. Another major challenge that RAIN will grapple with is the use of digital technologies within the nuclear sector. Virtual and Augmented Reality, AI and machine learning have arrived but the nuclear sector is poorly positioned to understand and use these rapidly emerging technologies. RAIN will deliver the necessary step changes in fundamental robotics science and establish the pathways to impact that will enable the creation of a research and innovation ecosystem with the capability to lead the world in nuclear robotics. While our centre of gravity is around nuclear we have a keen focus on applications and exploitation in a much wider range of challenging environments.

Understanding the mechanisms that lead to the breakup and evaporation of liquids is a key step towards the design of efficient and clean combustion systems. The complexity of the processes involved in the atomisation of Diesel fuels is such that many facets involved are still not understood. The morphological composition of a typical Diesel spray includes structures such as ligaments, amorphous and spherical droplets, but the quantity of fuel occupied by perfectly spherical droplets can represent a small proportion of the total injected volume. These relatively large non-spherical structures have never been thoroughly investigated and documented in high-pressure sprays, even though the increase in heat transfer surface area of deformed droplets is an influential factor for predicting the correct trend of evaporating Diesel sprays. The characterisation of fuel spray droplets is generally conducted using laser diagnostics that can measure droplet diameters with a high level of accuracy, but they are fundamentally unable to measure the size or shape of non-spherical droplets and ligaments. Hence the data obtained through these diagnostic techniques provide a partial and biased characterisation of the spray. The experimental bias towards spherical droplets is compounded by the complexity of modelling the heating and evaporation of deformed droplets. Consequently, theoretical models for liquid fuel atomisation and vaporisation are based on a number of simplifying hypotheses including the assumption of dispersed spherical droplets. Our proposal seeks to initiate a step change in the description of petroleum and bio fuel spray formation by developing diagnostics and numerical models specifically focused on non-spherical droplets and ligaments. Our approach will build upon recent advances with microscopic imaging to build novel diagnostics and algorithms that can measure the shape, size, velocity and gaseous surrounding of individual droplets and ligaments. This morphological classification, along with the velocity measurements, will be used to develop new phenomenological and numerical models for spray breakup, heating and evaporation. The models will then be implemented into computational fluid dynamics (CFD) codes to simulate spray mixing under modern engine conditions, and generate information where optical diagnostics cannot be applied. These goals will be achieved by combining the expertise of the academic and industrial partners with that of international experts from the University of Bergamo, CORIA, and Moscow State University. The project's concerted approach, aimed at removing the experimental and numerical biases towards spherical droplets, will establish a unique world leading research capability with potential impact for numerous practical spray applications. The project would underpin research in areas that rely upon the atomisation or evaporation of liquids, including the efficient delivery of liquid fuel, pharmaceutical drugs, cryogens, lubricants and selective catalytic reductants.

If imaging required less data, it would enable faster throughput, improved performance in restricted access situations and simpler, cheaper hardware. The information from images enables damage to be accurately quantified within engineering components, avoiding the need to choose between excessive conservatism and unpredicted failures. To enable improved reconstructions from limited data sets, a diverse set of approaches have been identified, incorporating knowledge of physical wave interaction with objects, use of external information, image processing and other techniques. The fellowship will address the broad problem by applying these approaches to several example applications which are of great interest to industry, and will ultimately enable the development of the field of limited data imaging. While primarily focused on NDE (non-destructive evaluation), the applications of this spread to areas including medicine, geophysics and security.