The conditions in which materials are required to operate are becoming ever more challenging. Operating temperatures and pressures are increasing in all areas of manufacture, energy generation, transport and environmental clean-up. Often the high temperatures are combined with severe chemical environments and exposure to high energy and, in the nuclear industry, to ionising radiation. The production and processing of next-generation materials capable of operating in these conditions will be non-trivial, especially at the scale required in many of these applications. In some cases, totally new compositions, processing and joining strategies will have to be developed. The need for long-term reliability in many components means that defects introduced during processing will need to be kept to an absolute minimum or defect-tolerant systems developed, e.g. via fibre reinforcement. Modelling techniques that link different length and time scales to define the materials chemistry, microstructure and processing strategy are key to speeding up the development of these next-generation materials. Further, they will not function in isolation but as part of a system. It is the behaviour of the latter that is crucial, so that interactions between different materials, the joining processes, the behaviour of the different parts under extreme conditions and how they can be made to work together, must be understood. Our vision is to develop the required understanding of how the processing, microstructures and properties of materials systems operating in extreme environments interact to the point where materials with the required performance can be designed and then manufactured. Aligned with the Materials Genome Initiative in the USA, we will integrate hierarchical and predictive modelling capability in fields where experiments are extremely difficult and expensive. The team have significant experience of working in this area. Composites based on 'exotic' materials such as zirconium diborides and silicon carbide have been developed for use as leading edges for hypersonic vehicles over a 3 year, DSTL funded collaboration between the 3 universities associated with this proposal. World-leading achievements include densifying them in <10 mins using a relatively new technique known as spark plasma sintering (SPS); measuring their thermal and mechanical properties at up to 2000oC; assessing their oxidation performance at extremely high heat fluxes and producing fibre-reinforced systems that can withstand exceptionally high heating rates, e.g. 1000oC s-1, and temperatures of nearly 3000oC for several minutes. The research planned for this Programme Grant is designed to both spin off this knowledge into materials processing for nuclear fusion and fission, aerospace and other applications where radiation, oxidation and erosion resistance at very high temperatures are essential and to gain a deep understanding of the processing-microstructure-property relations of these materials and how they interact with each other by undertaking one of the most thorough assessments ever, allowing new and revolutionary compositions, microstructures and composite systems to be designed, manufactured and tested. A wide range of potential crystal chemistries will be considered to enable identification of operational mechanisms across a range of materials systems and to achieve paradigm changing developments. The Programme Grant would enable us to put in place the expertise required to produce a chain of knowledge from prediction and synthesis through to processing, characterisation and application that will enable the UK to be world leading in materials for harsh environments.

Over the next 25 years, society will face major challenges in health, transportation, energy and climate that will demand novel engineering solutions. Recent rapid advances in device and materials fabrication offer an important opportunity to help meet these challenges by enabling new technologies to be engineered down to the nanometre scale. Devices that manipulate fluids at the smallest scales exhibit complex and sometimes counter-intuitive phenomena that present novel scientific and technological opportunities. The scientific opportunity is to understand and model how the microscopic physics at and around phase interfaces drives the overall flow behaviour. The technological opportunity is to exploit this behaviour to design and manufacture devices with unprecedented capabilities. This research Programme is about uncovering the engineering science of flows that are intrinsically multiscale, and encapsulating this in efficient modelling software in order to enable the design of next generation technologies. This Programme aims to underpin future UK innovation in nano-structured and smart interfaces by delivering a simulation-for-design capability for nano-engineered flow technologies, as well as a better understanding of the critical interfacial fluid dynamics. We will produce software that a) resolves interfaces down to the molecular scale, and b) spans the scales relevant to the engineering application. As accurate molecular/particle methods are computationally unfeasible at engineering scales, and efficient but conventional fluids models do not capture the important molecular physics, this is a formidable multiscale problem in both time and space. Our software will have embedded intelligence that decides dynamically on the correct simulation tools needed at each interface location, for every phase combination, and matches these tools to appropriate computational platforms for maximum efficiency. The outcome will be a revolutionary new framework for simulating multiscale multiphysics systems in nature as well as engineering, greatly surpassing current modelling capabilities. The step-change advances this represents include: - predictive simulations of engineering-scale systems with nanoscale fidelity; - new insight into the physics of interfacial flow systems; - computational resources allocated in-simulation to enable more rapid system analysis; - assessment of proposed flow system designs that were not previously amenable to investigation; - accessing trans-disciplinary applications in granular flows and avalanche dynamics, and social/economic systems including urban traffic modelling and financial market stability. This work is strongly supported by 9 external partners, ranging from large multinational companies to an SME. The targeted applications all depend on the behaviour of interfaces that divide phases, and include: radical cancer treatments that exploit nano-bubble cavitation; the cooling of high-power electronics through evaporative nano-menisci; nanowire membranes for separating oil and water, e.g. for oil spills; and smart nano-structured surfaces for drag reduction and anti-fouling, with applications to low-emissions aerospace, automotive and marine transport. These applications make demands on simulation for engineering design that far outstrip current capabilities. Our partners will therefore be 'early-adopters' of this Programme's outcomes in order to meet the technical capabilities they will need to provide in the future. This interdisciplinary research draws on techniques and results across the boundaries of applied mathematics, physics, mechanical engineering, and computing. Its timeliness lies in the convergence of a uniquely-qualified academic team with a group of engaged and committed industrial partners, who will work together to exploit current and emerging nano-engineered flow systems for societal and economic benefit to the UK and elsewhere.

Laser powder bed fusion (LPBF) additive manufacturing (AM) transforms digital designs into functional products by joining materials together, layer upon layer. It offers flexible, sustainable manufacturability and short product development time to produce high-value components with complex geometries for business across the globe, including aerospace, automotive, and biomedical sectors. The global market for AM is expected to grow from $6b (2016) to $26b (2022), resulting in major initiatives launched across the globe to grow AM technologies, including "UK Industrial strategy", "Fraunhofer Additive Manufacturing Alliance", "Made in China 2025", and "America Makes". Despite the key advantages of AM, industries are facing technical challenges to use AM technology for safety-critical products, e.g. propellers and turbine blades, etc. These products may exhibit poor mechanical performance due to the presence of processing defects. To produce high-performance AM products, the stakeholders must understand the process and defect dynamics during AM, however, they are difficult to characterise due to the fast, complex laser-matter and multi-phase (solid-liquid-gas-plasma) interactions which occur in milliseconds. This project involves UCL and world-leading industrial partners in AM (Renishaw plc.), laser technologies (STFC - Central laser facility), machine learning (STFC - Scientific Machine-learning group), ultra-fast imaging (European Synchrotron Radiation Facility) and process simulations (European Space Agency) to co-develop engineering solutions to understand, evaluate, and control the process-structure-property-performance relationships in AM. This project is expected to collect a wide range of digital data that can be used to develop a data-driven, reliable and efficient AM process. Firstly, a unique chemical imaging tool will be developed and deployed to monitor and evaluate the metal vapourisation process during LPBF with a temporal resolution of 200 kHz. These results will be cross-validated by flagship ultra-fast X-ray imaging experiments which enable users to see inside the melt pool and defect dynamics during LPBF at micron resolution and a time resolution of up to 1 MHz. Correlative chemical and X-ray imaging of AM will be a game-changer characterisation technique to study the dynamic behaviour and multiphase interaction in AM. It will bring new understanding by which defects are introduced during AM and suggest ways to improve the overall process. Secondly, we will make advancement of novel beam shaping technologies to control the heat input to the fusion process, minimising metal vapourisation and defect formation during LPBF. The performance of the beam-shaping technologies will be assessed and verified by correlative imaging. Thirdly, all the digital data collected through this project will be used to build, train and deploy machine learning (ML) model(s) for process control, i.e. ML-guided process control. They will also be used to verify, validate, and advance an open-source high fidelity process simulation model that analyses multi-phase and multi-physics interactions in AM, which can be extended to other advanced manufacturing processes. Besides the development of new technologies, this project will also provide opportunities for early-career researchers to disseminate their research to the public, industries, and scientific communities, promote knowledge exchange and technology transfer activities.

The aim of this research is to fabricate microwave radiating antennas and substrates using nanomaterials. These novel dielectric substrates will facilitate electromagnetic advantages.Antennas are becoming increasingly prevalent in our modern, wireless and digital society; they are crucial for voice and data communication, GPS information and the provision of wireless communication between components of larger integrated systems. Antennas are subject to constant market forces which demand that products and their antennas become cheaper and smaller with improved functionality. With multiple antennas with multiband and MIMO capabilities whilst in very close proximity, for example on a mobile phone, the isolation between the different antennas also requires technological advances for improvement. The establishment of a novel technique to create antennas with improved radiation efficiency would reduce energy consumption.Nanoparticles are typically smaller than one millionth of a metre in at least one dimension and can be combined to form nanomaterials. Yet because the size of nanoparticles is so small and their resultant surface area-to-volume ratio so extremely large, nanomaterials possess a range of very useful and exciting properties. These include proportionately increased electrical conductivity, strength, heat and scratch resistance. Note, we will not be using nano-powders so the health risks will be minimal - and we will take all necessary steps to further minimise them.The use of nanomaterials will fundamentally allow increased versatility and improve functionality by design innovations. This area of research is highly novel as the use of nanomaterials as proposed here has not previously been reported at the application-rich microwave frequencies (wavelength ~ 30cm >> 1 micron). Using such nanomaterials for microwave antennas would allow manufacturing benefits as the antenna, the substrate and RF circuitry can be constructed together and integrated into one process. Currently, antennas designs are limited to certain specific fixed substrate properties. By constructing the substrate from non-metallic nanomaterials, advantageous, novel and heterogeneous substrates, with low losses and desirable electric and magnetic properties, can be produced, which can then be tailored for specific applications. Creating antennas from nanomaterials enables highly conductive and thinner than conventional layers.Intensive simulations using high performance computers will enhance Loughborough University's (LU) recent pilot study of how these novel antennas can behave. When these preparatory stages have been completed, prototype samples and antennas will be fabricated. Initially, geometrically simple antenna designs such as dipoles and patches will be used, enabling extrapolation to more complex antenna geometries later in the project. Once these are created their characteristics will be measured using LU's anechoic chamber, and compared with the simulation results.LU is ideally placed to research this exciting new area. The Communications Group has extensive expertise of simulating, design and measuring antennas and metamaterials. We have assembled an extremely strong multi-disciplinary team which has over 700 journal publications and more than 100 patents and book chapters. The Centre for Renewable Energy Systems Technology (CREST) has the capabilities to produce and characterise our specially made nanostructures. We also have close contacts with Patras University in Greece, which can fabricate nanostructures by an alternative (but viable) method using polymers.

Arctic sea ice area has been mapped for nearly four decades using the long-term data record provided by successive passive microwave satellite missions; showing an accelerated pace of ice loss since 1979. Less is known about how much the ice has also thinned, in part because of the lack of a similarly long-term and consistent data record on sea ice thickness. Radar altimeters, such as the one flown on the European Space Agency (ESA)'s CryoSat-2 (CS2) since April 2010, and the SARAL/AltiKa satellite, launched in February 2013 as part of a joint mission by the Centre National d'Etudes Spatiales (CNES) and the Indian Space Research Organization (ISRO), are now providing pan-Arctic (or up to 81.5N for AltiKa) thickness observations. However, one key uncertainty in using these data is how far the radar actually penetrates into the overlying snow cover. The general assumption has been that the radar return is from the snow-ice interface at Ku-band (CS2) frequencies, and from the snow-air interface at Ka-band (AltiKa) frequencies. Using this information together with assumptions on the depth of the overlying snow pack and its density, scientists can then convert the radar returns into total ice thickness assuming hydrostatic equilibrium. However, field evidence has put this general assumption into question, even for a homogeneous snowpack. A further complication is the lack of knowledge on how deep the snow pack is and its density. Typically, snow depth and density information based on a climatology constructed over thick multiyear ice in the 1980s have been used. However, as the total area in the sea ice cover has declined, there is now a larger proportion of first-year sea ice in the Arctic Basin. Snow over first-year ice tends to be more saline than over multiyear ice, and as such it has the potential for a significant impact on the radar returns. In addition, autumn and winter freeze-up has been delayed by several weeks to months in certain regions of the Arctic, shortening the duration for accumulation of snow. Given these current uncertainties, it is difficult to accurately assess how sea ice thickness is changing from year to year and over the long-term. Because sea ice is an important indicator of climate change, plays a fundamental role in the Arctic energy and freshwater balance, and is a key component of the marine ecosystem, it is essential that we improve the accuracy of thickness retrievals from radar altimetry. This project aims to do just that by making ground-based observations of the radar penetration depth over a full annual cycle at both Ku- and Ka-band frequencies, from autumn freeze-up, through winter snow metamorphism and summer melt. This information, together with detailed snow pack characteristics, will allow us to assess how changes in snow accumulation, snow morphology and snow salinity impact Ku- and Ka-band penetration factors. The MOSAiC drifting station provides a unique opportunity, possibly the only opportunity, to obtain a benchmark dataset that involves coherent field, airborne and satellite data. Analysis of this information will enable scientists to better characterize how the physical properties of the snow pack (above different ice types) influence the penetration of Ka and Ku band radar. Importantly, we will be able to evaluate the seasonal evolution of the snow pack over first-year (sea ice greater than a few cm) and multiyear sea ice. MOSAiC additionally provides the opportunity for year-round observations of snow depth and density that will allow for assessment of the validity of climatological assumptions typically employed in thickness retrievals from radar altimetry and provide data for validation of snow depth products. These activities are essential in order to improve sea ice thickness retrievals from radar altimetry over the many ice and snow conditions found in the Arctic.