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.

In 2003 BP installed a dense array of seimic recording equipment on the sea bottom above the Vallhall oil field in the North Sea. Nearly 2500 state-of-the-art seismometers were attached to 120 km of cables that cover a 45 square km area and are connected to a recording platform. The installation is the first of its kind anywhere in the world and cost nearly US$45million. Such permanent monitoring allows the acquisition of ship-borne seismic surveys at regular intervals in time (so-called 4D seismics) for the life of the field (hence the name Life of Field Seismic or LoFS). Because the surveys are identical each time the data can be used to very accurately monitor changes in the reservoir, for example, the migration of oil due to production. The multicomponent sensors can also be used to record less conventional data. For example, in this part of the North Sea shear-waves are much better than the conventional first arriving P-waves at imaging through the cloud of gas that lies above the reservoir. This new way of monitoring an oil field has dramatically improved reservoir management and productivity, and reduce costs in the long term. The sensors are continuously recording, even when active-source (airguns) ship surveys are not being conducted. Thus there is great untapped potential in using these data to study small earthquakes in the subsurface. Such microseismic events are useful because they provide information about regional tectonics and production related forces. They provide information about fault locations and fluid migration, knowledge of which are of great importance to production. Furthermore, such stress releases can lead to well failure (borehole breakout), which costs the industry billions of pounds each year and can be quite dangerous. We are proposing a study of these micro-earthquakes by developing sophisticated imaging techniques that will use the sensors like eyes that can look in different directions into the Earth. Whilst these earthquakes a very small (they release roughly the same amount of energy as breaking a pencil) they can be accurately located and studied because of the redundancies afforded by such an immense amount of data. We can use standard techniques from conventional earthquake seismology to infer the orientation of fault planes and the stress field in the reservoir. A further synergy comes from the detailed information about the field that BP has at hand (e.g., velocity structure). We will work closely with BP staff and will be allowed to use their massive computing clusters to process the data. We are one of the very first organisations being allowed to look at this exciting dataset and the project will produce high-profile results. This is a unique and timely opportunity.

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.

Moore's Law states that the number of active components on an microchip doubles every 18 months. Variants of this Law can be applied to many measures of computer performance, such as memory and hard disk capacity, and to reductions in the cost of computations. Remarkably, Moore's Law has applied for over 50 years during which time computer speeds have increased by a factor of more than 1 billion! This remarkable rise of computational power has affected all of our lives in profound ways, through the widespread usage of computers, the internet and portable electronic devices, such as smartphones and tablets. Unfortunately, Moore's Law is not a fundamental law of nature, and sustaining this extraordinary rate of progress requires continuous hard work and investment in new technologies most of which relate to advances in our understanding and ability to control the properties of materials. Computer software plays an important role in enhancing computational performance and in many cases it has been found that for every factor of 10 increase in computational performance achieved by faster hardware, improved software has further increased computational performance by a factor of 100. Furthermore, improved software is also essential for extending the range of physical properties and processes which can be studied computationally. Our EPSRC Centre for Doctoral Training in Computational Methods for Materials Science aims to provide training in numerical methods and modern software development techniques so that the students in the CDT are capable of developing innovative new software which can be used, for instance, to help design new materials and understand the complex processes that occur in materials. The UK, and in particular Cambridge, has been a pioneer in both software and hardware since the earliest programmable computers, and through this strategic investment we aim to ensure that this lead is sustained well into the future.

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.