In March 2011 a magnitude-9.0 earthquake struck in the Pacific Ocean off the northeast coast of Japan's Honshu island. Named the Great East Japan Earthquake by the Japanese government, it triggered a massive tsunami that flooded more than 200 square miles of coastal land. This devastating disaster caused a series of catastrophic failures resulting in the meltdown of the Fukushima Daiichi Nuclear Power Plant (NPP) and initiated a nuclear emergency. Reactor meltdown occurs when the cooling systems used to maintain and control the temperature of the nuclear fuel fails. The fuel then heats up uncontrollably and breaches the containment vessel or creates enough pressure to cause an explosion. Reactor meltdown occurred at all three reactors at Fukushima, resulting in fuel debris being dispersed throughout the reactors. Retrieval of the fuel debris from the Fukushima Daiichi NPP is of great importance for decommissioning and waste management. It requires detailed understanding of the radioisotope composition within the debris and knowledge of their location. However, inside the stricken reactors' containment vessels, the radiation levels are so intense it presents a significant challenge. It prevents direct human intervention, can overwhelm detectors and sensors, damage electronics and cause materials to perish. Access routes to inside the containment vessels are also very narrow. To make general observations, identify fuel debris composition, location and retrieval, dedicated robots are deployed. Many of the robots deployed to date have failed due to radiation damage during operation or their function is severely hampered by the extreme environment. This project brings together two world-leading research activities in the United Kingdom associated with radiation-hard, portable radiation detection (Lancaster University) and the development of small, radiation-hard remotely-operated vehicles (The University of Manchester) in collaboration with Okayama University and Kobe City College of Technology who have pioneered radiation-hard processors. The key aim of the research is to develop and deploy a simplified robot that prioritises radiation hardness and reliability over functional complexity. The hypothesis is, 'can such robots be more effective than the sophisticated alternatives tried to date?'. The ground-based radiation-hard robot will be equipped with non-destructive sensors for remote inspection. A radiation tolerant payload consisting of radiation sensors and LiDAR (light detection and ranging) will afford 3-dimensional (3D) spatial mapping of highly radioactive environments superimposed with located radiation intensities and radioisotope identities. The robot will be tested in realistic fields to demonstrate its ability to locate and identify dispersed radioisotopes derived from nuclear fuel debris inside Fukushima's stricken reactors. Such technology is also applicable to the UK's nuclear decommissioning challenges, specifically at Sellafield Site Ltd., and world-leading research in fusion energy at the UK Atomic Energy Authority.

In high-value manufacturing sectors such as aerospace and nuclear, safety is paramount. For this reason, the design and qualification of inspection for safety-critical components is a crucial part of the overall development cycle. However, current practice makes extensive use of experimental trials on physical components and mock-ups, into which artificial defects, limited to small numbers of specific test cases, must be introduced to demonstrate that they can be detected and characterised. Inspection qualification is therefore extremely time-consuming and costly (with some full mock-ups of defect-containing components costing £millions), and at odds with the general move toward agile, small-batch, bespoke, digitally-enabled manufacturing. We propose replacing the use of these expensive, wasteful, physical test specimens with digital alternatives, to improve manufacturing efficiency. Delivering this will require high-speed, representative, realistic numerical simulation capabilities to be developed, in combination with solutions to reliably sample and interpolate across the high dimensionality of the parametric space. This virtual testing capability will enable the inspection of a high value component to be designed, optimised, and qualified before a single part has been manufactured. It will provide the basis of a simulation tool for operator training and be able to generate data at the scale and fidelity needed to train future machine learning solutions for inspection automation. Ultrasonic array inspection will be the demonstrator case as this is the most widely used method for assessing the internal integrity of safety-critical components, both at manufacture and in service. To achieve the goal requires validated tools to synthesise authentic inspection data at scale and a methodology to robustly explore the vast parameter space of possible defects to determine inspection performance. Our idea to achieve this ambitious vision is to approach the problem from two complimentary directions. Bottom-up: we will make the direct numerical simulation of raw data more efficient. Building on previous world-leading research by the applicants, we will show how numerical simulation tools can be better exploited to reduce the computational burden by at least one order of magnitude. Top-down: we will make the quantitative characterisation of the multi-dimensional parameter space to qualify inspection performance more efficient. Drawing on our domain knowledge and in extensive discussion with industrial collaborators (Rolls-Royce, EDF, Jacobs, Airbus, and KANDE), we will develop suitable surrogate modelling, sampling, and integration strategies for accurately characterising the parameter space with a small number of high-fidelity numerical simulations. In addressing this problem we will produce a set of tools and techniques that ensure that inspection qualification is reduced in cost and complexity by orders of magnitude, leaving it fit for the future of digital manufacturing.

Nuclear reactors in various forms are increasingly prominent in the context of net zero. However, stringent safety standards and advanced reactor designs necessitate ever-greater certainty and understanding in reactor physics and operation. As physical experimentation becomes more expensive, nuclear engineering relies increasingly on high-fidelity simulation of reactors. Traditionally, resolving different physical phenomena in a reactor (such as neutron transport or thermal-hydraulics) proceeded by assuming only a weak dependence upon other phenomena due to limits on computational power. Such approximations were allowable when additional conservatisms were included in reactor designs. However, more economical or sophisticated reactor designs render such approximations invalid, and reactor designers must be able to resolve the interplay between each physical phenomenon. This poses a challenge to reactor physicists due to vastly increased computational costs of multi-physics calculations, as well as the risks of numerical instabilities - these are essentially non-physical behaviours which are purely an artefact of simulation. This proposal aims to provide the basis of new computational approaches in nuclear engineering which are both substantially cheaper and more stable than present multi-physics approaches. Traditional methods tend to have one tool fully resolve one phenomenon, pass the information to another tool which resolves a second phenomenon, and then pass this updated information back to the first tool and repeat until (hopefully) the results converge. This proposal hopes to explore a slightly simpler approach, where information is exchanged between different solvers before each has fully resolved its own physics, extending this to many of the phenomena of interest to a reactor designer. Preliminary analysis suggests that this approach should be vastly more stable and computationally efficient than previous methods. The investigations will be carried out using home-grown numerical tools developed at the University of Cambridge which are designed for rapid prototyping of new ideas and algorithms. The final result is anticipated to transform the nuclear industry's approach to multi-physics calculations and greatly accelerate our ability to explore and design more advanced nuclear reactors.

Our infrastructure is central to the economic prosperity of the nation and to the flourishing of a stable, yet dynamic, civil society. Its interconnected strands - the energy, transportation, water, sanitation and communication networks that provide access to services and markets and which underpin the securities of daily life - must be not only affordable and reliable but also resilient against threats such as technological uncertainty, environmental causes, economic and political change, and demographic and societal change unfolding in an increasingly uncertain world. FIBE2 CDT will lead a paradigm shift in the approach to infrastructure resilience through the creation of an inspirational doctoral training programme for talented cohorts from diverse academic and social backgrounds to conduct world-class, cutting-edge and industry-relevant research. Our goal is to develop the infrastructure professionals of the future, equipped with a versatile and cross-disciplinary skillset to meet the most complex emerging challenges, harness the full value of existing infrastructure and contribute effectively to better infrastructure decision-making in the UK. The programme's technical focus will exploit high-level interconnected research themes in advanced infrastructure materials, rethinking design & construction, digitised civil engineering, whole-life performance, built environment and global challenges, along high-level crosscutting themes in emerging technologies, performance to data to knowledge, research across scales, and risk and uncertainty. In FIBE2 CDT we offer a radical rethink to deliver innovation for the cross-disciplinary and interconnected challenges in resilient infrastructure. Our 1+3 MRes/PhD programme proposes a new approach to infrastructure research where students from different disciplines proactively forge new training and research collaborations. FIBE2 is inspired by the paradigm of a 3D 'T' shaped engineer embodying a combination of depth and breadth of knowledge, augmented by our new thinking around cross-disciplinary training and research. High level Infrastructure Engineering concepts will be interlinked and related to the detailed technical fundamentals that underpin them in bespoke core and elective modules. Cohort-based learning will bridge across the wider environmental, societal, economic, business and policy issues within the even broader context of ethics, responsible innovation and ED&I. These depth and breadth elements are interwoven and brought together through problem-based challenges using large-scale cross-disciplinary infrastructure projects. Individual student plans will be carefully crafted to harmonise the specificity of PhD research with the need for expansive understanding of threats and opportunities. The development of Resilient FIBE2 CDT students with strong personal, technical and professional resilience attributes is integral to the FIBE2 approach to training and research. The FIBE2 PhD projects will build upon Cambridge's internationally leading research, investment and funding in the diverse areas related to infrastructure and resilience. Our major strategic initiatives include >£60M funding from EPSRC and industry. Our engagements in UKCRIC, CDBB, Alan Turing and Henry Royce Institutes and our world class graduate training programmes provide an inspirational environment for the proposed CDT. The FIBE2 vision has been co-created with our 27 strategic industry partners from across all infrastructure sectors and nine international academic centre partners across the world, who have pledged over £12M. We will work together to deliver the FIBE2 CDT objectives and add new dimensions to our students' experience. The lasting impact of FIBE2 will be embodied in our students acting as role models to inspire future generations of infrastructure engineers and rising to lead the profession through all the technological and societal challenges facing UK infrastructure.