There is an ever pressing need to develop clean and green methods for energy generation. Fusion is an important part of the solution, in principal at least providing close to unlimited high grade energy with a close to zero carbon footprint. Fusion is at a cross-roads, the physics is well understood but the engineering still needs a great deal of effort to bring about practical fusion. High Temperature Superconductors (HTS) have recently emerged as a serious contender for achieving this goal. The purpose of this project is to overcome the major difficulty of AC losses which are a perennial problem with HTS. HTS are only lossless under DC conditions, this will not be the case with a fusion magnet which will be ramped up and down. In a reactor there are coils which need to be ramped rapidly this will induce AC losses. AC losses are a perennial problem for superconductors which needs to be solved. Once it is solved a huge range of applications will become practical, not only fusion but, for example electric flight, even more powerful wind generators and many more. We aim to solve this problem.

Since 9-11 and 7-7, terrorism has been a major public concern. To ensure public safety and to protect the UK economy, research is needed that offers new methods to foil attacks before they are executed, to identify people and networks who might be preparing for or undertaking an attack, and to provide clear evidence that can be used to justify questioning, arrests and prosecutions. In this study, we will investigate whether deception can be identified and proved from 'scent trails', that is, coherent accounts of suspects' activities over time compiled from tracking their movements, communications and behaviours. We will develop software to derive inferences about what activities are consistent with suspects' scent trails and what are ruled out. These inferences will allow investigators to challenge suspects, both in real time (e.g., to encourage suspects to abandon an ongoing attack) and during interviews (e.g., to point out inconsistencies between a suspect's account and scent trail evidence that might change the course of an interview). The project will investigate scent trails in the context of people undertaking deceptive activities to gain advantage in adversarial 'treasure hunt'-type games. The games will be developed in consultation with stakeholders to provide a non-sensitive analogy to counter-terrorism contexts. Players, typically undergraduate students paid for participation, will be monitored during games via positional and communication data obtained from mobile devices enabled with geospatial positioning devices. Novel software for integrating these data will be developed to build up scent trails of players' activities during game play. Methods of artificial intelligence will be combined to derive inferences from the scent trails about what kinds of activity are possible and impossible given a player's location, trajectory, activities and links with others. We envision games with 3 teams: Team A represent the adversary, Team B the police or general public, and Team C the intelligence services. Team A scores points by visiting target locations within a time limit under a set of game rules that they must violate if they are to win. They must try to hide rule violations from Team B, who score points by preventing or identifying Team A's deceptions successfully. Team C can challenge Team A by sending them indications of the scent trails that are held or can feed Team B intelligence information. Moreover, the inferences from scent trails will support Team C in deciding how best to prove or falsify a suspicion during an interview with Team A players at key points during the games. By conducting observation of players during games, we can investigate how people change their behaviours when they are confronted with evidence that reveals their deceptions. We will also interview players at key points during games as a simulation of interviews with suspects, eliciting from players accounts of their activities before presenting them with challenges based on their own scent trails that are either consistent or inconsistent with legal game playing. This will allow interview and analysis techniques to be improved and will provide clues as to how people subsequently change their behaviour after they have been confronted with their deception. The results will also allow us to test between hypotheses deriving from forensic psychology as to how best to detect deception. The research also allows us to explore public awareness of, and response to, monitoring and surveillance in counter-terrorism. With an advisory panel of stakeholders and subject specialists representing key public and academic bodies, we will identify ethical and legal issues associated with collecting and using data on peoples' movements through public spaces. We will also conduct questionnaire studies with game players and others not involved in the games, to measure attitudes to monitoring and surveillance in game-playing and other contexts.

In highly engineered materials, microscale defects can determine failure modes at the compo-nent/system scale. While X-ray CT is unique in being able to image, find, and follow defects non-destructively at the microscale, currently it can only do so for mm sized samples. This currently presents a significant limitation for manufacturing design and safe life prediction where the nature and location of the defects are a direct consequence of the manufacturing process. For example, in additive manufacturing, the defects made when manufacturing a test-piece may be quite different from those in a three dimensionally complex additively manufactured engineering component. Similarly, for composite materials, small-scale samples are commonly not large enough to properly represent all the hierarchical scales that control structural behaviour. This collaboration between the European Research Radiation Facility (ESRF) and the National Research Facility for laboratory CT (NRF) will lead to a million-fold increase in the volume of material that can be X-ray imaged at micrometre resolution through the development and exploitation of a new beamline (BM18). Further, this unparalleled resolution for X-rays at energies up to 400keV enables high Z materials to be probed as well as complex environmental stages. This represents a paradigm shift allowing us to move from defects in sub-scale test-pieces, to those in manufactured components and devices. This will be complemented by a better understanding of how such defects are introduced during manufacture and assembly. It will also allow us to scout and zoom manufactured structures to identify the broader defect distribution and then to follow the evolution of specific defects in a time-lapse manner as a function of mechanical or environmental loads, to learn how they lead to rapid failure in service. This will help to steer the design of smarter manufacturing processes tailored to the individual part geometry/architecture and help to establish a digital twin of additive and composite manufacturing processes. Secondly, we will exploit high frame rate imaging on ID19 exploiting the increased flux available due to the new ESRF-extremely bright source upgrade to study the mechanisms by which defects are introduced during additive manufacture and how defects can lead to very rapid failures, such as thermal runaway in batteries In this project, we will specifically focus on additive manufacturing, composite materials manufacturing and battery manufacturing and the in situ and operando performance and degradation of such manufactured articles, with the capabilities being disseminated and made more widely available to UK academics and industry through the NRF. The collaboration will also lead to the development of new data handling and analysis processes able to handle the very significant uplift in data that will be obtained and will lead to multiple site collaboration on experiments in real-time. This will enable us to work together as a multisite team on projects thereby involving less travelling and off-setting some of the constraints on demanding experiments posed by COVID-19.

VISION: To create, run and exploit the world's leading research programme in mobile autonomy addressing fundamental technical issues which impede large scale commercial and societal adoption of mobile robotics. AMBITION: We need to build better robots - we need them to be cheap, work synergistically with people in large, complex and time-changing environments and do so for long periods of time. Moreover, it is essential that they are safe and trusted. We are compelled as researchers to produce the foundational technologies that will see robots work in economically and socially important domains. These motivations drive the science in this proposal. STRATEGY: Robotics is fast advancing to a point where autonomous systems can add real value to the public domain. The potential reach of mobile robotics in particular is vast, covering sectors as diverse as transport, logistics, space, defence, agriculture and infrastructure management. In order to realise this potential we need our robots to be cheap, work synergistically with people in large, complex and time-changing environments and do so robustly for long periods of time. Our aim, therefore, is to create a lasting, catalysing impact on UKPLC by growing a sustainable centre of excellence in mobile autonomy. A central tenet to this research is that the capability gap between the state of the art and what is needed is addressed by designing algorithms that leverage experiences gained through real and continued world use. Our machines will operate in support of humans and seamlessly integrate into complex cyber-physical systems with a variety of physical and computational elements. We must, therefore, be able to guarantee, and even certify, that the software that controls the robots is safe and trustworthy by design. We will engage in this via a range of flagship technology demonstrators in different domains (transport, logistics, space, etc.), which will mesh the research together, giving at once context, grounding, validation and impact.

Uncertainty quantification, verification and validation are crucial to establish the reliability and reproducibility of all forms of computer-based simulation. We propose to establish an open source and open development VVUQ toolkit optimised for efficient execution at current pre- and emerging exascale, which will raise new challenges and new opportunities for simulations in fields as diverse as fusion and climate modelling. Computer simulation results are validated compared with experiment in several ways, ranging from qualitative to quantitative measures which apply a validation metric. Likewise, verification is concerned with confirmation that the mathematical model and corresponding algorithm have been coded correctly. Uncertainty quantification (UQ) is concerned with understanding the origins of and assessing the magnitudes of the errors which accompany computer simulations, whether epistemic or aleatoric. VVUQ is necessary for any simulation that makes predictions in advance of an event to become actionable - that is, for its output to be useful in any form of decision-making process, from government interventions in pandemics to the choice of materials to combine for aircraft wing production. Here, exascale computing offers more opportunities to make actionable predictions. Moreover, because VVUQ is intrinsically compute intensive due to its ensemble-based execution pattern, it too requires exascale resources, as well as advanced resource management strategies to efficiently manage the large numbers of concurrent runs necessary. We propose to establish an open source and open development VVUQ toolkit optimised for efficient execution at current pre- and emerging exascale. This will include advanced approaches for surrogate modelling in order to minimise the expense and time needed to perform the most compute-intensive calculations and will demonstrate its efficiency gains for a diverse array of VVUQ workflows within multiple scientific applications, and on architecturally and geographically diverse emerging exascale environments. The software developed, implemented and benchmarked in this project will become an open and invaluable asset to the UK ExCALIBUR community but also much more widely within UK and internationally as high-performance computing enters the exascale era. The proposed exascale toolkit will be built on a combination of widely used tools and services which will be evolved to handle systems of increasing levels of complexity. These include components from the VECMA project (EasyVVUQ, FabSim3, QCG-PJ and EasySurrogate), as well as the UCL-Alan Turing Institute Multi-Output Gaussian Process Emulator (MOGP). We will apply these capabilities to several applications, including: (i) the UKAEA's tokamak fusion modelling use case for which a working software environment will be produced; (ii) weather and climate forecasting for the Met Office; (iii) turbulent flow simulation for environmental science; (iv) prediction of advanced materials properties of graphene-polymer based nanocomposites for aerospace applications; (v) high-fidelity patient-specific virtual human blood flow system for medical research; (vi) drug discovery; and (vii) human migration.