We will train a cohort of 65 PhD students to tackle the challenge of Data Creativity for the 21st century digital economy. In partnership with over 40 industry and academic partners, our students will establish the technologies and methods to enable producers and consumers to co-create smarter products in smarter ways and so establish trust in the use of personal data. Data is widely recognised by industry as being the 'fuel' that powers the economy. However, the highly personal nature of much data has raised concerns about privacy and ownership that threaten to undermine consumers' trust. Unlocking the economic potential of personal data while tackling societal concerns demands a new approach that balances the ability to innovate new products with building trust and ensuring compliance with a complex regulatory framework. This requires PhD students with a deep appreciation of the capabilities of emerging technology, the ability to innovate new products, but also an understanding of how this can be done in a responsible way. Our approach to this challenge is one of Data Creativity - enabling people to take control of their data and exercise greater agency by becoming creative consumers who actively co-create more trusted products. Driven by the needs of industry, public sector and third sector partners who have so far committed £1.6M of direct and £2.8M of in kind funding, we will explore multiple sectors including Fast Moving Consumer Goods and Food; Creative Industries; Health and Wellbeing; Personal Finance; and Smart Mobility and how it can unlock synergies between these. Our partners also represent interests in enabling technologies and the cross cutting concerns of privacy and security. Each student will work with industry, public, third sector or international partners to ensure that their research is grounded in real user needs, maximising its impact while also enhancing their future employability. External partners will be involved in PhD co-design, supervision, training, providing resources, hosting placements, setting industry-led challenge projects and steering. Addressing the challenges of Data Creativity demands a multi-disciplinary approach that combines expertise in technology development and human-centred methods with domain expertise across key sectors of the economy. Our students will be situated within Horizon, a leading centre for Digital Economy research and a vibrant environment that draws together a national research Hub, CDT and a network of over 100 industry, academic and international partners. We currently provide access to a network of >80 potential supervisors, ranging from leading Professors to talented early career researchers. This extends to academic partners at other Universities who will be involved in co-hosting and supervising our students, including the Centre for Computing and Social Responsibility at De Montfort University. We run an integrated four-year training programme that features: a bespoke core covering key topics in Future Products, Enabling Technologies, Innovation and Responsibility; optional advanced specialist modules; internship and international exchanges; industry-led challenge projects; training in research methods and professional skills; modules dedicated to the PhD proposal, planning and write up; and many opportunities for cross-cohort collaboration including our annual industry conference, retreat and summer schools. Our Impact Fund supports students in deepening the impact of their research. Horizon has EDI considerations embedded throughout, from consideration of equal opportunities in recruitment to ensuring that we deliver an inclusive environment which supports diversity of needs and backgrounds in the student experience.

Industrial Control Systems underpin almost all aspects of life in the UK, the power network operated by the National Grid and the rail network, which is over seen by the Rail Safety and Standards Board (RSSB) are two key examples of this. In this project we will work with the National Grid and RSSB to perform a detailed security analysis of their systems, looking for possible points of cyber attack and building an understanding of the impact of possible failures. This will lead to better security for these important systems. Based on what we learn from this analysis we will work with the company Level 3 TRL and Parsons Brinckerhoff to generalise our methods into business processes that other owners of industrial control systems can use to help ensure their systems are safe from cyber attacks.

There are a variety of aerodynamic effects associated with train design and operation - the determination of aerodynamic drag, the effect of cross winds on train stability, pressure transient loading on trackside structures, the physiological effect of tunnel pressure transients, the effect of train slipstreams and wakes on waiting passengers and trackside workers etc. The magnitude of these effects broadly increases as the square of the vehicle speed and thus with the continued development of high speed train lines aerodynamic effects will become more significant in terms of design and operation. Now it can be hypothesised that the techniques that have been used to predict aerodynamic effects in the past (wind tunnel and CFD methods) are likely to determine magnitudes of pressures, velocities, forces etc. that are higher than those observed in practice, where other effects - such as track roughness, variability in meteorological conditions etc. are likely to usually obscure aerodynamic effects to some extent and, because of this, some of the current design methodologies are unnecessarily restrictive and/or conservative. Thus the aim of the current project is to investigate and measure a range of aerodynamic phenomena observed in real train operation, both relative to the train and relative to a fixed point at the trackside, and to compare how such effects match model scale measurements and various types of CFD calculation, and thus to test the validity, or otherwise, of the above hypothesis. This will be achieved through the instrumentation of the Network Rail High Speed Measuring Train to measure aerodynamic effects, as the train carries out its normal duty cycle around the UK rail network. Also trackside instrumentation will be installed at a suitable site that will allow off-train phenomena to be measured. Calibration wind tunnel, CFD and moving model tests will be carried out in the conventional way for comparison with data measured at full scale. The full scale, model scale and computational trials will be carried out by experienced RFs and will provide data for two doctoral studies, one of which will investigate how the train based measurements of cross wind forces, pressure transients etc compare with those predicted by conventional methodologies, and one of which will investigate how the track side measurements compare with conventional test results. The investigators will synthesise the results and make recommendations for future aerodynamic test methods.

Flight Data Monitoring (FDM) is the process by which data from on-board recorders (or so-called 'black boxes') is subject to regular and systematic analysis, not just after emergencies but after every flight. This is performed so that subtle trends which arise as pre-cursors to more serious incidents can be detected in advance and used to proactively manage risk. The same technique is also used as a way of meeting environmental and economic goals through improved operational efficiency, fuel consumption and maintenance. FDM is relevant to the railway industry because since 2005 all trains now have to carry similar on-board recorders. The primary motivation is to provide accident investigators with an invaluable diagnostic tool, but like the aviation sector, because accidents are comparatively rare a far greater quantity of data is collected on normal, routine, non-accident journeys. As a result, recorder data in the rail industry represents a significantly underused resource. The proposed research relates to a class of problem which occurs firmly at the human/system interface and which is shared by both the aviation and rail sectors. Both domains experience problems, for example, Signals Passed At Danger (or SPADs) and Controlled Flight Into Terrain (CFIT), where several safety systems are defeated by human operators and otherwise fully functional trains or aircraft are placed in highly unsafe conditions. Problems such as these fall within the purview of Human Factors. On-board recorder data, be it from the rail or aviation sectors, represents a novel source of input for established human factors methodologies targeted at addressing them. The primary goal of the research, therefore, will be to couple recorder data to human factors methods in a way not previously attempted. The outcome will be 'leading indicators' of problems which, so far, have proven resistant to conventional safety interventions. Related to this are leading indicators, or metrics, that could help to inform ongoing research into operational efficiency, 'eco-driving', and potentially cost-saving insights into future maintenance practices. These opportunities can be systematically examined with reference both to human factors methods and to the mature FDM processes that currently exist in the aviation industry. The project is set against, and motivated by, a wider backdrop of European rail interoperability, a desire to maximise the environmental benefits of rail travel and the UK's position as a world leader in FDM processes. Whilst the research has at its core an innovative programme of theoretical advance, it is also coupled to several near-term applications. Firstly, the UK Civil Aviation Authority (CAA) seek to inform (and be informed of) best practice in other transport domains and the proposed project aims to provide a conduit for such knowledge. Secondly, both the CAA and the Association of Train Operating Companies (ATOC) are actively seeking leading indicators of safety related problems, particularly those which occur at the human/system interface. The project will map important theoretical developments in human factors methods to these real-world applications. Third, the proposed research is directly relevant to current industry projects managed by the Rail Safety and Standards Board (RSSB), including several relating to safety management systems, eco-driving and route knowledge. In summary, the proposed research represents a highly innovative approach to understanding and diagnosing issues which occur at the boundary of humans and transport systems. It is also an example of research with high economic and societal impacts, and an example of research application with great potential to develop further work and collaborations with industry partners.

Railway industry invests considerable resources to manage low adhesion caused by the build-up leaves, despite these efforts, adhesion issues still have a significant safety and financial impact on the industry and society. The current process of treating railheads to resolve the issue has less than 20% efficiency. The treatment plan is based on a set of assumptions and operator's experience, but actual adhesion enhancement levels are not considered as they are unknown. Low adhesion is estimated to cost the UK industry £345m per annum and leads to costly delays as well as safety issues due to the loss of traction, potentially leading to uncontrolled condition and in the worst-case collisions. Rail Standard Safety Board (RSSB) has developed the ADHERE research programme to strategically tackle this challenge. However, the lack of fundamental understanding of the fundamental physics at the rail-wheel interface presents a barrier to progress. The rail-wheel interface is a multi-scale, multi-phase problem which has a highly transitory condition and it is exposed to open operating environments that can produce a variety of contaminations. Understanding the physical and chemical interactions at the interface is challenging, but it is essential and the only route to tackle the problem. In this project, a predictive computational model to simulate adhesion enhancement using sand particles in the rail-wheel interface will be a deliverable. This tool will be calibrated using experimental data at the micro-scale and validated using a full-scale rail-wheel set-up in collaboration with Prof Roger Lewis at the University of Sheffield. Running computational parametric simulations will lead to underscoring the crucial role of particle characteristics to assess the current assumptions stated in the RSSB standard catalogue GMRT2461. I hypothesise that tailoring particle characteristics (such as shape) will enhance 'self-steering' and 'self-entraining' of particles in rail-wheel interface, therefore it reduces particle ejections and increases efficiency. The outcomes of this project will be disseminated to stakeholders at an event hosted by RSSB, in addition to usual academic dissemination routes, i.e. conferences and journals. The main impact of this research work will be: In the short term: developing an understanding of the role of particle characteristics in adhesion enhancement; engagement with public and industry. In the mid-term: informing planning and decision-making models, design engineers and consultants; amendment of standard. In the long term: increased network capacity, reduced carbon, lower costs and improved customer satisfaction.