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.

A major advance in the reduction of aerofoil trailing edge self-noise has recently been made by the team at Virginia Tech led by Professors William Devenport and Stewart Glegg, collaborators in this project. They demonstrated that introducing 'canopies' into the turbulent boundary layer, which may be constructed from fabric, wires, or rods, produced significant reductions in the surface pressure spectrum near the trailing edge, and hence similar reductions in the far field noise. These treatments were chosen to reproduce the downy canopy that covers the surface of exposed flight feathers of many owl species. Aerofoil self-noise is often the dominant noise source emitted from lifting surfaces, such as aerofoils and turbine blades, and is a major issue in a number of strategically important sectors in the UK, including environment, energy and transport. This work is in its early stages and the precise control mechanisms are poorly understood. This 36-month project is concerned with establishing the fundamental physical control mechanisms of surface treatments with the objective of developing effective treatments on aerofoil geometries at realistic Reynolds numbers and Angle of attack (AoA) that do not significantly degrade aerodynamic performance. The project is a combination of advanced and detailed experimentation together with the application of recent advances in high-resolution computational methods and high-performance computing. At the heart of this project is the use of a new turbulent off-wall boundary condition to allow accurate modelling of the interaction between the boundary layer and canopy surfaces.

Powered by data, Industrial Digital Technologies (IDTs) such as artificial intelligence and autonomous robots, can be used to improve all aspects of manufacturing and supply of products along supply chains to the customer. Many companies are embracing these technologies but uptake within the pharmaceutical sector has not been as rapid. The Medicines Made Smarter Data Centre (MMSDC) looks to address the key challenges which are slowing digitalisation, and adoption of IDTs that can transform processes to deliver medicines tailored to patient needs. Work will be carried out across five integrated platforms designed by academic and industrial researcher teams. These are: 1) The Data Platform, 2) Autonomous MicroScale Manufacturing Platform, 3) Digital Quality Control Platform, 4) Adaptive Digital Supply Platform, and 5) The MMSDC Network & Skills Platform. Platform 1 addresses one of the sector's core digitalisation challenges - a lack of large data sets and ways to access such data. The MMSDC data platform will store and analyse data from across the MMSDC project, making it accessible, searchable and reusable for the medicines manufacturing community. New approaches for ensuring consistently high-quality data, such as good practice guides and standards, will be developed alongside data science activities which will identify what the most important data are and how best to use them with IDTs in practice. Platform 2 will accelerate development of medicine products and manufacturing processes by creating agile, small-scale production facilities that rapidly generate large data sets and drive research. Robotic technologies will be assembled to create a unique small-scale medicine manufacturing and testing system to select drug formulations and processes to produce stable products with the desired in-vitro performance. Integrating several IDTs will accelerate drug product manufacture, significantly reducing experiments and dramatically reducing development time, raw materials and associated costs. Platform 3 focusses on the digitalisation of Quality Control (QC) aspects of medicines development which is important for ensuring a medicine's compliance with regulatory standards and patient safety requirements. Currently, QC checks are carried out after a process has been completed possibly spotting problems after they have occurred. This approach is inefficient, fragmented, costly (>20% of total production costs) and time consuming. The digital QC platform will research how to transform QC by utilising rich data from IDTs to confirm in real time product and process compliance. Platform 4 will generate new understanding on future supply chain needs of medicines to support adoption of adaptive digital supply chains for patient-centric supply. IDTs make smaller scale, autonomous factory concepts viable that support more flexible and distributed manufacture and supply. Supply flexibility and agility extends to scale, product variety, and shorter lead-times (from months to days) offering a responsive patient-centric or rapid replenishment operating model. Finally, technology developments closer to the patient, such as diagnostics provide visibility on patient specific needs. Platform 5 will establish the MMSDC Network & Skills Platform. This Network will lead engagement and collaboration across key stakeholder groups involved in medicines manufacturing and investments. The Network brings together the IDT-using community and other relevant academic and industrial groups to share developments across pharmaceuticals and broader digital manufacturing sectors ensuring cross-sector diffusion of MMSDC research. Existing strategic networks will support MMSDC and act as gateways for IDT dissemination and uptake. The lack of appropriate skills in the workforce has been highlighted as a key barrier to IDT adoption. An MMSDC priority is to identify skills needs and with partners develop and deliver training to over 100 users

The ENCOMPASS project principally aims to create a fully digital integrated design decision support (IDDS) system to cover the whole manufacturing chain for a laser powder bed fusion (L-PBF) process encompassing all individual processes within in. The ENCOMPASS concept takes a comprehensive view of the L-PBF process chain through synergising and optimising the key stages. The integration at digital level enables numerous synergies between the steps in the process chain and in addition, the steps themselves are being optimised to improve the capability and efficiency of the overall manufacturing chain. ENCOMPASS addresses the three key steps in the process chain: component design, build process, and post-build process steps (post-processing and inspection). The links between these stages are being addressed by the following five interrelations: 1. Between the design process and both the build and post-build processes in terms of manufacturing constraints / considerations to optimise overall component design 2. Between the design process and build process component-specific L-PBF scanning strategies and parameters to optimise processing and reduce downstream processing 3. Between the design process and the build and post-build processes in terms of adding targeted feature quality tracking to the continuous quality monitoring throughout the process chain 4. Between the build and post-build processes by using build specific processing strategies and adaptation based on actual quality monitoring data (for inspection and post-processing) 5. Between all stages and the data management system with the integrated design decision support (IDDS) system By considering the entire AM process chain, rather than the AM machine in isolation, ENCOMPASS will integrate process decision making tools and produce substantial increases in AM productivity, with clear reductions in change over times and re-design, along with increased ‘right-first time’, leading to overall reductions in production costs, materials wastage, and over-processing. This will lead to higher economic and environmental sustainability of manufacturing, and re-inforce the EU’s position in industrial leadership in laser based AM.