As energy costs increase, and legislation to reduce carbon emissions are implemented, then existing processes that use significant amounts of heat become expensive; in the case of food production this results in higher prices to the consumer. This work will examine the heat use within three key stages of the baking process (i) proving (where dough is conditioned) (ii) baking in continuous ovens where dough enters on a conveyor and exits as baked product (iii) cooling where the bread is reduced in temperature over a period of 2 hours. This information will be used to guide a study where the potential for (a) reduction and (b) re-use of heat will be assessed. This will be through a two-pronged approach. Firstly a study will be made to retro-fit existing installations for heat recovery and reduction, for example using a 'heat exchanger' to capture waste heat in one part of the process and use it in another. The second approach will be to make a detailed study of the exact conditions within the oven using experimental measurement and computational predictions. The aim here is to establish the baseline conditions for baking, and then determine enhanced equipment setup and designs. The results of this work in the short-to-medium term will be optimised process lines, in the longer term it will guide future design work of ovens and offer better competitiveness to UK industries.

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.

Additive Manufacturing (AM) often known by the term three-dimensional printing (3DP) has been acknowledged as a potential manufacturing revolution. AM has many advantages over conventional manufacturing techniques; AM techniques manufacture through the addition of material - rather than traditional machining or moulding methods. AM negates the need for tooling, enabling cost-effective low-volume production in high-wage economies and the design & production of geometries that cannot be made by other means. In addition, the removal of tooling and the potential to grow components and products layer-by-layer means that we can produce more from less in terms of more efficient use of raw materials and energy or by making multifunctional components and products. The proposed Centre for Doctoral Training (CDT) in Additive Manufacturing and 3D Printing has the vision of training the next generation of leaders, scientists and engineers in this diverse and multi-disciplinary field. As AM is so new current training programmes are not aligned with the potential for manufacturing and generally concentrate on the teaching of Rapid Prototyping principles, and whilst this can be useful background knowledge, the skills and requirements of using this concept for manufacturing are very different. This CDT will be training cohorts of students in all of the basic aspects of AM, from design and materials through to processes and the implementation of these systems for manufacturing high value goods and services. The CDT will also offer specialist training on aspects at the forefront of AM research, for example metallic, medical and multi-functional AM considerations. This means that the cohorts graduating from the CDT will have the background knowledge to proliferate throughout industry and the specialist knowledge to become leaders in their fields, broadening out the reach and appeal of AM as a manufacturing technology and embedding this disruptive technology in company thinking. In order to give the cohorts the best view of AM, these students will be taken on study tours in Europe and the USA, the two main research powerhouses of AM, to learn from their international colleagues and see businesses that use AM on a daily basis. One of the aims of the CDT in AM is to educate and attract students from complementary basic science, whether this be chemistry, physics or biology. This is because AM is a fast moving area. The benefits of having a CDT in AM and coupling with students who have a more fundamental science base are essential to ensure innovation & timeliness to maintain the UK's leading position. AM is a disruptive technology to a number of industrial sectors, yet the CDTs industrial supporters, who represent a breadth of industrial end-users, welcome this disruption as the potential business benefits are significant. Growing on this industry foresight, the CDT will work in key markets with our supporters to ensure that AM is positioned to provide a real and lasting contribution & impact to UK manufacturing and provide economic stability and growth. This contribution will provide societal benefits to UK citizens through the generation of wealth and employment from high value manufacturing activities in the UK.

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