Multi-phase, trans/supercritical and non-Newtonian fluid flows with heat and mass transfer are critical in enhancing the performance of energy production, propulsion and biomedical systems. Examples include: hydraulic turbomachines, ship propellers, CO2-neutral e-fuels and e-motor cooling systems, particleladen flows in inhalers and focused ultrasounds for drug delivery. What all these cases have in common is the high level of complexity which makes Direct Numerical Simulations impossible. State-of-the-art LES simulations rely on simplified assumptions but do not have yet the desired accuracy, while often require enormously expensive CPU resources. The aim of project (acronym 'SCALE') is to develop simulation methods and reduced-order models using physics-informed and data-driven Machine Learning and optimisation methods for such flow processes. These will be trained against 'ground-truth' databases that will be generated for the first time using both DNS and experimentally validated, industry-relevant LES and multi-fidelity RANS simulations. The new simulation tools will be applied for the first time to industrial problems and their ability to accelerate design times and improve accuracy will be jointly pursued and evaluated with the non-academic partners of SCALE. These are international corporations and market leaders in the aforementioned areas. Holistic training by experts from science and industry includes broad reviews on relevant scientific topics, modern high performance computing architectures suitable for performing such simulations, big data analytics as well as extensive support for mastering scientific tasks and transferring the knowledge acquired to industrial practice. SCALE will also deliver transferable soft skills training from a well-connected cohort of leaders with the ability to communicate across disciplines and within the general public. This coupling of research with industry makes SCALE a truly outstanding network for doctoral candidates to start their careers.

Our team specialises in the development of finite element methods to computationally simulate fluid flow, particularly low Mach number, transient, separated fluid flows in complex geometries and in the presence of strong multiphysics coupling. These models can be used to make predictions and answer scientific questions in problems ranging from blood flow through an arterial bypass graft to the flow over components of a Formula 1 racing car to explaining how the ocean circulates or predicting the response of the Earth's climate to increased CO2 in the atmosphere. What unifies these flows is that they have common features, such as vortices, that occur across a huge range of sizes and times; these features have a critical effect on the phenomena being studied. The range of these problem means that to address grand challenges such as the flow of blood in the numerous arteries of the human body, over a full Formula 1 car or the interaction of a massive array of tidal turbines, it is necessary to combine state-of-the-art modelling techniques with the capability to run models on massively parallel supercomputers. In recognition of the recent changes in computer hardware, this platform will enable the group to promote the next generation of developers to provide general purpose software that takes advantage of cutting edge computer science to enable effective use of parallel computers using emerging hardware in a way that is accessible to fluid modelling experts as well as computer scientists. Hence this platform brings together a team of computer scientists and computational engineers in a fundamentally multidisciplinary project, with the dual aim of providing flexible, internationally respected and widely adopted software libraries, and of training young researchers in this emerging area.

Composite materials, such as those based on carbon and glass fibre reinforced polymer play an important role in driving global decarbonisation, through corrosion resistant and high-performance products and light-weighting sectors such as transport that lead to improved fuel economy and so reduce emissions. Our proposal targets sustainability of high value composite components, through embedding ultra-thin glass planar sensors, that can be used during manufacture and through a component's life to assess parameters linked to structural performance. Hence informed decisions can be made to extend useable life and reduce the scrappage associated with manufacture. This makes most efficient use of our limited resource of energy and raw materials. In addition to environmental sustainability, this work will also have economic advantages enabling the UK economy to continue to grow innovative technology and associated highly skilled jobs. Despite the huge lightweighting benefit of composites they are not utilised to their full potential due to variability caused at the manufacturing stage. Composite components and the composite material they are made from are produced together. To achieve the desired material geometry features are included in their laminated structure that generate defects. To realise their full set of advantages new methodologies must be devised that support sustainable deployment integrated during production. At the manufacturing stage, many composite components are consigned to scrap before they go into service because of defect evolution. We are proposing a new non-invasive means to better monitor defect evolution and their affect on the final structural performance of the part. Once a composite component goes into service it is often heavier than necessary due to the design parameters necessary for safety assurance. Having an effective means of monitoring critical regions would motivate a means to reduce structural mass by reducing material usage, which in turn would allow increasing payload and or support a shift to heavier but more efficient designs. We are proposing a sensing methodology that can indicate a reduction in structural performance, as our sensors allow changes in through thickness strain to be captured. A laminated composite structure is designed to carry the load in the plane of the laminations as it is weak through the thickness of laminate. Any change in through thickness strain would be a prime indicator of a reduction in performance. At the end of the composite component's life there are currently limited options for recycling composites with 15% of the 110,000 tonnes of composites produced in the UK each year being reused at their end of life. Our sensors would support reuse and repurposing of large composite structures because a complete history of the component life cycle would be available through monitoring informing designers of the suitability to be deployed in other structural applications. To highlight the advantages of using the novel sensors we have chosen three important case studies/exemplars. The first is in the manufacture of thermosetting composites replacing the costly and time-consuming autoclave with microwave processing, which reduces energy consumption significantly. Our planar glass sensors will be non-conducting and so permit comprehensive in process monitoring, supporting uptake of microwave curing. As described above the through thickness strength of laminated composite materials is limited, hence 3D fibre architectures are being explored. Our second case study focuses on braiding process exploiting the sensor's geometry to fix it into a known position during the consolidation of the 3D fibre architecture in a thermoplastic matrix. Finally, we demonstrate the versatility of our sensors in an infield retrofitting application to extend the life of concrete infrastructure using composite repair patches.

The world's oil supply is decreasing rapidly and over the next 10 or 20 years the price per barrel will spiral inexorably. Aviation is a significant consumer of oil and is also implicated in global warming through its generation of massive quantities of carbon dioxide and nitrogen oxide. Aircraft noise continues to be an increasingly important problem as airports expand. For these reasons aviation as we know it now will rapidly become unviable. There is no single solution to the problem and enormous changes to engines, airframe design, scheduling and indeed people's expectations of unlimited air travel are inevitable. Here we address one of the most important issues, improved aerodynamics, and develop the underpinning technology for Laminar Flow Control (LFC), the technology of drag reduction on aircraft. This will become the cornerstone of aircraft design. Even modest savings in drag of the order of 10% translate into huge savings in fuel costs and huge reductions in atmospheric pollution. Applications of the technology to military aircraft where range is often the main requirement and marine applications are similarly important. The development of viable LFC designs requires sophisticated mathematical, computational and experimental investigations of the onset of transition to turbulence and its control. Existing tools are too crude to be useful and contain little input from the flow physics. Major hurdles to be overcome concern: a) How do we specify generic input disturbances for flow past a wing in a messy atmosphere in the presence of surface imperfections, flexing, rain, insects and a host of other complicating features b) How do we solve the mathematical problems associated with linear and nonlinear disturbance growth in complex 3D flows c) How do we find a criterion for the onset of transition based on flow physics which is accurate enough to avoid the massive over-design associated with existing LFC strategies yet efficient enough to be useable in the design office d) How can we use experiments in the laboratory to predict what happens in flight experiments e) How can we devise control strategies robust enough to be used on civilian aircraft f) How can we quantify the manufacturing tolerances such as say surface waviness or bumps needed to maintain laminar flow The above challenges are huge and can only be overcome by innovative research based on the mathematical, computational and experimental excellence of a team like the one we have assembled. The solution of these problems will lead to a giant leap in our understanding of transition prediction and enable LFC to be deployed. The programme is based around a unique team of researchers covering all theoretical, computational, and experimental aspects of the problem together with the necessary expertise to make sure the work can be deployed by industry. Indeed our partnership with most notably EADS and Airbus UK will put the UK aeronautics industry in the lead to develop the new generation of LFC wings. The programme is focussed primarily on aerodynamics but the tools we develop are relevant in a wide range of problems. In Chemical Engineering there has long been an interest in how to pump fluids efficiently in pipelines and how flow instabilities associated with interfaces can compromise certain manufacturing processes. In Earth Sciences the formation of river bed patterns behind topology or man-made obstructions is governed by the same process that describes the initiation of disturbances on wings. Likewise surface patterns on Mars can be explained by the instability mechanisms of sediment carrying rivers. In Atmospheric Dynamics and Oceanography a host of crucial flow phenomena are intimately related to the basic instabilities of a 3D flow over a curved aerofoil. Our visitor programme will ensure that our work impinges on these and other closely related areas and that likewise we are aware of ideas which can be profitably be used in aerodynamics.

Computational science is a multidisciplinary research endeavour spanning applied mathematics, computer science and engineering together with input from application areas across science, technology and medicine. Advanced simulation methods have the potential to revolutionise not only scientific research but also to transform the industrial economy, offering companies a competitive advantage in their products, better productivity, and an environment for creative exploration and innovation. The huge range of topics that computational science encapsulates means that the field is vast and new methods are constantly being published. These methods relate not only to the core simulation techniques but also to problems which rely on simulation. These problems include quantifying uncertainty (i.e. asking for error bars), blending models with data to make better predictions, solving inverse problems (if the output is Y, what is the input X?), and optimising designs (e.g. finding a vehicle shape that is the most aerodynamic). Unfortunately, the process through which advanced new methods find their way into applications and industrial practice is very slow. One of the reasons for this is that applying mathematical algorithms to complex simulation models is very intrusive; mostly they cannot treat the simulation code as a "black box". They often require rewriting of the software, which is very time consuming and expensive. In our research we address this problem by using automating the generation of computer code for simulation. The key idea is that the simulation algorithm is described in some abstract way (which looks as much like the underlying mathematics as possible, after thinking carefully about what the key aspects are), and specialised software tools are used to automatically build the computer code. When some aspect of the implementation needs to change (for example a new type of computer is being used) then these tools can be used to rebuild the code from the abstract description. This flexibility dramatically accelerates the application of advanced algorithms to real-world problems. Consider the example of optimising the shape of a Formula 1 car to minimise its drag. The optimisation process is highly invasive: it must solve auxiliary problems to learn how to improve the design, and it be able to modify the shape used in the simulation at each iteration. Typically this invasiveness would require extensive modifications to the simulation software. But by storing a symbolic representation of the aerodynamic equations, all operations necessary for the optimisation can be generated in our system, without needing to rewrite or modify the aerodynamics code at all. The research goal of our platform is to investigate and promote this methodology, and to produce publicly available, sustainable open-source software that ensures its uptake. The platform will allow us to make advances in our software approach that enables us to continue to secure industrial and government funding in the broad range of application areas we work in, including aerospace and automotive sectors, renewable energy, medicine and surgery, the environment, and manufacturing.