Fusion Forest is a radically novel and interdisciplinary approach to design our future treescapes. We face a challenging scenario, where tree cover needs to be increased - the UK government has set out a 25-year programme to plant 180,000 hectares of trees by 2042 and to increase the woodland cover to 12% by 2060 - whilst there is an alarming increase in tree epidemics. These outbreaks, favoured by increased disease portability, chemical resistances and climate change, compromise the future of our woodlands, producing a dramatic loss of biodiversity and resources. We propose a proactive strategy where new plantations are designed from the onset to halt and suppress diseases using their natural immunity. Fusion Forest achieves so by cutting through discipline boundaries and establishing synergies among the latest discoveries and techniques in tree immunity, ecological modelling and fluids modelling. Fusion Forest seeks to understand how to stimulate priming of defence in trees by using careful combinations of species and lowering the disease pressure. In parallel, the project incorporates the often overlooked spatial component of forest canopies and uses forest heterogeneity to our advantage, creating physical barriers that complement and enhance the ecological ones. To do so, we design a new interdisciplinary modelling framework - named ForestFlow - that brings together forest growth models and computational fluid dynamics. The understanding gained from the ecophysical model, complemented by field and laboratory measurements, allows for a change of paradigm in the way we confront tree epidemics. The response to tree disease outbreaks is mostly reactive, focused on monitoring, chemical treatments and tree felling, with the subsequent environmental and economic costs. Working alongside our partners (landowners, woodland managers, technological companies and policy makers), Fusion Forest will provide the means to prepare forests for outbreaks ahead of their occurrence, reducing critically mitigation costs. Forecasting pathways of transmission opens the door to new strategies to halt the spread of pathogens, that will no longer be assumed to be inevitable. Combining physical and biological barriers for pathogens in our forests is a ground-breaking idea, and Fusion Forest will generate tools and specific guidance to ensure this synergy.

Uncertainty is ubiquitous in the mathematical characterisation of engineered and natural systems. In many structural engineering applications, a deterministic characterisation of the response may not be realistic because of uncertainty in the material constitutive laws, operating conditions, geometric variability, unmodelled behaviour, etc. Ignoring these sources of uncertainties or attempting to lump them into a factor of safety is no longer widely considered to be a rational approach, especially for high-performance and safety-critical applications. It is now increasingly acknowledged that modern computational methods must explicitly account for uncertainty and produce a certificate of response variability alongside nominal predictions. Advances in this area are key to bringing closer the promise of computational models as reliable surrogates of reality. This capability will potentially allow significant reductions in the engineering product development cycle due to decreased reliance on extensive experimental testing programs and enable the design of systems that perform robustly in the face of uncertainty. The proposed investigation will address this important research problem and deliver convergent computational methods and efficient software implementations that are orders of magnitude faster than direct Monte-Carlo simulation for predicting the response of structural systems in the presence of uncertainty. This work will draw upon developments in stochastic subspace projection theory which have recently emerged as a highly efficient and accurate alternative to existing techniques in computational stochastic mechanics. The overall objectives of this project include: (1) formulation of convergent stochastic projection schemes for predicting the static and (low and medium frequency) dynamic response statistics of large-scale stochastic structural systems. (2) design and implementation of a state-of-the-art parallel software framework that leverages existing deterministic finite element codes for stochastic analysis of complex structural systems, and (3) laboratory and computer experiments to validate the methods developed. The methods to be developed will find applications to a wide range of structural problems that require efficient and accurate predictions of performance and safety in the presence of uncertainty. This is a crucial first step towards rational design and control strategies that can meet stringent performance targets and simultaneously ensure system robustness. Progress in this area would also be of benefit to many other fields in engineering and the physical sciences where there is a pressing need to quantify uncertainty in predictive models based on partial differential equations.

The advancement of numerous technologies has become increasingly reliant on the ability to dissipate large quantities of heat from small areas. Current designs in power electronics, supercomputers, lasers, X-ray medical devices, nuclear fusion reactor blankets, spacecraft, and hybrid vehicle electronics, and future improvements, rely on record high heat transfer rates. This rapid increase in heat dissipation rates required by such devices has led to a transition from more traditional fan-cooled heat-sink attachments to liquid cooling techniques. Liquid cooling techniques operating in single-phase, however, have now reached their limit being forced to run at very low inlet temperatures and exceedingly high mass flow rates, resulting in unacceptably high pressure drops and surface temperature gradients. Innovative approaches are urgently needed to overcome these significant shortcomings: one such approach is spray-cooling. Spray-cooling uses a nozzle to break up the liquid coolant into fine droplets that impinge individually on a heated surface. 'Low'- and 'high-temperature' spray-cooling applications involve surface temperatures below and above the critical heat flux (CHF), respectively. Single-phase spray-cooling (relies on liquid sensible heat rise only) provides greater operational stability and spatially uniform heat removal than liquid cooling, reducing the likelihood of large surface thermal gradients, particularly important for fragile electronic components. Two-phase spray-cooling (relies on liquid sensible heat rise and latent heat), are superior to single-phase systems and furthermore, compared to pool/flow boiling alternative systems, offer far less resistance to vapour removal from a heated surface enabling superior drop-surface contact . In fact, the CHF increases from 1.2 MW/m2 (for water pool boiling) to 10 MW/m2 for water sprays in two-phase applications. SANGRIA is an ambitious 3-year collaborative research programme aimed at investigating the fundamental mechanisms and transfer processes underlying spray-cooling. This project combines cutting-edge experimental techniques that furnish spatiotemporally-resolved diagnostics of the thermal, interfacial, and hydrodynamic fields, with multi-scale theory, modelling and 3-D high-fidelity numerical simulation that bridge the molecular and continuum-scales. The deep insights generated from SANGRIA will be harnessed to provide tools that are practically implementable by our industrial partners in order to maximise impact. Industrial and academic partners will provide additional technical support and feedback during the research programme plus pathways for direct industrial impact. The industrial partners include possible users of this technology: TMD Ltd (manufacturers of electronic equipment, high heat flux devices); Oxford naNosystems (manufacturers of enhanced heat transfer surfaces); ANSYS (Software development); Siemens (Software development); Spraying Systems Co. (Nozzle manufacturers); Syngenta (users of nozzles). LaVision offered a 15% discount on their Particle Master System. The academic partners from the University of Nottingham, Sorbonne University, Technical University of Darmstadt and Kyushu University are internationally recognised experts in single and two-phase thermal systems, including spray cooling. Participation and presentations during the HEXAG and PIN meetings will facilitate feedback and technology transfer.

The job of materials science is to develop the materials that we need to make all of the things that we rely on in our daily lives. These range from the materials used to make large scale objects, such as aeroplanes and buildings, right down to the smallest scales like the processors in the electronic devices we use every day. These materials are often complicated and need to be carefully designed with just the right properties needed to do their jobs for many decades and often in incredibly harsh conditions. There are many current challenges that require us to develop new, improved materials. We need to meet our net-zero climate goals and get better at designing products that can be fully recycled, for example. And there are some resources that we currently use in important materials for which we would like to find alternatives. These are difficult challenges and we need to overcome them quickly. But the way that materials scientists have worked to develop a new material in the past is too slow: it can take up to 20 years to develop a new material and we cannot wait that long. Fortunately, recent developments in the computer simulation of materials, in robotics and sensor technology, in our ability to exploit large volumes of data through machine learning and in techniques for quickly making and testing large numbers of different materials can help to speed things up. This idea, bringing digital technologies together to help us make better materials more quickly, is called "Materials 4.0". If we are going to take advantage of Materials 4.0 then we need to make sure that materials scientists have the necessary digital skills. These skills, things like data informatics, machine learning and advanced computer simulation, are not usually covered in depth in undergraduate university courses in science and engineering. So, the Henry Royce Institute, the UK's national institute for advanced materials, in partnership with the National Physical Laboratory, is proposing to set up a Centre for Doctoral Training (CDT) that will take at least 70 science and engineering graduates and train them in the techniques of Materials 4.0. These students will work towards PhDs and become leaders in the field of Materials 4.0. They will undertake research projects in universities across the UK (Cambridge, Oxford, Imperial College, Manchester, Sheffield, Leeds and Strathclyde), tackling a broad range of materials science challenges and developing new approaches in Materials 4.0. The need for these new approaches is widespread, throughout academia and in industry. In recognition of this, the training programme that we develop for the CDT will be made available more widely, in different forms, so that we can disseminate skills in Materials 4.0 to existing researchers in universities and industrial companies as quickly as possible. The training approach of the CDT will be to take our students from "Learners to Leaders" over the course of four years. Our students will be working across boundaries between materials science and computer / data science and between academia and industry. They will build new interfaces and help to develop a common language for communication. To strengthen our students' own learning and to disseminate their skills more widely, we will train our students as trainers so that the students are actively involved in designing and delivering training for fellow researchers and take the role of ambassadors for a cultural shift in materials science to modern ways of working.

The research will investigate the nature of the loading patterns imparted onto tidal stream turbines when positioned and operated within an array and develop operational procedures to mitigate the impacts of these extreme loading patterns. Exposure to open sea wave climates with high wave-current interactions will influence the power generating, structural integrity, product durability and maintenance requirements of the technologies deployed. The research will undertake both experimental and numerical analyses in a manner that will make the results and findings transferable to real-life implementations. This will inform developers of the peak and fluctuating loads that devices are exposed to in a commercial array environment and will also identify and test mitigating actions to be implemented in order to ensure the robustness and sustainability of the array. The dynamic, cyclic loadings on a tidal stream turbine have been shown to depend on the current profile and wave characteristics which can increase the severity of these loads. This must be considered in the design of the turbine. A turbine in an array will be subjected to more complex flows due to its position in the array, which will result in more diverse loading patterns, which must be fully understood by the turbine designers and operators. The project will therefore evaluate and measure the loading and performance of different configurations of tidal stream turbine arrays using numerical modelling and model scaled experiments. The numerical modelling will use fluid and structural modelling. An existing and proven, instrumented, laboratory scale turbine design will used for the tests. Initial work on a three turbine array will be undertaken to create models of a full-scale turbine array to determine the power output, loading patterns and accurate life-fatigue analysis based on realistic site deployment conditions. This information will be formulated to provide a basis for the industry to evaluate anticipated performance, monitoring needs, operational best practice and maintenance regimes in order to deliver the lowest cost of energy from tidal arrays