The Ocean-REFuel project brings together a multidisciplinary, world-leading team of researchers to consider at a fundamental level a whole-energy system to maximise ocean renewable energy (Offshore wind and Marine Renewable Energy) potential for conversion to zero carbon fuels. The project has transformative ambition addressing a number of big questions concerning our Energy future: How to maximise ocean energy potential in a safe, affordable, sustainable and environmentally sensitive manner? How to alleviate the intermittency of the ocean renewable energy resource? How ocean renewable energy can support renewable heat, industrial and transport demands through vectors other than electricity? How ocean renewable energy can support local, national and international whole energy systems? Ocean-REFuel is a large project integrating upstream, transportation and storage to end use cases which will over an extended period of time address these questions in an innovative manner developing an understanding of the multiple criteria involved and their interactions.

It is well established that long-term exposure to aircraft and wind turbine noise is responsible for many physiological and psychological effects. According to the recent studies, noise not only creates a nuisance by affecting amenity, quality of life, productivity, and learning, but it also increases the risk of hospital admissions and mortality due to strokes, coronary heart disease, and cardiovascular disease. The World Health Organization estimated in 2011 that up to 1.6 million healthy life years are lost annually in the western European countries because of exposure to high levels of noise. The noise is also acknowledged by governments as a limit to both airline fleet growth, acceptability of Urban Air Mobility, operation and expansion of wind turbines, with direct consequences to the UK economy. With regards to aerodynamic noise, aerofoil noise is perhaps one of the most important sources of noise in many applications. While aerofoils are designed to achieve maximum aerodynamic performance by operating at high angles of attack, they become inevitably more susceptible to flow separation and stall due to changing inflow conditions (gusts, wind shear, wake interaction). Separation and stall can lead to a drastic reduction in aerodynamic performance and significantly increased aerodynamic noise. In applications involving rotating blades, the near-stall operation of blades, when subjected to highly dynamic inflows, gives rise to an even more complex phenomenon, known as dynamic stall. While the very recent research into the aerodynamics of dynamic stall has shown the complexity of the problem, the understanding of dynamic stall noise generation has remained stagnant due to long-standing challenges in experimental, numerical and analytical methods. This collaborative project, which includes contributions from strong industrial and academic advisory boards, aims to develop new understanding of dynamic stall flow and noise and develop techniques to control dynamic stall noise. The team will make use of the state-of-the-art experimental rigs, dedicated to aeroacoustics of dynamic stall and GPU-accelerated high-fidelity CFD tools to generate unprecedented amount of flow and noise data for pitching aerofoils over a wide range of operating conditions (flow velocity, pitching frequency/amplitude, etc.). The data will then be used to identify flow mechanisms that contribute to the different aerofoil noise sources at high angles of attack, including aerofoil unsteady loading and flow quadrupole sources, and detailed categorisation of dynamic stall regimes. A set of new frequency- and time-domain analytical tools will also be developed for the prediction of dynamic stall noise at different dynamic stall regimes, informed by high-fidelity experimental and numerical datasets. This project will bring about a step change in our understanding of noise from pitching aerofoils over a wide range of operations and pave the way to more accurate noise predictions and development of potential noise mitigation strategies.

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.

Healthy infrastructure is critical in ensuring the continued health of UK society and the economy. Unfortunately, monitoring and maintaining our buildings and transport network is expensive. Considering bridges, inspection is usually carried out visually by human experts. There are not the resources to carry out the inspections as often as desired, or to make any repairs as quickly as needed; in the UK a backlog of maintenance works, identified in 2019, will cost £6.7bn. When resources are stretched, mistakes can be made, sometimes with tragic consequences; in 2018, despite warnings about possible problems, the Morandi Bridge in Genova, Italy, collapsed at a cost of 43 lives. Collapse is not the only problem; extreme weather events driven by climate change can test the performance of infrastructure beyond its limits e.g. consider the cost and inconvenience caused by bridge closures forced by flooding. Bridges are only one concern. The offshore wind (OW) sector has driven down energy costs and increased power output, and now pioneers a global change to clean energy. The UK leads globally in OW energy, with ~8 GW of capacity, expected to exceed 25 GW by 2030, providing almost one third of the UK's annual electricity demand and helping meet the Climate Change Act's (2008) difficult 2050 target for an 80% cut in UK carbon output. The drive for turbines in deeper water demands new ways of asset management, decision making and controlling and limiting operation/maintenance lifetime costs. As turbines increase in numbers, size, and capacity, these issues become even more important. The issues highlighted above are common across all elements of our infrastructure network (this PG will also consider telecoms infrastructure; another key test bed) and can be mitigated by automating the health monitoring. Instead of expensive, error-prone, human inspections, diagnoses can be provided economically by permanently-installed sensors, collecting structural data continuously and interpreting it via computer algorithms. This aim has led to the research discipline of Structural Health Monitoring (SHM), a subject of academic activity for over three decades. Despite intensive effort, SHM has not transitioned to widespread use because of a number of barriers - technical and operational. The main technological barriers are: optimal implementation of hardware systems; confident detection in the face of confounding effects for in situ structures e.g. wind, traffic, for bridges; lack of damage-state data limiting the potential of machine learning for SHM. The operational barriers are: inertia - over-reliance on conservative design codes; trust - the SHM system must be as reliable as the structure itself; transparency - complex technology must deliver interpretable, secure decision support. The key to progress is to shift from thinking about individual structures to thinking about populations. Population-Based SHM (PBSHM) is a game-changing idea, emerging in the UK very recently, with the potential to overcome the technological barriers above and transform our ability to automatically infer the condition of a structure, or a network of structures, from sensor data; this depends on an ability to collect a broader range of data, enriched into knowledge. ROSEHIPS will extend and exploit PBSHM, developing machine learning, sensing and digital twin technology for automated inference of health for structures in operation now, and drive new standards for safer, greener structures in future. The Programme brings together the perfect team, mixing complementary skills in machine learning and advanced data analysis with expertise in new sensor systems and insight into complex infrastructure systems. ROSEHIPS will provide open-source software systems, illustrated by realistic demonstrators and pre-populated with real-world data. Owners/operators will be able to customise and protect/secure their own data, while exploiting the knowledge base given.