One of the main contributors towards the cost of high-value engineering assets is the cost of maintenance. Taking an aircraft out of service for inspection means loss of revenue. However, if damage occurs and leads to catastrophic failure, safety and casualties are major issues. In terms of an offshore wind farm, the cost of an unscheduled visit to a remote ocean site to replace a 75m blade is exceedingly high. If one adopts an approach to maintenance where the structure of interest is monitored constantly by permanent sensors, and data processing algorithms alert the owner or user when damage is developing, one can optimise the maintenance programme for cost without sacrificing safety. If damage is detected early, repair rather than replacement can be viable. The complexity of modern structures and their challenging operating environments make it difficult to develop algorithms that can detect and identify early damage. The relevant discipline - structural health monitoring (SHM) - suffers from problems that have prevented uptake of the technology by industry. Although structural complexity makes analysis difficult, one variant of SHM - the data-based approach - shows great promise. In this case one uses machine learning techniques to diagnose damage from measured data. Data-based SHM faces a number of challenges; the first is that most data-based approaches to SHM require measured data from the structure in all possible states of damage. For a structure like an 5 MW wind turbine - it is simply not conceivable that one should damage a single one for data collection purposes, let alone many. Fortunately, if one is only interested in whether damage is present or not, this is possible using only data from the healthy condition. One builds a picture of the healthy state of the structure and then monitors for deviations. This raises a second issue with data-based SHM; if one is monitoring the structure for changes, one does not wish to be deceived by a benign change in its environmental/operational conditions - so-called 'confounding influences'. The original Fellowship aimed to solve these problems via a population-based approach to SHM modelled on the discipline of 'syndromic surveillance' (SS), which is used to detect disease outbreaks in human populations. The core of the proposed research was an intelligent database holding data across populations of structures, and an inference engine that could use damage data from an individual, to allow diagnostics on others. The original work has progressed very well; the required database was created and algorithms for inference across populations have been developed and demonstrated. Algorithms for removing confounding influences have also been created which are arguably now the state of the art. The Fellowship so far has also allowed insights into how population-based SHM can go far beyond technologies based on SS, leading to this new proposal. Very new concepts in SHM will be explored. The first idea is to extend the 'database' to an 'ontology'; ontologies encode, share and re-use domain knowledge. In a way, moving to an ontology adds a 'language centre' to the existing storage and processing; one might even think of the result as a computational brain concentrating on a specific engineering field - in this case SHM. New population-based methods are proposed. For populations of near-identical structures, the idea of the 'form' of a structure is presented. The form is created to represent all individuals in a population, if damage data are available for an individual turbine in a wind farm, they can be transferred into the form and thus allow inference across the farm. Furthermore, a general theory of populations of disparate structures will be constructed using ideas from mathematics and computation: geometry, graph theory, complex networks and machine learning. Again, the theory will allow damage data from individuals to generate insights across the population.

The UK is the world leader in offshore wind energy; almost 40% of global capacity is installed in UK waters. A new ambitious target of 40GW of wind power by 2030 aims to produce sufficient offshore wind capacity to power every home, helping to achieve net zero carbon emissions by 2050. Offshore wind turbine (OWT) foundations, which are typically steel monopiles, contribute approximately 25% to a windfarm's capital cost. The size of OWTs is increasing rapidly and continued optimisation of foundation design is paramount. Recent research has led to significant advances through theoretical developments combined with high-quality field-testing. Despite recent advances, there remains significant uncertainty in the measurement and interpretation of key soil deformation parameters that underpin new and existing design approaches. The central aim of SOURCE is to use rigorous measurement and interpretation in the field and laboratory to quantify and reduce material parameter uncertainty and minimise the impact on the predictive capability of OWT foundation design methods. Improved site characterisation will contribute to increased security in design, lowering capital costs, subsidies and carbon emissions and meeting the UK's ambitious new energy targets.

The need for the UK to shift to NetZero was highlighted at COP26 in Glasgow, and there is a clear need for UK energy security. UK policy to achieving these is based on massive expansion of off-shore wind. In 2022 Crown Estate Scotland "ScotWind" auctioned 9,000 km2 of sea space in the northern North Sea, with potential to provide almost 25 GW of offshore wind. Further developments are planned elsewhere, for example, the 300 MW Gwynt Glas Offshore Wind Farm in the Celtic Sea. These developments mark a shift in off-shore wind generation, away from shallow, well mixed coastal waters to deeper, seasonally stratified shelf seas This shift offers both challenges and opportunities which this proposal will explore. Large areas of the NW European shelf undergo seasonal thermal stratification. This annual development of a thermocline, separating warm surface water from cold deep water, is fundamental to biological productivity. Spring stratification drives a bloom of growth of the microscopic phytoplankton that are the base of marine food chains. During summer the surface layer is denuded of nutrients and primary production continues in a layer inside the thermocline, where weak turbulent mixing supplies nutrients from the deeper water and mixes oxygen and organic material downward. Tidal flows generate turbulence; the strength of turbulence controls the timing of the spring bloom, mixing at the thermocline, and the timing of remixing of the water in autumn/winter. Determining the interplay between mixing and stratification is fundamental to understanding how shelf sea biological production is supported. Arrays of large, floating wind turbines are now being deployed over large areas of seasonally-stratifying seas. These structures will inject extra turbulence into the water, as tidal flows move through and past them. This extra turbulence will alter the balance between mixing and stratification: spring stratification and the bloom could occur later, biological growth inside the thermocline could be increased, and more oxygen could be supplied into the deep water. There could be significant benefits of this extra mixing, but we need to understand the whole suite of effects caused by this mixing to aid large-scale roll-out of deep-water renewable energy. eSWEETS will conduct observations at an existing floating wind farm in the NW North Sea to determine how the extra mixing generated by tides passing through the farm affect the physics, biology and chemistry of the water. We will measure the mixing of nutrients, organic material and oxygen within the farm, and track the down-stream impacts of the mixing as the water moves away from the wind farm and the phytoplankton respond to the new supply of nutrients. We will use autonomous gliders to observe the up-stream and down-stream contrasts in stratification and biology all the way through the stratified part of the year. We will use our observations to formulate the extra mixing in a computer model of the NW European shelf, so that we can then use the model to predict how planned renewable energy developments over the next decades might affect our shelf seas and how those effects might help counter some of the changes we expect in a warming climate. Stratification is so fundamental to how our seas support biological production that we will develop a new, cost-effective way of monitoring it. We will work with the renewables industry and modellers at the UK Met Office on a technique that allows temperature measurements to be made along the power cables that lie on the seabed between wind farms and the coast. Our vision is that large-scale roll-out of windfarms will lead to the ability to measure stratification across the entire shelf. This monitoring will help the industry (knowledge of operating conditions), government regulators (environment responses to climate change) and to operational scientists at the UK Met Office (constraining models for better predictions).

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.

This proposal is to establish a DTC in Wind and Marine Energy Systems. It brings together the UK's leading institutions in Wind Energy, the University of Strathclyde, and Marine Energy, the University of Edinburgh. The wider aim, drawing on existing links to the European Research Community, is to maintain a growing research capability, with the DTC at is core, that is internationally leading in wind and marine energy and on a par with the leading centres in Denmark, the USA, Germany and the Netherlands. To meet the interdisciplinary research demands of this sector requires a critical mass of staff and early stage researchers, of the sort that this proposal would deliver, to be brought together with all the relevant skills. Between the two institutions, academic staff have in-depth expertise covering the wind and wave resource, aerodynamics and hydrodynamics, design of wind turbines and marine energy devices, wind farms, fixed and floating structures, wind turbine, wind farm and marine energy devices control, power conversion, condition monitoring, asset management, grid-integration issues and economics of renewable energy. A centre of learning and research with strong links to the Wind and Marine Energy industry will be created that will provide a stimulating environment for the PhD students. In the first year of a four year programme, a broad intensive training will be provided to the students in all aspects of Wind and Marine Energy together with professional engineer training in research, communication, business and entrepreneurial skills. The latter will extend throughout the four years of the programme. Research will be undertaken in all aspects of Wind and Marine Energy. A DTC in Wind and Marine Energy Systems is vital to the UK energy sector for a number of reasons. The UK electricity supply industry is currently undergoing a challenging transition driven by the need to meet the Government's binding European targets to provide 15% of the UK's total primary energy consumption from renewable energy sources by 2020. Given that a limited proportion of transport and heating energy will come from such sources, it is expected that electricity supply will make the major contribution to this target. As a consequence, 40% or more of electricity will have to be generated from non-thermal sources. It is predicted that the UK market for both onshore and offshore wind energy is set to grow to £20 billion by 2015.There is a widely recognised skills gap in renewable energy that could limit this projected growth in the UK and elsewhere unless the universities dramatically increase the scale of their activities in this area. At the University of Strathclyde, the students will initially be housed in the bespoke accommodation in the Royal College Building allocated and refurbished for the existing DTC in Wind and Marine Energy Systems then subsequently in the Technology and Innovation Centre Building when it is completed. At the University of Edinburgh, the students will be housed in the bespoke accommodation in the Kings Buildings allocated and refurbished for the existing IDC in Offshore Renewable Energy. The students will have access to the most advanced design, analysis and simulation software tools available, including the industry standard wind turbine and wind farm design tools and a wide range of power system and computation modelling packages. Existing very strong links to industry of the academic team will be utilised to provide strategic guidance to the proposed DTC in Wind and Marine Energy through company membership of its Industrial Advisory Board and participation in 8 week 7 projects as part of the training year and in 3 year PhD projects. In addition, to providing suggestions for projects and engaging in the selection process, the Industry Partners provide support in the form of data, specialist software, access to test-rigs and advice and guidance to the students.