Identifying and understanding how engineering structures respond over time to different loads is a key task which supports many other activities. For example, the creation of digital twins for design, production and management of engineering systems is currently a key area of development across academia and industry. This project aims to extend the range of operational scenarios in which this is possible and to further automate the process. The offshore energy sector is a good example of an application area where the technology being developed in this project can support enhanced insight into an engineering system. The nature of the environment often means direct measurements of structural loads is infeasible. For example, the forces generated by the wind on a turbine blade or by the waves on a mono-pile structure are difficult to measure but important in the structure's operation. This difficulty motivates the development of methods which can infer, not only the dynamic properties of these systems, but the unmeasured inputs (loads) from the available measurements. The project will also extend the identification task further to include the ability to make autonomous modelling decisions. In other words, the information from collected data is fused with prior engineering judgement to learn appropriate ways to represent the system automatically. The technology developed in this project will support operations and maintenance activities in two key ways. By providing improved estimates of the properties of engineering structures and the conditions they have operated in, decisions can be made with an increased level of confidence. Secondly, automating the way engineers build models reduces subjectivity in the process and frees up engineers to focus on where their intervention will bring greatest benefit. The key outcome of this project will be allowing engineers better understanding of the behaviour of structures of interest, developing better representations of them - such as digital twins - and improving their ability to make operations and maintenance decisions based on measurements. Overall, this increased insight into engineering systems will allow for more targeted and effective management. The result of which is reduced unnecessary maintenance, hence a reduction in costs and reduction in risk to the staff that have to perform maintenance in these harsh environments. There are challenges in this proposal; the first is to enhance existing methodologies for learning the inputs and parameters of a dynamic system to include more complex loading scenarios. These scenarios include modelling multiple correlated forces, distributed loads and loads at unknown locations. The second challenge is to bring further automation to the process of deciding an appropriate model for the dynamic system. It is hypothesised that by addressing these two challenges, determination of multiple/distribution unknown loads and automated learning of the dynamic model structure, the range of situations which can be robustly considered is increased. This enhanced understanding will greatly aid the incorporation of dynamic models in virtualisation of engineering systems and the development of smart infrastructure.

This project aims to improve the current techniques used to assess the condition and safety of offshore and aerospace structures. The platforms used by the Oil and Gas industry in the North Sea were designed to operate for around 25 years in total. Over 600 of these platforms have now reached the end of their design life and the decision must be taken as to whether they can continue to be used safely or whether they should be decommissioned. For new offshore wind turbines, it is critical to have a good understanding of current structural condition so that maintenance can be planned optimally - unscheduled maintenance and downtime is extremely costly, owing to the difficulty of accessing these structures. Equally, in the aerospace industry, the ability to follow a condition-based maintenance strategy will save much time and money in avoiding unscheduled/emergency repair work. This project brings together researchers from the University of Sheffield, who are experts in Structural Health Monitoring and nonlinear system modelling, with industry experts who are leading the way in the monitoring and assessment of offshore and aerospace structures. The aim of this collaboration is to develop the most accurate means possible of assessing structural condition using monitoring data. The approach that will be taken here will combine the latest developments in artificial intelligence with more traditional methods that exploit understanding of the physics at work. Predictive models based on well-understood physics can often fall short of being able to explain complex behaviour, such as the loading an offshore structure will experience in a changing sea-state. This is where learning from measured data can be used to augment the model and improve prediction at times when the physics doesn't explain the behaviour captured by the sensors. The combination of physics and data-based models will be used to improve the prediction of the forcing a structure experiences from a changing environment. An accurate quantification of this enables one to calculate the stresses a structure has undergone, which leads to a prediction of its current condition. A similar modelling approach will be used to help make predictions about the structure itself. Finally, as well as improving the accuracy of the methods used to assess structural condition, the project aims to quantify the amount of uncertainty inherent in the models and algorithms that will be implemented. This approach acknowledges the fact that it is not always possible to make an accurate prediction of structural condition at a given time, but allows a confidence level to be assigned to each assessment made. To make responsible and optimal decisions concerning the repair or decommission of a structure, understanding the level of confidence one has in an assessment of structural condition is absolutely key.

Our modern industrial society produces increasing amounts of waste. Yet many of these wastes might either contain useful materials (perhaps metals or nutrients) or could themselves be used as an input for another process (maybe as a fuel or raw material). Recovering these resources from wastes is an important part of waste management and normally involves collecting wastes from an industrial process, organisation or community and then carrying out sorting, reprocessing, recycling or incineration. All these activities have benefits and impacts in many different ways, for example: * the economy: benefits come from selling the recovered materials, while the impacts are the costs of collection, processing etc; * our society: providing reprocessing jobs is a benefit, at the cost of harsh rules on rubbish collection; * the environment: preventing harmful materials from being dumped helps the environment, but reprocessing may involve carbon emissions or use of more resources; * our health: keeping the streets clean prevents disease, but some recycling jobs may be dangerous. When choosing which resource recovery system is best, it is difficult to weigh up all these factors. Often, we simply 'bolt on' a piece of technology to the end of the process, worrying mainly whether it is cost-effective and often assuming that because we are recovering resources, the environmental impact is automatically good. But many recovery systems have 'hidden' impacts that require complex analysis to untangle. Studies have shown that in some cases, collection and recycling of plastic bottles produces more carbon dioxide and uses more resources than simply making new bottles; a hidden environmental impact. In fact, much of our plastic waste is exported to the Far East, where it is reprocessed by workers in unhealthy conditions paid very poor wages; a hidden social and health impact. Until we have a method for weighing up all these factors, poor decisions driven by faith in simplistic ideas such as 'the waste heirarchy' will continue to be made. In the C-VORR project, we will bring together scientists, engineers, mathematicians and economists to help build this method. Working with our industry partners and international experts, we will look at processes that produce waste; not just at the 'end of the pipe' , but upstream and downstream throughout the whole system. We will examine the flows of materials through these systems and see how their 'complex value' - the balance of their economic, social, environmental and health benefits and impacts - changes as we adjust the system. This will allow us to identify the adjustments - perhaps a change in the way a product is made, or a new recycling process, or using the waste from one system as the input to another - that give us the best value overall; not just in terms of money, but also in terms of the effect on our health, happiness and environment. To do this, we will need to combine scientific and engineering methods that measure flows with ways of measuring benefits and impacts, checking how these vary with time and space. We will have to completely redefine value, using unorthodox economic thinking to help us. If we get this right, then we can completely change the way that we look at recovering resource from waste, and instead talk about preventing value from being dissipated into waste in the first place. We will have a tool that will not only let us decide which recovery technology - or change to the process - is best for society and the environment, but that can also identify business opportunities to recover previous hidden value. It will allow us to move away from simplistic ideas about recycling and reprocessing that may have unintended consequences, and give us all a more sophisticated understanding of how to best preserve our scarce resources, our precious environment and the quality of not just our lives but those connected to us; in this globalised world, that's everyone.

The proposed new CCP-WSI+ builds on the impact generated by the Collaborative Computational Project in Wave Structure Interaction (CCP-WSI) and extends it to connect together previously separate communities in computational fluid dynamics (CFD) and computational structural mechanics (CSM). The new CCP-WSI+ collaboration builds on the NWT, will accelerate the development of Fully Coupled Wave Structure Interaction (FCWSI) modelling suitable for dealing with the latest challenges in offshore and coastal engineering. Since being established in 2015, CCP-WSI has provided strategic leadership for the WSI community, and has been successful in generating impact in: Strategy setting, Contributions to knowledge, and Strategic software development and support. The existing CCP-WSI network has identified priorities for WSI code development through industry focus group workshops; it has advanced understanding of the applicability and reliability of WSI through an internationally recognised Blind Test series; and supported collaborative code development. Acceleration of the offshore renewable energy sector and protection of coastal communities are strategic priorities for the UK and involve complex WSI challenges. Designers need computational tools that can deal with complex environmental load conditions and complex structures with confidence in their reliability and appropriate use. Computational tools are essential for design and assessment within these priority areas and there is a need for continued support of their development, appropriate utilisation and implementation to take advantage of recent advances in HPC architecture. Both the CFD and CSM communities have similar challenges in needing computationally efficient code development suitable for simulations of design cases of greater and greater complexity and scale. Many different codes are available commercially and are developed in academia, but there remains considerable uncertainty in the reliability of their use in different applications and of independent qualitative measures of the quality of a simulation. One of the novelties of this CCP is that in addition to considering the interface between fluids and structures from a computational perspective, we propose to bring together the two UK expert communities who are leading developments in those respective fields. The motivation is to develop FCWSI software, which couples the best in class CFD tools with the most recent innovations in computational solid mechanics. Due to the complexity of both fields, this would not be achievable without interdisciplinary collaboration and co-design of FCWSI software. The CCP-WSI+ will bring the CFD and CSM communities together through a series of networking events and industry workshops designed to share good practice and exchange advances across disciplines and to develop the roadmap for the next generation of FCWSI tools. Training and workshops will support the co-creation of code coupling methodologies and libraries to support the range of CFD codes used in an open source environment for community use and to aid parallel implementation. The CCP-WSI+ will carry out a software audit on WSI codes and the data repository and website will be extended and enhanced with database visualisation and archiving to allow for contributions from the expanded community. Code developments will be supported through provision and management of the code repository, user support and training in software engineering and best practice for coupling and parallelisation. By bringing together two communities of researchers who are independently investigating new computational methods for fluids and structures, we believe we will be able to co-design the next generation of FCWSI tools with realism both in the flow physics and the structural response, and in this way, will unlock new complex applications in ocean and coastal engineering