This project will deliver new computational modelling tools that will allow engineers working onsafety critical structures to rationally assess the effects of crack initiation and crack propagation. Suchproblems have to date remained intractable. The research will permit unprecedented understanding of crackpropagation, thereby delivering less conservative designs, and, most importantly avoid unpredictedcatastrophic failures in service. This is possible by building upon the recent success of the extended finite elementmethod (XFEM), which has emerged as a revolutionary simulation tool for modelling discontinuities and has the potential to require an order of magnitude less engineering time than conventional methods.Yet, this new method requires much reliability improvements to invade industry. By leveraging recent theoreticaland numerical developments and working hand-in-hand with future users, this project has the potential toprovide XFEM with the accuracy and robustness it requires to become the new tool of choice for structuralintegrity predictions and reconcile accuracy and computational tractability.Cracks or defects are almost always present in engineering structures. In aerospace engineering for instance, during the life of the aircraft (take offs, flights and landings), these cracks will grow under the influence of the forces applied to the structure. How do engineers ensure that, despite these growing cracks, the aircraft can still be operated safely? The idea is to regularly inspect the aircraft to monitor the major cracks. The next question is to know how often should an aircraft be inspected to prevent catastrophic failure between two inspections. To answer this question, engineers must be able to evaluate the time (number of flights) it takes for the cracks to become fatal to the structure. If it takes 1,000 flights, the maximum inspection interval should be less than 1,000. To estimate the time to failure, engineers use computer methods, where they model the behaviour of the structure using various simplifications: this is known as Damage Tolerance Analysis (DTA).However, today, existing software are still unable to provide engineers with a rational tool to assess the tolerance of a structure to damage. The proposed research has the long-term goal to provide this tool which could provide a paradigm shift in the way engineers think about simulating fracture, whereby sufficient accuracy would not be synonymous with intractable computational time or manpower.

The nuclear industry has some of the most extreme environments in the world, with radiation levels and other hazards frequently restricting human access to facilities. Even when human entry is possible, the risks can be significant and very low levels of productivity. To date, robotic systems have had limited impact on the nuclear industry, but it is clear that they offer considerable opportunities for improved productivity and significantly reduced human risk. The nuclear industry has a vast array of highly complex and diverse challenges that span the entire industry: decommissioning and waste management, Plant Life Extension (PLEX), Nuclear New Build (NNB), small modular reactors (SMRs) and fusion. Whilst the challenges across the nuclear industry are varied, they share many similarities that relate to the extreme conditions that are present. Vitally these similarities also translate across into other environments, such as space, oil and gas and mining, all of which, for example, have challenges associated with radiation (high energy cosmic rays in space and the presence of naturally occurring radioactive materials (NORM) in mining and oil and gas). Major hazards associated with the nuclear industry include radiation; storage media (for example water, air, vacuum); lack of utilities (such as lighting, power or communications); restricted access; unstructured environments. These hazards mean that some challenges are currently intractable in the absence of solutions that will rely on future capabilities in Robotics and Artificial Intelligence (RAI). Reliable robotic systems are not just essential for future operations in the nuclear industry, but they also offer the potential to transform the industry globally. In decommissioning, robots will be required to characterise facilities (e.g. map dose rates, generate topographical maps and identify materials), inspect vessels and infrastructure, move, manipulate, cut, sort and segregate waste and assist operations staff. To support the life extension of existing nuclear power plants, robotic systems will be required to inspect and assess the integrity and condition of equipment and facilities and might even be used to implement urgent repairs in hard to reach areas of the plant. Similar systems will be required in NNB, fusion reactors and SMRs. Furthermore, it is essential that past mistakes in the design of nuclear facilities, which makes the deployment of robotic systems highly challenging, do not perpetuate into future builds. Even newly constructed facilities such as CERN, which now has many areas that are inaccessible to humans because of high radioactive dose rates, has been designed for human, rather than robotic intervention. Another major challenge that RAIN will grapple with is the use of digital technologies within the nuclear sector. Virtual and Augmented Reality, AI and machine learning have arrived but the nuclear sector is poorly positioned to understand and use these rapidly emerging technologies. RAIN will deliver the necessary step changes in fundamental robotics science and establish the pathways to impact that will enable the creation of a research and innovation ecosystem with the capability to lead the world in nuclear robotics. While our centre of gravity is around nuclear we have a keen focus on applications and exploitation in a much wider range of challenging environments.

Recent years have seen increasing interest in the use of thick-section composites for safety-critical components in, for example, primary aircraft structure and fan blades in aero engines. All such components are required to undergo non-destructive evaluation (NDE) during manufacture; this is time consuming and NDE throughput is stretched to its limit internationally. Current composite Non-destructive Evaluation (NDE) is based on a qualitative empirical approach where a single normal-incidence ultrasonic probe is used to estimate the average ultrasonic attenuation from the amplitude of the back-wall reflection. While adequate for accepting or rejecting thin composite panels, this approach does not provide the level of defect characterisation and localisation necessary for the quantitative NDE of larger components.There is a clear and pressing industrial need for quantitative NDE techniques that can be applied to safety-critical composite components both at manufacture and in-service. An ultrasonic technique is the industrially preferred option for reasons of cost, safety and ease of deployment, but increased scanning speeds are required to speed up throughput. However, the conflicting demands of rapid scanning, high-penetration depth and accurate defect characterisation cannot be achieved with a single normal-incidence probe. Instead the data from multiple inspection directions must be combined. The necessary raw data can be rapidly and efficiently obtained using an ultrasonic array, but at present it cannot be exploited. This is due to the lack of (a) an appropriate forward model of oblique wave propagation and scattering processes, and (b) a suitable inversion scheme to turn the raw data into useful information. This is the motivation for the proposed research programme, the aim of which is to develop ultrasonic array data processing techniques based on physical reasoning for the characterisation of safety-critical aerospace composites. The programme requires advancement of the fundamental science of wave phenomena in composites, the solution of a challenging inverse problem and, crucially, the translation of the scientific findings into practical industrial solutions.

Composite materials are becoming increasingly important for light-weight solutions in the transport and energy sectors. Reduced structural weight, with improved mechanical performance is essential to achieve aerospace and automotive's sustainability objectives, through reduced fuel-burn, as well as facilitating new technologies such as electric and hydrogen fuels. The nature of fibre reinforced composite materials however makes them highly susceptible to variation during the different stages of their manufacture. This can result in significant reductions in their mechanical performance and design tolerances not being met, reducing their weight saving advantages through requiring "over design". Modelling methods able to simulate the different processes involved in composite manufacture offer a powerful tool to help mitigate these issues early in the design stage. A major challenge in achieving good simulations is to consider the variability, inherent to both the material and the manufacturing processes, so that the statistical spread of possible outcomes is considered rather than a single deterministic result. To achieve this, a probabilistic modelling framework is required, which necessitates rapid numerical tools for modelling each step in the composite manufacturing process. Focussing specifically on textile composites, this project will develop a new bespoke solver, with methods to simulate preform creation, preform deposition and finally, preform compaction, three key steps of the composite manufacturing process. Aided by new and developing processor architectures, this bespoke solver will deliver a uniquely fast, yet accurate simulation capability. The methods developed for each process will be interrogated through systematic probabilistic sensitivity analyses to reduce their complexity while retaining their predictive capability. The aim being to find a balance between predictive capability and run-time efficiency. This will ultimately provide a tool that is numerically efficient enough to run sufficient iterations to capture the significant stochastic variation present in each of the textile composite manufacturing processes, even at large, component scale. The framework will then be applied to industrially relevant problems. Accounting for real-world variability, the tools will be used to optimise the processes for use in design and to further to explore the optimising of manufacturing processes. Close collaboration with the project's industrial partners and access to their demonstrator and production manufacturing data will ensure that the tools created are industry relevant and can be integrated within current design processes to achieve immediate impact. This will enable a step change in manufacturing engineers' ability to reach an acceptable solution with significantly fewer trials, less waste and faster time to market, contributing to the digital revolution that is now taking place in industry.

The ultimate ambition of the proposed research programme is reduced environmental impact of aviation and power generating gas turbine engines. Serious emissions reduction can only come from better understanding and modelling of the combustion and emissions generation processes and the roles of different fuels. Several disruptive chemical and particulate species measurement methods will be developed for detailed combustion zone and exhaust characterisation. These transformational new measurement capabilities will be applied to establishing, for the first, time the spatial and temporal evolution of combustion species and unwanted emissions within the engines. Such measurements will inform new understanding of the combustion and emissions generation processes and enable new technical strategies to ultimately deliver improved engine and fuel technologies for reduced emissions.