Discovering the nature of particle dark matter (DM) is a key priority in Physics - and for STFC. Direct detection of electroweak-scale DM in our galaxy is the primary goal of the XLZD consortium, formed by the coming together of the foremost collaborations in the field: XENON-nT, LUX-ZEPLIN (LZ) and DARWIN. XLZD is proposing a large underground experiment based on the leading liquid xenon (LXe) technology: the definitive search for WIMP DM, able to rule out or discover in the accessible parameter space remaining above the irreducible neutrino background. The scientific potential of such a "rare event observatory" is detailed in a comprehensive white paper signed by 600 authors worldwide. LZ and Xenon-nT are the leading experiments in the field at present. Discovery of DM at XLZD would have profound implications for our understanding of the universe, its birth and its structure. STFC is currently considering a proposal to host the XLZD experiment in a state-of-the-art new facility at the Boulby Underground Laboratory in North Yorkshire (XLZD@Boulby). A liquid xenon rare event observatory might well be the largest project hosted at Boulby, supported by the largest international collaboration visiting the facility, and hence it would drive the facility design to a significant degree. This project will engage UK industry in planning for industrialising the construction of XLZD underground at Boulby, identify cost-effective routes to the supply of the necessary xenon stock, and plan for training of the skilled workforce in the local area that will be required to construct, install and operate the experiment. This project will prepare the way for the major investment in goods, services and people in the local area that will contribute substantially to the levelling-up of the North-East.

Micro-scale defects, ranging from several tens to a few hundreds of micrometers, frequently occur in metallic components, yet there is a lack of inspection equipment capable of identifying them. Failure to address the development of these micro-scale defects can result in their progression into macrocracks, leading to the failure of the components. Detecting micro-scale defects at an early stage enables scheduling of maintenance and timely implementation of measures to extend the lifespan of safety-critical infrastructure components. This is especially crucial for the UK's economy, as the country is planning to construct new generations of nuclear power plants designed to last 50 years or more, which intensifies the need for pre-service and in-service inspections of micro-scale defects. Additionally, the UK has nuclear power plants that are either approaching or have reached the end of their intended lifespan and are being considered for life extension. In all cases, early detection of micro-scale defects is essential to enhance the safety and performance of metallic components. In other power plants and high-value manufacturing, there is a comparable need to detect micro-scale defects. For example new hydrogen plants, the focus is on defects caused by high-temperature hydrogen or oxygen attack in pipes, while in high-value manufacturing, the focus is on detecting interlayer micro-scale defects, such as 100 - 300 micrometers wide pores in the additive manufacturing process, which is critical for quality control purposes. Ultrasound is popularly used to inspect defects as it can propagate inside materials and carry information about their condition. However, currently there is no ultrasonic technique available to directly detect micro-scale defects in metallic components. This is because that the material microstructure generates noise with similar amplitudes to the scattered signals from micro-scale defects, making it challenging to distinguish between them directly. The objective of this project is to create new ultrasonic array techniques that can detect and characterise micro-scale defects in metallic components, which is a long-standing challenge in NDT field. To accomplish this goal, ultrasonic models, signal processing techniques, ultrasound imaging algorithms and inverse modelling methods will be developed to analyse the ultrasonic array data from the material microstructures and micro-scale defects. Three work packages (WPs) have been identified: WP1: Array data generation through developed experimental and modelling protocols. WP2: Development of inverse modelling methods for detecting micro-scale defects. WP3: Validation of the proposed methods through case studies of detecting and characterising micro-scale defects in selected metallic components with industrial collaborators. Through the use of these developed ultrasonic array techniques, this project will unlock new understandings and insights into the characteristics and behavior of micro-scale defects in metallic components. The project involves multiple academic fields, including ultrasonics and NDT, mathematical modelling, finite element analysis, and engineering structural integrity. As an interdisciplinary project, it will have impacts in academia with the contributions to finite element models, statistical models, ultrasound imaging algorithms. Through close collaboration with industrial partners, which are Sellafield, EDF Energy, Hitachi, Rolls-Royce, GKN, Shell, TWI, and Lavender International, the project outcomes will provide new inspection tools for detecting and evaluating micro-scale defects in metallic components, thereby enhancing their safety and performance. The project's outcome will support progress toward net zero and energy sustainability of engineering structures, including design for manufacturing and assembly, and sustainable materials management.

The DigitalMetal CDT is born out to meet a national, strategic need for training a new generation of technical leaders able to lead digital transformation of metals industry & its supply chain with the objective of increasing agility, productivity & international competitiveness of the metals industry in the UK. The metals industry is a vital component of the UK's manufacturing economy and makes a significant contribution to key strategic sectors such as construction, aerospace, automotive, energy, defence and medical, directly contributing £20bn to UK GDP, and underpins over £190bn manufacturing GDP. Without a new cadre of leaders in digital technologies, equipped to transform discoveries and breakthroughs in metals and manufacturing (M&M) technologies into products, the UK risks entering another cycle of world-leading innovation but losing the benefits arising from exploitation to more capable and better prepared global competitors. The evolution to Industry 4.0 and Materials 4.0 coupled with unprecedented opportunities of "big data" enable the uptake of artificial intelligence/deep learning (AI/DL) based solutions, making it feasible to implement zero-defects, right first-time manufacturing/zero-waste (ZDM/ZW) concepts and meet the environmental-, sustainable- and societal- challenges. However, to fully take advantage of these opportunities, two critical challenges must be addressed. First, as user-identified problems in the metals industry that spans domains (from discoveries in M&M to their up-scaling and deployment in high volume/value production), urgently needed a new breed of engineers with skills to traverse these domains by going beyond the classical PhD training, i.e., T-model signifying transferable skills and in-depth knowledge in a single domain, to a new Pi-model raining that is underpinned by transferable skills and in-depth knowledge that transverse across domains i.e.,: AI/DL and engineering (M&M) to enable rapid exploitation of discoveries in M&M. Second, while AI/DL domain provides data-driven correlation analysis critical for product performance and defect identification, it is insufficient for root cause analysis (causality). This necessitates training on integrating data-driven with physics-based models of product & production, which is currently lacking in the metals industry. The Midlands region, as the top contributor to UK Gross Value Added through metals and metal products, with world-leading companies, such as Rolls-Royce and Constellium, LEAR and their customers, underpinned through collaborations with the five Midlands universities: Birmingham, Leicester, Loughborough, Nottingham & Warwick, is uniquely positioned to integrate research and industry resources and train a new cadre of engineers & researchers on the Pi-model to address user-needs. Our vision is to train future leaders able to accelerate the exploitation of M&M discoveries using digital technology to enable defect-free, right first-time manufacturing at reduced costs, digitise to decarbonise, and implement fuel switching in metals manufacturing industry.

Meeting emerging science and engineering modelling challenges requires scientists who can master complex theory and simulation techniques, can assimilate data, and can collaborate in multidisciplinary teams with expertise across a range of modelling scales. Securing the UK's position as a world-leading research hub into the future therefore requires a well-integrated pool of researchers with a skillset that is both broad and deep. HetSys is leading the way in addressing these needs by producing students with the tools necessary to meet the challenges of the future through our training programme. We are training the scientists who will develop the next generation of computational models, implemented in reusable software with robust error bars from uncertainty quantification (UQ), and who can learn from experimental and simulated data on an equal footing through advances in 'scientific machine-learning' (SciML). Linking heterogeneous materials models with UQ allows performance to be improved, enabling the technology needed to reach net zero through a step-change in design capability. The ongoing AI revolution has necessitated a redesign of our training programme to enable us to build on what we learnt during the first funding period and deliver our new vision. In particular, changes to our core training enable our students to (i) embed robust and sustainable research software engineering (RSE) in modelling; (ii) quantify modelling uncertainties through enhanced use of statistical methods; and (iii) exploit new trends in scientific machine learning. The research focus of HetSys on new paradigms in the behaviour of heterogeneous materials remains vital for the competitiveness of the UK's high-value manufacturing and automotive industries. Prominent examples of challenges we are addressing include the design of (i) energy materials for future vehicles with reduced carbon footprints; (ii) low dimensional and/or strongly correlated materials for quantum devices; (iii) high entropy alloys for fusion applications; (iv) biomolecules for combatting infectious diseases. Historically, the modelling pattern has focused on just one length- or time-scale; HetSys transforms this landscape by explicitly targeting the multiscale modelling of heterogeneous systems required by industry. The expertise we have accumulated opens up opportunities to capitalise on the transformative combination of mechanistic modelling with data-driven approaches (SciML). This requires a broader combination of disciplinary expertise, provided through our enhanced bespoke training programme. Only a cohort approach can train high-quality computational scientists who can develop and implement new modelling methods in close collaboration with other scientists. The cohesive, interdepartmental cohorts and training programme we are creating lower many of the current barriers to interdisciplinary work and demonstrate our vision for the future of scientific endeavour, where teams of researchers work together to combine their skills and expertise. Only a critical mass of students and a large and highly collaborative team of supervisors makes this targeted and fully inclusive training approach feasible. HetSys supports the delivery of EPSRC's Physical and Mathematical Sciences Powerhouse strategic priority, helping to provide the platform on which research and innovation across the sciences is built.