The strategic vision of this Prosperity Partnership for Advanced Simulation and Modelling of Virtual Systems (ASiMoV) is to enable the research and development of the next generation of engineering simulation and modelling techniques. Our aim is to achieve the world's first high fidelity simulation of a complete gas-turbine engine during operation, simultaneously including the effects of thermo-mechanics, electromagnetics, and CFD. This level of simulation will require breakthroughs at all levels, including physical models, numerical solvers, algorithms, software infrastructure, and Exascale HPC hardware. Our partnership uniquely combines fundamental engineering and computational science research with two high tech SMEs and Rolls-Royce plc to address a challenge that is well beyond the capabilities of today's numerical solvers. Simulation and modelling, enabled by high performance computing, have transformed the way products are designed and engineered. The technology developed for the Trent XWB, the world's most efficient aero engine, could only have been achieved through simulation and modelling. However, next generation products will place demands on simulation that cannot be met by incremental changes to current techniques. The ACARE Flightpath 2050 goals demand fundamental changes to engine architectures and the 2015 Aerospace Technology Institute Propulsion Strategy identified "virtual certification" as a key technology needed in the 2025-30 timeframe. The journey to virtual certification is an incremental one requiring a thorough evidential database to convince the certification authorities that the analysis can be trusted. It will move forward on a number of fronts. One of those is the whole engine tests to certify operational performance and thrust. Our driving ambition is to realise new simulation technology for the ultra-high resolution and extreme scale needed for meaningful virtual certification models. For Rolls-Royce, virtual certification will bring a major business transformation requiring unprecedented trust in simulation and fundamental changes to design processes and skills. Estimated cost savings for virtual certification are measured in the many £millions per engine programme; but, we also estimate that each simulation will require a billion core hours. At this scale, savings from computational cost and performance optimisation will be £millions per design study. Hence the need for ASiMoV to push forward the boundaries of numerical modelling and simulation on the next generation of Exascale supercomputers.

Distributed Energy Resources (DERs) are small, modular energy generation and storage units, e.g., wind turbines, photovoltaics, batteries, and electric vehicles, that could be connected directly to the power distribution network. DERs play a critical role in achieving Net Zero. Presently there are over 1 million homes with solar panels in the UK. With the green energy transition well under way in the UK, by 2050 there could be tens of millions of DERs connected to the UK power grid. Although DERs have many benefits, e.g., a reduced carbon footprint and improved energy affordability, they present complex challenges for network operators (e.g., low DER visibility, bi-directional power flow, and voltage anomalies), creating a major barrier to Net Zero. Meanwhile, natural hazards and extreme events are an increasing threat not only to humans but also power grid resilience - a direct impact is the power cuts, e.g., Storms "Dudley", "Eunice" and "Franklin" in February 2022 left over a million homes without electricity. How best to manage millions of DERs is still an open question, especially for improving the grid resilience to natural hazards and extreme events, e.g., storms and heatwaves. This project will develop innovative physics-informed Artificial Intelligence (AI) solutions for enabling Virtual Power Plants (VPP), capable of aggregating and managing many diverse DERs; not only improving decision-making for network operators but also enhancing the grid resilience to natural hazards and extreme events. These could also lead to reduced energy bills for millions of UK energy consumers, less power cuts during extreme events, to greater adoption and more efficient management of DERs, and ultimately to enable rapid progress towards Net Zero.

Society is driving the need for Responsive Manufacturing and requires fundamental research to come-up with strategies that can complement existing Modern Manufacturing Practice (MPP) (e.g. batch, mass and just-in-time). Driver 1 is Big Demand, which concerns the response, volume, variety and location in demand, arising from large-scale events, such as COVID-19, Brexit, Disaster Response, Global Financial Crisis and War, and mass-customisation/bespoke products simply cannot be met by MPP, such as automotive production lines and supply chains, as they have been optimised for particular products. Driver 2 is accommodating dynamic production constraints. COVID-19's measures of social distancing and tiering system as well as trade disputes (Brexit and America vs. China) have shown how quickly MPP can be severed, significantly reducing supply to society. Driver 3 is facilitating manufacturing independence. MMP has enabled large developed nations - America, China, EU, Japan, South Korea, India - to provide production capability that developed smaller (e.g. UK, Switzerland) and developing nations would not have had access to. However, many society's view manufacturing independence as a strategic goal (e.g. Reshoring) especially in light of Drivers 1 & 2 where a nations reliance on other nations' manufacturing capability leaves them vulnerable and without the capability to combat their national needs. Brokered Additive Manufacturing (BAM) will prove that these drivers can be met through a nation's highly distributed and diverse Additive Manufacturing (AM) capability if it can be effectively brokered. BAM brings together world-leading researchers from the Schools of Civil, Mechanical and Aerospace engineering and Business Management, 300+ leaders in the AM industry (GTMA, AMUG, AT 3D Squared) and Model-Based Systems Engineering (CFMS), and industry/government initiatives (Reshoring UK) to create novel brokering of highly distributed and diverse manufacturing systems. BAM's transdisciplinary approach will see the team: 1. profile Big Demand, dynamic production constraints and local, regional, national and global contexts to facilitate independence. 2. develop Business Models and Government Policy. 3. characterise AM capability. 4. create Production System boundary condition models and agent-based models of BAM that simulate both human and machine brokering of jobs at community, regional, national and international scales. BAM solutions will be evaluated through controlled lab experiments, living labs and development of industry demonstrators. The solutions will give rise to a new class of production system that broker highly distributed and diverse manufacturing capability (e.g. AM). This will underpin factories of the future that are not confined to single facilities but are as diverse and distributed as the manufacturing capability they house, revolutionising society's production giving it greater flexibility and responsiveness to meet our future needs.

The performance and strength of a composite aero-structure is established incrementally through a programme of analysis and a series of experimental tests conducted using specimens of varying size and complexity. The process utilises a so-called 'building block' or 'testing pyramid' approach with tests at each of the following levels: (i) Coupon, (ii) Structural detail, (iii) Component, and (iv) Sub-structure or full structure. The 'building block' approach provides a comprehensive and systematic methodology to demonstrate airworthiness and structural integrity, and as such represents the backbone of the certification processes for composite aero-structures. The vast majority of certification tests are conducted at the coupon level, whereas far fewer certification tests are conducted at the subsequent higher pyramid levels. The complexity, cost and time of each test escalates up through the testing pyramid. The underlying assumption is that the material properties derived from tests at the lower levels can be used to define the requirements and design allowables at higher length scales and component complexity. At the mid-pyramid level, the as-manufactured strength of parts is currently assessed by empirical 'manufacturing knockdown factors', and the uncertainties in this assessment, together with uncertain in-service damage, propagate up the pyramid to the full component and structure levels. At best, this leads to conservative, over-constrained design. At worst, there is risk that potentially unsafe scenarios can develop where combinations of weakening events cascade into premature failure. Thus, the very time consuming and expensive testing at the coupon level, produces conservative strain limits with questionable relevance to the strength of large parts or at the full structure level. Also, innovative material and technology developments, which facilitate lightweighting, safer and more damage tolerant composite design, are only relevant at the sub-structure and component levels, and therefore cannot be incorporated into applications because of the current validation practices. Accordingly there is increasing evidence that the building block approach has severe limitations, particularly the high cost of certification, time to market, and the general inability to characterise and predict limit states that may lead to failure at structural scales. There is increasing awareness that, in its current form, the 'building block' approach prevents the innovative use of composites, and consequently that the potential benefits of using advanced composites in terms of lightweighting and efficiency cannot be fully realised under current certification and regulatory procedures. The vision and ambition of the PG are: AMBITION: To enable lighter, more cost and fuel efficient composite aero-structures through developing the scientific foundations for a new approach for integrated high-fidelity structural testing and multi-scale modelling and 3D product quantification based on Bayesian learning and statistical Design of Experiments (DoE), incorporating understanding of design features at structural lengths scales. VISION: To enable more structurally efficient and lightweight airframes that are essential for meeting future fuel and cost efficiency challenges and to maintain and enhance the UK's international position in the aerospace industry. The PG provides a route for lessening regulatory constraints, moving towards a more cost/performance optimised philosophy, by reducing the multiple coupon level tests at the bottom of the test pyramid. Instead structural behaviour will be accounted for in a new culture of virtual design and certification focusing on the higher levels of the testing pyramid. This will promote a change towards virtual testing, enabling reduction of empiricism, significant mass savings, expansion of the design and performance envelopes, and reduction of design costs and associated development time.

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.