The presence of walls alters the thermo-chemical and fluid-dynamical processes associated with turbulent premixed flames. The increasing demands for light-weight combustors make flame-wall interactions (FWI) inevitable, which influence the cooling load, thermal efficiency and pollutant emission in these applications. However, this aspect has not yet been sufficiently analysed in the existing turbulent reacting flow literature because of the challenge this poses for both experimental and numerical investigations in terms of spatial and temporal resolutions among others. Therefore, a thorough physical understanding of the FWI mechanism is necessary to develop and design more energy-efficient and environmentally-friendly combustion devices. In this project, recent advances of both high-performance computing and experimental techniques will be utilised to analyse and model premixed FWI in turbulent boundary layers (TBLs). The proposed analysis will consider different FWI configurations (based on the orientation of the mean flame normal with respect to the wall) in turbulent channel flows and unconfined boundary layers (BLs) using state-of-the-art experiments and high-fidelity Direct Numerical Simulations for different wall boundary conditions. Experiments will utilize a suite of advanced laser diagnostics, providing new simultaneous measurement capabilities. DNS will simulate the turbulent flow without any recourse to physical approximations. The fundamental physical insights obtained from DNS and experimental data will be used to develop a novel hybrid RANS/LES approach for device-scale simulation of FWI, building on expertise in the context of Flame Surface Density (FSD) and Scalar Dissipation Rate (SDR) closures for Reynolds Averaged Navier Stokes (RANS) and Large Eddy Simulations (LES). The newly-developed models will be implemented to carry out hybrid RANS/LES of experimental configurations for the purpose of model validation. The project will offer robust and cost-effective Computational Fluid Dynamics (CFD) design tools for fuel-efficient and low-emission combustion devices (e.g. gas turbines, micro-combustors and automotive engines).

There are increasing concerns about the safety and security of critical infrastructure such as nuclear power plants, the electricity grid and other utilities in the face of possible cyber attacks. As ageing controllers are replaced by smart devices based on Field-Programmable Gate Arrays (FPGAs) and embedded microprocessors, the safety of such devices raises many concerns. In particular, there is the very real risk of malicious functionality hidden in the silicon or in software binaries, dormant and waiting to be activated. Current hardware and software systems are of such complexity that it is impossible to discover such malicious code through testing. We aim to address this problem by closely connecting the system design specification with the actual implementation through the use of a formal design methodology based on type systems with static and dynamic type checking. The type system will be used as a formal language to encode the design specification so that the actual implementation will automatically be checked against the specification. Static type checking of data types and multiparty session types can ensure the correctness of the interaction between the components. However, as static checking assume full access to the design source code it cannot be used to protect against potential threads issuing from third-party functional blocks (know as ``Intellectual Property Cores'' or IP cores) that are commonly used in hardware design: the provider of the IP core can claim adherence to the types and protocols, so that the IP core will meet the compile-time requirements, but the run-time the behaviour cannot be controlled using static techniques. The same applies to third-party compiled software libraries. Therefore we propose to use run-time checking of data types as well as session types at the boundaries of untrusted modules ("Border Patrol"), so that any intentional or unintentional breach of the specification will safely be intercepted.

We will develop a data science of the natural environment, deploying modern machine learning and statistical techniques to enable better-informed decision-making as our climate changes. While an explosion in data science research has fuelled enormous advances in areas as diverse as eCommerce and marketing, smart cities, logistics and transport, health and wellbeing, these tools have yet to be fully deployed in one of the most pressing problems facing humanity, that of mitigating and adapting to climate change. This project brings together world-leading statisticians, computer scientists and environmental scientists alongside an extensive array of key public and private stakeholder organisations to effect a step change in data culture in the environmental sciences. The project will develop a new approach to data science of the natural environment driven by three representative grand challenges of environmental science: predicting ice sheet melt, modelling and mitigating poor air quality, and managing land use for maximal societal benefit. In each motivational challenge, there is already an extensive scientific expertise, with intricate models of processes at multiple scales. However this sophisticated modelling of system components is usually let down by naive integration of these components together, and inadequate calibration to observed data. The consequence is poor predictions with a high level of uncertainty and hence poorly-informed policy making. As new forms of environmental data become available, and the pressures on our natural environment from climate change increase, this gap is becoming a pressing concern, and we bring an impressive team to bear on the problem. A key theme of the project is integration, developing a suite of novel data science tools which work together in a modular fashion, and with existing scientifically-informed process models. By building a team that spans the inter-disciplinary divisions between data and environmental scientists we can ensure the necessary interoperability of methods that is currently lacking. Working with the full range of stakeholder environmental organisations will enable continual co-design of the programme and training of end-user scientists to ensure a reduction of the skills gap in this area. The resultant culture shift in the data literacy of the environmental sciences will enable better decision-making as climate change places ever greater strains on our society.

The sudden 'en masse' appearance of jellyfish has serious consequences for coastal power stations through biofouling of cooling water systems. The reduction in water flow caused by jellyfish has forced power plants to run at reduced efficiency or temporarily shut down as a precautionary measure to prevent overheating, which impacts the provision of electricity to customers at a significant financial cost to the electricity supplier. A persistent difficulty lies in identifying the origin of blooms and when they will appear at a coastal facility and water intake. The main project partner for this proposal is EDF (nuclear), however the methodology is intentionally generic to allow adaptation to other sensitive coastal sites and therefore our project has the support of both SSE (gas energy) and SSPO (Scottish Salmon Producers Organisation). The aim of this proposal is to provide a robust tool for rapid evaluation of the likelihood and scale of jellyfish ingress at EDF's Torness Nuclear Power Station based on simulated patterns of historic bloom dispersal within the North Sea from the last 20 years. To achieve this we will translate our previously NERC-funded research with the state-of-the-art marine Connectivity Modelling System to simulate dispersal of individuals within blooms incorporating specific biological behaviours of jellyfish (e.g. vertical migration, rough surface conditions avoidance and buoyancy-related effects of aging). We have two objectives which will be completed within 18 months at a cost of £161,618 (80% fEC): (1) to provide gridded maps, specific to the time of the year or oceanographic conditions, giving the probability of jellyfish arriving at Torness, as well as minimum and peak arrival times, for blooms arising at any given source location within the North Sea. (2) to test the suitability of the tool for providing an early warning of potential ingress threat from jellyfish blooms, including validation with historic and satellite-based observational data. This tool will allow rapid risk evaluation and inform operational response by EDF when a jellyfish bloom is located in the future or during specific weather events. The tool will also identify critical locations in the North Sea where ongoing monitoring is essential for an early warning system for Torness Nuclear Facility.

Lancaster University (LU) proposes a Centre for Doctoral Training (CDT) to develop international research leaders in statistics and operational research (STOR) through a programme in which cutting-edge industrial challenge is the catalyst for methodological advance. Our proposal addresses the priority area 'Statistics for the 21st Century' through research training in cutting-edge modelling and inference for large, complex and novel data structures. It crucially recognises that many contemporary challenges in statistics, including those arising from industry, also engage with constraint, optimisation and decision. The proposal brings together LU's academic strength in STOR (>50FTE) with a distinguished array of highly committed industrial and international academic partners. Our shared vision is a CDT that produces graduates capable of the highest quality research with impact and equipped with an array of leadership and other skills needed for rapid career progression in academia or industry. The proposal builds on the strengths of an existing EPSRC-funded CDT that has helped change the culture in doctoral training in STOR through an unprecedented level of engagement with industry. The proposal takes the scale and scientific ambition of the Centre to a new level by: * Recruiting and training 70 students, across 5 cohorts, within a programme drawing on industrial challenge as the catalyst for research of the highest quality; * Ensuring all students undertake research in partnership with industry: 80% will work on doctoral projects jointly supervised and co-funded by industry; all others will undertake industrial research internships; * Promoting a culture of reproducible research under the mentorship and guidance of a dedicated Research Software Engineer (industry funded); * Developing cross-cohort research-clusters to support collaboration on ambitious challenges related to major research programmes; * Enabling students to participate in flagship research activities at LU and our international academic partners. The substantial growth in data-driven business and industrial decision-making in recent years has signalled a step change in the demand for doctoral-level STOR expertise and has opened the skills gap further. The current CDT has shown that a cohort-based, industrially engaged programme attracts a diverse range of the very ablest mathematically trained students. Without STOR-i, many of these students would not have considered doctoral study in STOR. We believe that the new CDT will continue to play a pivotal role in meeting the skills gap. Our training programme is designed to do more than solve a numbers problem. There is an issue of quality as much as there is one of quantity. Our goal is to develop research leaders who can innovate responsibly and secure impact for their work across academic, scientific and industrial boundaries; who can work alongside others with different skills-sets and communicate effectively. An integral component of this is our championing of ED&I. Our external partners are strongly motivated to join us in achieving these outcomes through STOR-i's cohort-based programme. We have little doubt that our graduates will be in great demand across a wide range of sectors, both industrial and academic. Industry will play a key role in the CDT. Our partners are helping to co-design the programme and will (i) co-fund and co-supervise doctoral projects, (ii) lead a programme of industrial problem-solving days and (iii) play a major role in leadership development and a range of bespoke training. The CDT benefits from the substantial support of 10 new partners (including Morgan Stanley, ONS Data Science Campus, Rolls Royce, Royal Mail, Tesco) and continued support from 5 existing partners (including ATASS, BT, NAG, Shell), with many others expected to contribute.