The scientific discipline of fluid dynamics is primarily concerned with the measurement, modelling and underlying physics and mathematics of how liquids and gases behave. Almost all natural and manufactured systems involve the flow of fluids, which are often complex. Consequently, an understanding of fluid dynamics is integral to addressing major societal challenges including industrial competitiveness, environmental resilience, the transition to net-zero and improvements to health and healthcare. Fluid dynamics is essential to the transition of the energy sector to a low-carbon future (for example, fluid dynamics simulations coupled with control algorithms can significantly increase wind farm efficiency). It is vital to our understanding and mitigation of climate change, including extreme weather events (for example in designing flood mitigation schemes). It is key to the digitisation of manufacturing through 3d printing/additive manufacturing and development of new greener processing technologies. In healthcare, computational fluid dynamics in combination with MRI scanning provides individualised modelling of the cardio-vascular system enabling implants such as stents to be designed and tested on computers. Fluid dynamics also shows how to design urban environments and ventilate buildings to prevent the build-up of pollutants and the transmission of pathogens. The UK has long been a world-leader in fluid dynamics research. However, the field is now advancing rapidly in response to the demand to address more complex and interwoven problems on ever-faster timescales. Data-driven fluid dynamics is a major area where there are rapid advances, with the increasing application of data-science and machine learning techniques to fluid flow data, as well as the use of Artificial Intelligence to accelerate computational simulations. For the UK to maintain its competitive position requires an investment in training the next generation of research leaders who have experience of developing and applying these new techniques and approaches to fluids problems, along with professional and problem-solving skills to lead the successful adoption of these approaches in industry and research. The University of Leeds is distinctive through the breadth, depth and unified structure of its fluid dynamics research, coordinated through the Leeds Institute for Fluid Dynamics (LIFD), making it an ideal host for this CDT. The CDT in Future Fluid Dynamics (FFD-CDT) will build on the experience of successfully running a CDT in Fluid Dynamics to address these new and exciting needs. Our students will carry out cutting-edge research developing new fluid dynamics approaches and applying them across a diverse range of engineering, physics, computing, environmental and physiological challenges. We will recruit and train cohorts of students with diverse backgrounds, covering engineering, mathematical, physical and environmental sciences, in both the fundamental principles of fluid dynamics and new data-driven methodologies. Alongside this technical training we will provide a team-based, problem-led programme of professional skills training co-developed with industry to equip our graduates with the leadership, team-working and entrepreneurial skills that they need to succeed in their future careers. We will build an inclusive, diverse and welcoming community that supports cross-disciplinary science and effective and productive collaborations and partnerships. Our CDT cohort will be at the heart of growing this capability, integrated with and within the Leeds Institute for Fluid Dynamics to deliver a dynamic and vibrant training and research environment with strong UK and international partnerships in academia, industry, policy and outreach.

Developing scientific software, for example for climate modeling or medical research, is a highly challenging task. Domain scientists are often deeply involved in low-level programming details just to make their code run sufficiently fast. These tedious, but important, optimization steps significantly reduce the productivity of scientists. Domain specific languages (DSLs) revolutionize the productivity of domain scientists by enabling them to focus on scientific questions rather than making their code run fast. Sophisticated DSL compilers automatically generate high-performance code from domain-specific high-level problem descriptions. While there are individual successes, the existing landscape of DSLs is scattered and the reuse of software components in DSL compiler implementations is limited as traditionally DSL compilers are built in isolation. This results in high development costs of new DSLs and prevents many DSLs from ever achieving a level of maturity and sustainability that enables uptake by the scientific community. This project revolutionizes the design of DSL compiler implementations by leveraging the breadth and cross-industry support of the MLIR compiler and Python ecosystems. Python is the tool of choice for application developers in many domains, such as machine learning, data science, and - we believe - an important component of the future of High Performance Computing software. This project establishes MLIR as a common representation for code at multiple levels of abstraction in DSL compiler development. DSLs embedded in various host languages, including Python and Fortran, will be easily built on top of MLIR. Instead of building DSL compilers as isolated monolithic towers, our research will build a toolbox that enables developers to build DSLs using a rich ecosystem of shared intermediate representations IRs and optimizations. This project evaluates, drives, and demonstrates the DSL design toolbox to build the next generation of DSLs for Seismic and Climate Modelling as well as Medical imaging. These will share common software components and make them available for other DSLs. An extensive evaluation will show the scalability of DSL software towards exascale. Finally, this project investigates how future disruptors, including artificial intelligence, data science, and on-demand HPC-as-a-service, will shape and influence the next generations of high performance software. This project will work towards deeply integrating modern interactive data analytics and machine learning methods from the Python ecosystem with high-performance scientific code.

Global climate change threatens our future. Urgent societal action is demanded. However, crucial uncertainties regarding the future climate still need to be addressed. Extreme climate events are wreaking enormous environmental, societal, and economic tolls and they are becoming increasingly common and intense. The huge number of uncertainties related to our future climate combine with the sensitivity of the Earth's climate system to create extremely demanding challenges. Extending our understanding for deriving effective solutions demands interdisciplinary collaboration to determine the dominant factors in climate change. Currently, there is a lack of highly qualified mathematicians with the necessary training and experience to address the diverse problems and urgent challenges posed by climate change using computational and data-driven research. Our Centre for Doctoral Training (CDT) will train new cohorts of PhD students and build a scientific community to address the grand mathematical challenges raised by the significant levels of uncertainty in our future climate. The mission of our CDT will be to prepare graduates with strong mathematics, physics and engineering backgrounds to apply their skills in mathematical modelling, scientific computing, statistics and machine learning to key climate-related problems in oceanic, atmospheric and engineering contexts. By bringing together leading experts from Imperial College London, the University of Reading and the University of Southampton along with a wide range of external partners, our CDT will be uniquely placed to equip future mathematicians with the tools required to address global climate uncertainties. Our CDT will achieve its goals by developing the mathematics and its applications that are required to understand, better predict and, ultimately, respond to impending changes in the Earth's climate and the associated risks. A particular emphasis will be the creation of a vibrant environment to integrate strong cross-disciplinary engagement and collaboration, both within and between cohorts and disciplines, in advancing the range of scientific techniques, fundamental theories, approaches and applications. This will include engaging with academics, government organisations, industry and the public. As a result, the development of outstanding skills in mathematics and science communication will be a priority. The collaborative and peer-to-peer interactions will help develop the complementary techniques and approaches that will underpin essential technical research and innovation and will be coupled with exciting opportunities to discover and advance fundamental mathematics to provide practical solutions in climate science and beyond. Our CDT will act as a seed for growing the capability and capacity to inform decisions and efforts related to climate change on a rapid timescale. The technical focus of our CDT will be enhanced by activities to appreciate the social, political and economic dimensions of societal response to climate change. Furthermore, sustained efforts to mitigate and adapt to climate change will be required during the coming decades. For this reason, along with building a professional community of graduates, the CDT will invest in imaginative outreach programmes involving school pupils and undergraduates, building on opportunities through the institutions partnering with the CDT, including the Grantham Institute for Climate Change and the Environment, the National Oceanography Centre, the National Centre for Earth Observations, the UK Meteorological Office, the European Centre for Medium-Range Weather Forecasts, and the Natural History Museum.

If CO2 emissions continue to rise, climate change will adversely affect global food and water availability, ecosystems, cities, and coastal communities. While reduction of fossil fuels will be an essential step for reducing atmospheric CO2, Negative Emission Technologies (NETs) can help meet emission targets. During combustion, CO2 can be extracted, transported, and stored in geologic repositories - this is the process of Carbon Capture and Storage (CCS). Combining bioenergy with CCS (BECCS) could result in negative emissions of CO2. BECCS is attractive since it results in a net removal of CO2 from the atmosphere while also providing a renewable source of energy. However, BECCS requires a large commitment of land and will have impacts on food and water availability. This work focuses on BECCS and addresses the challenges for planning a global and nationwide distribution of bioenergy crops. The vast majority of IPCC scenarios that remain below 2 degrees C makes use of NET in the 21st century. Although bioenergy crops and BECCS are an essential component of the scenarios (produced by Integrated Assessment Models, or IAMs), the crops in even the most sophisticated IAMs only respond to mean changes in climate. This results in an inconsistency in the modelling framework: the IAMs can assume bioenergy crops are effective at drawing down CO2 and producing energy in a region where actually climate change will reduce their effectiveness. Earth System Models (ESMs) represent the dynamics of the atmosphere, oceans, sea ice, and land surface. They can account for biophysical (i.e. changes to albedo and latent heat fluxes) and biogeochemical (i.e. uptake or release of greenhouse gases) feedbacks due to land use change. They are the only tool available to investigate future impacts of spatial and temporal variability in climate on the food, energy, and water nexus. However, the ESMs used in the last IPCC report only accounted for a generic crop type at best, not differentiating between bioenergy and food crops. Without an explicit representation of bioenergy crops in ESMs, the effects of climate change do not feedback to affect the food, energy, and water resources assumed to be true in the IAMs. There is an urgent need for predicting the productivity of bioenergy crops in a coupled climate simulation, to see the impact of a range of climate change on the productivity, and associated impacts on food crop productivity, energy production, and water availability. In this project, I will include representations of first and second generation bioenergy crops in the UK ESM, and investigate the impacts of climate change on the productivity at the global and regional (for the UK) level. This work will assess the viability of negative emissions of CO2 through bioenergy crops as an effective climate mitigation strategy under a changing climate, and provide data to support decisions that will minimize the impacts of both climate change and climate change mitigation on bioenergy production, food, and water availability. The outcomes of this project will enhance the resilience of the food/water/energy nexus to climate change and climate variability through better planning, and providing socially responsible recommendations for balancing the challenges of reducing climate change with feeding our growing global population.

Advances in artificial intelligence (AI) are revolutionising how we search for information. Large language models (LLMs), such as OpenAI's 'Chat-GPT' or Google's 'Bard', are good at understanding what we say and the meaning behind our words. Through conversations with these tools, they are helping to improve the accuracy of what information we want to find. While existing search tools focus on using 'keywords', this may not always give good answers. LLMs help people who might not know the exact words to say, because they know the context and relationships behind our language. They can adapt to different ways of asking questions, as well as provide explanations about why they found such information. We believe that these maturing technologies can help researchers search for data. Through training existing LLMs to learn what UKRI-supported research data exist, we can make the most of their existing abilities to understand human language to create a powerful data search tool. Their potential to be used as a data search tool is unknown and we are not aware of any existing tools for UK research datasets. Our proposal will develop, pilot and evaluate the effectiveness of LLMs to this end. The main output of this work will be a fully deployable 'chat box' search tool that researchers will be able to use to discover research datasets. To achieve this, we will collate the metadata of data catalogues across a range of UKRI research investments including the Consumer Data Research Centre, NERC Environmental Data Service, Administrative Data Research UK and UK Data Service. Through combining data catalogues across these unconnected services, we provide a new single 'port of call' for searching research data. We will design our project so that it can easily adapt to integrate new datasets. These data will then be used to develop a new AI derived search tool based on LLMs. We want to understand how these technologies can be used effectively by researchers and whether they will give more useful searches. Our mixed methods approach will test and evaluate the acceptability, suitability, and performance of our new search tool in comparison to existing UKRI search tools. This will include focus groups to qualitatively examine the acceptability of LLMs for data discovery, a quantitative comparison of how our new tool performs against existing keyword search tools, and by running tests that task participants with searching for data. We will report the strengths and limitations of LLMs to examine how useful they are. We will make recommendations for how they can be deployed, refined and sustain the changing ways in how researchers search for data. Our project will bring added value to existing UKRI data discovery resources through creating a new tool that will know the context and meaning of search queries, providing a broader and more accurate list of datasets based on what is searched for. We hope that this will help researchers to find exactly the data they need for their research.