Every day, society is demanding more efficient, safer, affordable and environmental friendly products. To do that, industry needs to innovate by developing new methods and tools able to obtain superior performances of their competitors. This is the main topic of SSeCoID´s research: to develop new methods and tools for design in order to enhance their industrial performance. SSeCoID will demonstrate its innovations by applying the new methods & tools to different problems of aeronautics and mechanical engineering, and will provide very specialized training to 15 researchers to make these novel technologies available to relevant industrial sectors. SSeCoID proposes to work in the: Development of new numerical methods and tools better suited for unsteady flows: stability analysis and high fidelity simulation, capable of capturing and controlling the growth of small perturbations in stable flows and able to provide enhanced accuracy. Identification of the features causing unsteadiness, acoustic, flow detachment or lack of performance of current aerodynamic configurations; Investigation of control/suppression of unsteady flow through flow control devices or surface modifications, by identifying the most suitable zones that have most influence on the flow features; Multidisciplinary evaluation of the most promising applications in relevant problems proposed by industry, including detached flows, acoustic feedback, flow induced vibration, inkjet or race car design; assessment of technical feasibility; potential of control devices for flow instability suppression or delay. Training 15 new researchers in the development of most advanced methods for simulation, feature detection, stability and flow control techniques applied to industrial design. Disseminate the project results in international forums and non-specialized public. Exploit the project results in new patents that include new designs and methods more stable and easy to control.

The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.

Our goal is to create a world-class Centre for Doctoral Training (CDT) in fluid dynamics. The CDT will be a partnership between the Departments of Aeronautics, Bioengineering, Chemical Engineering, Civil Engineering, Earth Science and Engineering, Mathematics, and Mechanical Engineering. The CDT's uniqueness stems from training students in a broad, cross-disciplinary range of areas, supporting three key pillars where Imperial is leading internationally and in the UK: aerodynamics, micro-flows, and fluid-surface interactions, with emphasis on multi-scale physics and on connections among them, allowing the students to understand the commonalities underlying disparate phenomena and to exploit them in their research on emerging and novel technologies. The CDT's training will integrate theoretical, experimental and computational approaches as well as mathematical and modelling skills and will engage with a wide range of industrial partners who will contribute to the training, the research and the outreach. A central aspect of the training will focus on the different phenomena and techniques across scales and their inter-relations. Aerodynamics and fluid dynamics are CDT priority areas classified as "Maintain" in the Shaping Capabilities landscape. They are of key importance to the UK economy (see 'Impact Summary in the Je-S form') and there currently is a high demand for, but a real dearth of, doctoral-level researchers with sufficient fundamental understanding of the multi-scale nature of fluid flows, and with numerical, experimental, and professional skills that can immediately be used within various industrial settings. Our CDT will address these urgent training needs through a broad exposure to the multi-faceted nature of the aerodynamics and fluid mechanics disciplines; formal training in research methodology; close interaction with industry; training in transferable skills; a tight management structure (with an external advisory board, and quality-assurance procedures based on a monitoring framework and performance indicators); and public engagement activities. The proposed CDT aligns perfectly with Imperial's research strategy and vision and has its full support. The CDT will leverage the research excellence of the 60 participating academics across Imperial, demonstrated by a high proportion of internationally-leading researchers (among whom are 15 FREng, and, 4 FRS), 5*-rated (RAE) departments, and a fluid dynamics research income of 93M pounds sinde 2008 (with about 32% from industry) including a number of EPSRC-funded Programme Grants in fluid dynamics (less than 4 or 5 in the UK) and a number of ERC Advanced Investigator Grants in fluid dynamics (less than about 7 across Europe). The CDT will also leverage our existing world-class training infra-structure, featuring numerous pre-doctoral training programmes, high-performance computing and laboratory facilities, fluid dynamic-specific seminar series, and our outstanding track-record in training doctoral students and in graduate employability. The Faculty of Engineering has also committed to the development of bespoke dedicated space which is important for cohort-building activities, and the establishment of a fluids network to strengthen inter-departmental collaborations for the benefit of the CDT.

Our team specialises in the development of finite element methods to computationally simulate fluid flow, particularly low Mach number, transient, separated fluid flows in complex geometries and in the presence of strong multiphysics coupling. These models can be used to make predictions and answer scientific questions in problems ranging from blood flow through an arterial bypass graft to the flow over components of a Formula 1 racing car to explaining how the ocean circulates or predicting the response of the Earth's climate to increased CO2 in the atmosphere. What unifies these flows is that they have common features, such as vortices, that occur across a huge range of sizes and times; these features have a critical effect on the phenomena being studied. The range of these problem means that to address grand challenges such as the flow of blood in the numerous arteries of the human body, over a full Formula 1 car or the interaction of a massive array of tidal turbines, it is necessary to combine state-of-the-art modelling techniques with the capability to run models on massively parallel supercomputers. In recognition of the recent changes in computer hardware, this platform will enable the group to promote the next generation of developers to provide general purpose software that takes advantage of cutting edge computer science to enable effective use of parallel computers using emerging hardware in a way that is accessible to fluid modelling experts as well as computer scientists. Hence this platform brings together a team of computer scientists and computational engineers in a fundamentally multidisciplinary project, with the dual aim of providing flexible, internationally respected and widely adopted software libraries, and of training young researchers in this emerging area.

Computational science is a multidisciplinary research endeavour spanning applied mathematics, computer science and engineering together with input from application areas across science, technology and medicine. Advanced simulation methods have the potential to revolutionise not only scientific research but also to transform the industrial economy, offering companies a competitive advantage in their products, better productivity, and an environment for creative exploration and innovation. The huge range of topics that computational science encapsulates means that the field is vast and new methods are constantly being published. These methods relate not only to the core simulation techniques but also to problems which rely on simulation. These problems include quantifying uncertainty (i.e. asking for error bars), blending models with data to make better predictions, solving inverse problems (if the output is Y, what is the input X?), and optimising designs (e.g. finding a vehicle shape that is the most aerodynamic). Unfortunately, the process through which advanced new methods find their way into applications and industrial practice is very slow. One of the reasons for this is that applying mathematical algorithms to complex simulation models is very intrusive; mostly they cannot treat the simulation code as a "black box". They often require rewriting of the software, which is very time consuming and expensive. In our research we address this problem by using automating the generation of computer code for simulation. The key idea is that the simulation algorithm is described in some abstract way (which looks as much like the underlying mathematics as possible, after thinking carefully about what the key aspects are), and specialised software tools are used to automatically build the computer code. When some aspect of the implementation needs to change (for example a new type of computer is being used) then these tools can be used to rebuild the code from the abstract description. This flexibility dramatically accelerates the application of advanced algorithms to real-world problems. Consider the example of optimising the shape of a Formula 1 car to minimise its drag. The optimisation process is highly invasive: it must solve auxiliary problems to learn how to improve the design, and it be able to modify the shape used in the simulation at each iteration. Typically this invasiveness would require extensive modifications to the simulation software. But by storing a symbolic representation of the aerodynamic equations, all operations necessary for the optimisation can be generated in our system, without needing to rewrite or modify the aerodynamics code at all. The research goal of our platform is to investigate and promote this methodology, and to produce publicly available, sustainable open-source software that ensures its uptake. The platform will allow us to make advances in our software approach that enables us to continue to secure industrial and government funding in the broad range of application areas we work in, including aerospace and automotive sectors, renewable energy, medicine and surgery, the environment, and manufacturing.