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.

According to Moore's law, the number of transistors on a micro-chip doubles every two years. Hence, the transistor size is expected to approach atomic scale in the near future due to our quest for miniaturization and more processing power. However, atomic level behaviour is governed by the laws of quantum physics, which are significantly different from those of classical physics. More explicitly, the inherent parallelism associated with quantum entities allows a quantum computer to carry out operations in parallel, unlike conventional computers. More significantly, quantum computers are capable of solving challenging optimization problems in a fraction of the time required by a conventional computer. However, the major impediment in the practical realization of quantum computers is the sensitivity of the quantum states, which collapse when they interact with their environment. Hence, powerful Quantum Error Correction (QEC) codes are needed for protecting the fragile quantum states from undesired influences and for facilitating the robust implementation of quantum computers. The inherent parallel processing capability of quantum computers will also be exploited to dramatically reduce the detection complexity in future generation communications systems. In this work, we aim for jointly designing and ameliorating classical and quantum algorithms to support each other in creating powerful communications systems. More explicitly, the inherent parallelism of quantum computing will be exploited for mitigating the high complexity of classical detectors. Then, near-capacity QEC codes will be designed by appropriately adapting algorithms and design techniques used in classical Forward Error Correction (FEC) codes. Finally, cooperative communications involving both the classical and quantum domains will be conceived. The implementation of a quantum computer purely based on quantum-domain hardware and software is still an open challenge. However, a classical computer employing some quantum chips for achieving efficient parallel detection/processing may be expected to be implemented soon. This project is expected to produce a 'quantum-leap' towards the next-generation Internet, involving both classical and quantum information processing, for providing reliable and secure communications networks as well as affordable detection complexity.

In view of the rapid increase in demand for mobile data services, next generation wireless access networks will have to provide greatly increased capacity density, up to 10 Gbps per square kilometre. This will require a much larger density of very small, cheap and energy-efficient base stations, and will place increasing demand on the bandwidth and energy efficiency of the network, and especially the backhaul network. Recent work on network MIMO, or coordinated multipoint (CoMP) has shown that by ensuring base stations cooperate to serve users, especially those close to cell edge, rather than interferring with one another, inter-user interference can be effectively eliminated, greatly increasing the efficiency of the network, in terms of both spectrum and energy. However this tends to greatly increase the backhaul load. This work proposes a form of wireless network coding, called network coded modulation, as an alternative to conventional CoMP. This also enables base station cooperation, but instead of sending multiple separate information flows to each base station, flows are combined using network coding, which in principle allows cooperation with no increase in backhaul load compared to non-cooperative transmission, while gaining very similar advantages to CoMP in terms of bandwidth and energy efficiency. The objective of the proposed work is to establish the practical feasibility of this approach, and evaluate its benefits, as applied to next generation wireless access networks. To this end it will develop practical signalling schemes, network coordination and management protocols, and, with the help of industrial collaborators, will ensure compatibility with developing wireless standards.