Achieving UK and EU emissions targets requires a transformation in the power generation and manufacturing industries. In the UK we consume 350TWhr of electricity every year, but with modern power-stations which are typically around 50% efficient a large proportion of energy is wasted as rejected heat. Recovering just 10% of this heat would save the equivalent power output of 22 power stations. This is not to mention the heat which could be recovered from manufacturing industries where large quantities of energy are wasted through the heating and cooling during metal-forming processes. In order to make heat-recovery economically viable, low-temperature Organic Rankine Cycles (ORC) can be deployed using fluids with boiling-points close to ambient temperatures, such as many 'molecularly-complex' fluids. The power is extracted in an ORC across a turbine, where these 'molecularly-complex' fluids exist in a gaseous state, and pass through the turbine at high speeds. Increasing the power extracted from the turbine makes heat-recovery systems much more economically favourable and can be achieved by raising the pressure ratio across the turbine. In order to do this efficiently requires a better understanding of molecular-complex gas flows because there is very little known about these complex flows in turbines. The lack of an in-depth understanding of the molecular complex gas-dynamics in ORC turbines means that it is unlikely that optimum power levels are being achieved with present-day design methods. Therefore this proposal aims to determine methods of significantly increasing heat-recovery system power outputs by exploiting the effects of molecular complexity in Organic Rankine Cycle turbines. A target is set of doubling current turbine power levels. In order to determine methodologies to achieve this, a combination of experimental and computational tests are planned. Experiments of molecularly complex gas flows will be studied using a specially designed experimental test-rig which will be able to mimic the flow conditions found in the ORC turbine. The computational simulations will involve the use of a research flow-solver, which will be modified to account for molecular-complex gas properties. The experimental data will aid the development of an accurate computational model, which will then be used to determine novel turbine blade designs to operate at high pressure ratios. This research will directly benefit both the fluid-mechanics research community and the power-generation industry. The research will improve our fundamental understanding of the fluid mechanics of molecularly complex fluids, and will also aid the development of sustainable power generation technologies. An improved understanding of molecular-complex gas flows in turbines has the potential to substantially reduce the UK's fossil fuel dependence and improve our ability to recover currently otherwise 'wasted' heat from power stations and manufacturing processes as well as solar and geothermal radiation. This has a large societal benefit both in-terms of aiding the fight against climate-change and improving the UK's energy security. This work will help towards meeting the targets of the UK Climate Change Act 2008 to reduce by 34 percent our greenhouse gas emissions by 2020 and 80 percent by 2050, against the 1990 baseline.

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.