Carbon capture from power stations and industrial sources is an essential pillar in the effort of reducing greenhouse gas emissions in order to achieve the legally binding target set by the 2008 Climate Change Act of 80% reductions by 2050. The current state-of-the-art technologies for post-combustion capture (including retrofit options for existing plants) are based on amine scrubbers, but inherent energy requirements make this an expensive option and significant research is aimed at the development of next generation carbon capture processes that reduce the cost of capital equipment and the energy needed.
This project will develop improved methodologies and tools for assessing and providing more detailed information on complex system-user interactions, which will be further implemented in an integrated framework for system state identification, system or plant/component condition assessment and evaluation of the overall system performance (all currently performed in a number of separate studies).
The exploration and development of deeper wells with heavier and more viscous oils, requiring greater operating pressures and more fracture to fissures to release the oils. This results in significantly increased sand content that has the potential to bring about a fundamental shift in flow behaviour. This project aims to investigate the potential – and develop – a coupled smooth particle hydrodynamics (SPH) and discrete element method (DEM) model to simulate high-pressure multi-phase flows with support from an extensive experimental programme and industrial collaboration.
This project aims to create a generally applicable framework for transferring academic innovations in the modelling of particulate materials into industrial practice in the UK. The process of twin-screw granulation has been selected as an exemplar industrial process which is simulated across multiple scales using the coupled methods of population balance modelling and the discrete element method.
Low intensity prescribed fires are often employed in forests and wildland in order to manage hazardous fuels, restore ecological function and historic fire regimes, and encourage the recovery of threatened and endangered species. Current predictive models used to simulate fire behavior during low-intensity prescribed fires (and wildfires) are empirically-based, simplistic, and fail to adequately predict fire outcomes because they do not account for variability in fuel characteristics and interactions with important meteorological variables. Experiments are being carried out at scales ranging from the fuel particle, to fuel bed, to field plot and stand scales, with an aim of better understanding how fuel consumption is related to the processes driving heat transfer, ignition and flame spread, and thermal degradation through flaming and smouldering combustion, at the scale of individual fuel particles and fuel layers. Focus is placed on how these processes, and thus fuel consumption, are affected by spatial variability in fuel particle type, fuel moisture status, bulk density, and horizontal and vertical arrangement of fuel components, as well as multi-scale atmospheric dynamics.
This project aims to innovate and improved solutions for the management of power flows in a hybrid electrical power system, to provide a secure, reliable, and high quality supply to varying load demands. The expected research outcome is the design of a robust and fault-tolerant management system, featuring higher efficiency and improved techno-economic performance.
Optimal system sizing through linear programming
Testing and analysis of an off-the-shelf hybrid system
Novel control system design for optimised performance