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Intelligent Egress: Real time modelling based upon sensor data to steer evacuation in case of fire |
Dr Stephen Welch
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Intelligent egress is a novel approach to enhancing evacuations from fire emergencies. It combines sensor-linked simulations and route-planning tools to provide real-time information to occupants on efficient egress. The specific issues associated with disabilities and mobility impairment are addressed. Mechanisms to provide “way finding” information to relevant end users are being studied. Detailed guidance and recommendations on use of such systems will be developed.
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Investigating the micromechanics of granular soils subjected to cyclic loading using the discrete element method |
Dr Kevin Hanley
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The objective of this research is to investigate the behavior of Dunkerque sand under undrained triaxial cyclic loading using the discrete element method (DEM).
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Investigation of particle breakage of dry granular materials using x-ray computed tomography and the DEM |
Prof. Jin Ooi
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When a load is applied to an assembly of particles and particle breakage occurs, the macroscopic behaviour of the assembly is greatly affected by changes in the micro-scale caused by breakage. In this project particle breakage is studied in 3D using x-ray tomography and simulating the process with the DEM.
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Measurement and modelling of powder flow in flexible containers |
Prof. Jin Ooi
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The research focuses on understanding cohesive powder flow in flexible bulk solid containers (buggies and bulk bags) with a view to develop a design methodology for ensuring reliable discharge from these containers. The project involves experimental powder flowability characterisation, finite element analysis of the stresses in flexible containers and pilot scale experiments to study the powder flow field and validate the new design methodology for reliable discharge.
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Measurement and modelling of powder flow in flexible containers |
Prof. Jin Ooi
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The research focuses on understanding cohesive powder flow in flexible bulk solid containers (buggies and bulk bags) with a view to develop a design methodology for ensuring reliable discharge from these containers. The project involves experimental powder flowability characterisation, finite element analysis of the stresses in flexible containers and pilot scale experiments to study the powder flow field and validate the new design methodology for reliable discharge.
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Modelling and measurement for oil and gas multi-phase flows - SPH-DEM fluid-particle simulation and validation |
Dr Filipe Teixeira-Dias
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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.
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Modelling of dense suspensions rheology |
Dr. Jin Sun
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We examine the rheology of granular dense suspensions using computer simulations with discreste particles and develop constitutive models for flow of such suspensions.
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Models for manufacturing of particulate products |
Professor Jin Ooi
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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.
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Multi-scale analyses of wildland fire combustion processes |
Dr Rory Hadden
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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.
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Multi-scale analysis of DEM data to enhance the prediction at system scale |
Prof. Jin Ooi
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While the discrete element method (DEM) can provide particle-scale information to inform the design of particulate equipment, many industrial sectors are interested in large-scale modelling and scaling-up processes [1].
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