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.
Working with the fire brigades, and using a small-scale experimental apparatus to define appropriate fire-fighting responses to underventilated fires in sealed or partially sealed compartments.
Dr Dimitrios Gerogiorgis, Lecturer in Chemical Engineering, has been awarded a Royal Academy of Engineering Industrial Fellowship in collaboration with Molson Coors, the global brewing company.
Qi Zhou (co-supervised by Timm Krüger and Peter R. Hoskins) has won the prize for Best Student Poster in Computational Biophysics at an international congress held in Edinburgh last month.
