Elsa Pastor Ferrer

The effect of the computational grid size on the prediction of a flammable cloud dispersion

The consequence analysis is used to define the extent and nature of effects caused by undesired events being of great help when quantifying the damage caused by such events. For the case of leaking of flammable and/or toxic materials, effects are analyzed for explosions, fires and toxicity. Specific models are used to analyze the spills or jets of gas or liquids, gas dispersions, explosions and fires. The central step in the analysis of consequences in such cases is to determine the concentration of the vapor cloud of hazardous substances released into the atmosphere, in space and time. With the computational advances, CFD tools are being used to simulate short and medium scale gas dispersion events, especially in scenarios where there is a complex geometry. However, the accuracy of the simulation strongly depends on diverse simulation parameters, being of particular importance the grid resolution. This study investigates the effects of the computational grid size on the prediction of a cloud dispersion considering both the accuracy and the computational cost. Experimental data is compared with the predicted values obtained by means of CFD simulation, exploring and discussing the influence of the grid size on cloud concentration the predicted values. This study contributes to optimize CFD simulation settings concerning grid definition when applied to analyses of consequences in environments with complex geometry.

Short term forecasting of large scale wind-driven wildfires using thermal imaging and inverse modelling techniques

Eucalyptus trees are among the most important sources of firebrands with potential to produce spot fires which are one of the most relevant manifestations of extreme fire behaviour. The mechanism of spot fires has several sequential phases starting in the release of a burning ember which is lofted and transported by the airflow, to land in a fuel bed causing a new ignition. In the present study, dedicated to the phase of firebrand release, an analysis of the firebrands released in a process of burning of eucalyptus trees is carried out. Barks of eucalyptus trees have a great potential to produce firebrands during a forest fire. Sometimes these fuels are lay down on the ground or are attached to the trunk and to large branches of the canopy. In this study, the number and size distribution of firebrands released during the burning of these fuels for different scenarios are analysed. Three tests were made varying the location of the barks (suspended and lay down on a fuel bed) and the orientation of suspended barks (vertical and horizontal). An additional similar test was performed with a fuel bed composed of shrubs. A particle image velocimetry system available in the Forest Fire Research Laboratory of ADAI was used to analyse the release of firebrands. Additionally, the convective up flow velocity and the temperature 2m above the tree, as well as the weight loss decay were measured. The final results among the several parameters controlled are compared.A key factor in decision-making process during a wildfire incident is counting on the forecast of how the fire is likely to behave in different fuels, weather conditions and terrain. Wildfire models and simulators attempt to assist fire responders in gaining understanding of the fire behaviour. The main hurdle to overcome when applying such technologies at operational level is the lack of a complete model that describes wildfire governing physics and the trade-off between accuracy and computing time. A forecasting prediction must be delivered within a positive lead time and current physical models are far beyond this requirement. Inverse modelling and data assimilation techniques offer a great potential of operational applicability in wildfires, coupling fire monitoring and fire behaviour forecast at real time. With this approach, a better description of the processes simulated by the fire behaviour models can be achieved when adding real-state information of the system, since discrepancies between simulated fire behaviour variables and observed variables are minimized. The use of this approach accelerates fire simulations without loss of forecast accuracy. In this paper we explore the adaptation to real fire scenarios of a synthetic-data-based inverse modelling structure for fire behaviour forecast. Improvements are investigated to extrapolate the already existing algorithm to real data assimilation from IR aerial monitoring. The technique explores elliptical Huygens expansion coupled with simple -yet effective- semi-empirical wildfire models. The algorithm assimilates fire fronts positions extracted from airborne thermal imaging and additional available data as wind speed and direction or fuel characteristics. The invariants -set of governing parameters that are mutually independent and constant for a significant amount of time- are resolved by means of forward model and linear tangent minimization. The technique has been adapted to be employed in large-scale mallee-heath shrubland fires experiments conducted in South Australia in 2008. Fires were filmed with a helicopter transported TIR camera. The IR images were processed to obtain the position of the fire perimeter at a maximum frequency of one isochrone every 10 seconds. The algorithm shows great capability to simulate fire fronts observations and opens the door to keep developing a fully automatic data assimilation algorithm with forecasting capacity.