Characteristics of impinging flames from a multi-slot burner fueled by synthetic gas is studied using a comprehensive numerical model. A multi-slot burner with five fuel slots and six air slots arranged in an alternate manner has been analyzed. The power rating of the burner is kept as 10 kW and the air flow rate is fixed at 400% of stoichiometric air required for the net fuel flow rate. The numerical model incorporates a short chemical kinetic mechanism, variable thermo-physical properties, full multi-component diffusion, thermal diffusion and a radiation sub-model. The location of the solid surface from the burner exit, primary air in fuel stream, dimensions of solid surfaces and their temperatures have been varied to study the heat flux distributions from impinging flames. Results show that the location of the solid surface from the burner ports and partial premixing affect the heat transfer characteristics. The heat fluxes received by the side and top surfaces depend on the characteristics of neighbouring flames located around the central flame. The heat flux distribution and the net heat flux received by the surface are found to be uniform and optimum at a height of 90 mm from burner exit. Mass fraction of CO is affected by heating height and partial premixing. It remains almost constant for varying temperature of the impinging surfaces. Primary aeration of 20% is found to be optimum for higher net heat flux and lower CO emissions.
Prescribed burns are an essential tool of fire management to reduce the impact and occurrence of wildfires. While managing prescribed burns, the smoke trajectory and downwind exposure to smoke are intimately coupled with the smoke production dynamics and the development of the fire plume in the vicinity of the fire front. In turn, the fire plume development is strongly coupled to fire behavior and the flow environment near the fire. This work aims at understanding fire behavior and plume development while interacting with vegetation at the large laboratory scale through experiments and modeling. In order to investigate these coupled processes, initially, flame and plume behavior from a static fire source will be characterized. A rectangular pool fire fueled by diesel is used and point measurements of flow, temperature and heat flux will be conducted. The burning rate will be measured using a load cell. K-type thermocouples and bi-directional pressure probes will be used for measuring the temperature and velocity, respectively in the flame and plume zones. These data will be used for validating a numerical model for simulating pool fires and the model will be subsequently used for predicting the plume interaction with vegetation. A Douglas fir tree, whose properties are well defined in the literature, will be used as vegetation. The Lagrangian particle model available in the Fire Dynamics Simulator (FDS) will be used to model the tree. The tree will be of regular shape and size with foliage and different classes of wood segregated based on typical size (diameter) range. The bulk density of the tree will be varied to replicate the systematic and controlled variation of the flow obstruction encountered by the plume and gives a realistic prediction of velocity, temperature, and heat flux within the vegetation. In the future, experiments with vegetation located in the plume region will be conducted to validate the numerical predictions.
Fire behavior models ingest a variety of inputs such as weather, topography, and fuel maps to generate predictions of how a fire will behave. Model prediction accuracy is thus to some degree dependent on the fidelity of the input data sources. For many widely used fire models, however, the exact relationship between fuel input quality and model performance is not well understood. This paper seeks to quantify the relationship between input fuel data and output prediction accuracy in popular fire models based on the Rothermel fire spread equation. In particular, it examines how granularity of fuel classes, spatial resolution, and temporal resolution affect the accuracy of fire behavior predictions. Fuel maps used in the study are generated from remote sensing images using machine learning to map between satellite and ground conditions. Prediction accuracy is evaluated with multiple metrics including rate of spread (ROS) and fire front shape. The outcomes of this study will provide important guidance as to the benefit of producing high fidelity fuel maps when utilizing the Rothermel spread equation to predict fire behavior.
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