Lulu Sun

Fire spread in chaparral—'go or no-go?'

Current fire models are designed to model the spread of a linear fire front in dead, small-diameter fuels. Fires in predominantly living vegetation account for a large proportion of annual burned area in the United States. Prescribed burning is used to manage living fuels; however, prescribed burning is currently conducted under conditions that result in marginal burning. We do not understand quantitatively the relative importance of the fuel and environmental variables that determine spread in live vegetation. To address these weaknesses, laboratory fires have been burned to determine the effects of wind, slope, moisture content and fuel characteristics on fire spread in fuel beds of common chaparral species. Four species (Adenostoma fasciculatum, Ceanothus crassifolius, Quercus berberidifolia, Arctostaphylos parryana), two wind velocities (0 and 2 m s −1 ) and two fuel bed depths (20 and 40 cm) were used. Oven-dry moisture content of fine fuels (<0.63 cm diameter) ranged from 0.09 to 1.06. Seventy of 125 fires successfully propagated the length (2.0 m) of the elevated fuel bed. A logistic model to predict the probability of successful fire spread was developed using stepwise logistic regression. The variables selected to predict propagation were wind velocity, slope percent, moisture content, fuel loading, species and air temperature. Air temperature and species terms were removed from the model for parsimony. The final model correctly classified 94% of the observations. Comparison of results with an empirical decision matrix for prescribed burning in chaparral suggested some agreement between the laboratory data and the empirical tool.

Experimental Investigation Of The Velocity Field In Buoyant Diffusion Flames Using PIV And TPIV Algorithm

We investigated a simultaneous temporally and spatially resolved 2-D velocity field above a burning circular pan of alcohol using particle image velocimetry (PIV). The results obtained from PIV were used to assess a thermal particle image velocimetry (TPIV) algorithm previously developed to approximate the velocity field using the temperature field, simultaneously captured by an infrared (IR) thermal camera. By tracing “thermal particles,” which were assumed to be virtual particles that corresponded to pixels of temperature values in successive IR images, the TPIV algorithm estimated a larger scale instantaneous velocity field than either a single-point velocity measurement (e.g., LDV) or the area velocity measurement such as PIV. Instantaneous velocity fields obtained from both methods are presented. Time series vertical velocity profiles and time-averaged velocity vector fields are compared. The comparison demonstrates the applicability and performance of the TPIV algorithm in wildfire research.