Abstract
A simple, easy-to-evaluate, surrogate model was developed for predicting the particle emission source term in wildfire simulations. In creating this model, we conceptualized wildfire as a series of flamelets, and using this concept of flamelets, we developed a one-dimensional model to represent the structure of these flamelets which then could be used to simulate the evolution of a single flamelet. A previously developed soot model was executed within this flamelet simulation which could produce a particle size distribution. Executing this flamelet simulation 1200 times with varying conditions created a data set of emitted particle size distributions to which simple rational equations could be tuned to predict a particle emission factor, mean particle size, and standard deviation of particle sizes. These surrogate models (the rational equation) were implemented into a reduced-order fire spread model, QUIC-Fire. Using QUIC-Fire, an ensemble of simulations were executed for grassland fires, southeast U.S. conifer forests, and western mountain conifer forests. Resulting emission factors from this ensemble were compared against field data for these fire classes with promising results. Also shown is a predicted averaged resulting particle size distribution with the bulk of particles produced to be on the order of 1 μm in size.
Highlights
While interest in the cause and effects of wildfires has increased dramatically over the last few decades, researchers have struggled to predict fire behavior
A reduced-order fire spread model, Quick Urban and Industrial Complex (QUIC)-Fire, was developed which significantly reduced the computational cost of wildfire simulations while maintaining an acceptable degree of accuracy in its predictive capabilities [4]
Each image shows the computation of the particle emission source term in a single timestep
Summary
While interest in the cause and effects of wildfires has increased dramatically over the last few decades, researchers have struggled to predict fire behavior. Implementation of a physics-based particle formation model in a wildfire CFD model is difficult for two reasons. The fundamental physics and chemistry which govern the initial formation of soot, the backbone of wildfire particulate emissions, occur on a microscale level, and implementation of a fundamental model would require resolution of the simulation at a millimeter to centimeter scale. We implemented a highly-detailed, physics-based model on a microscale in a variety of scenarios one might see in a wildfire. The results of these simulations were used to identify key characteristics of the fire which govern particle formation and calibrate a proposed, easy-to-evaluate, surrogate model to these simulations. Experimental data were used to validate the proposed surrogate model
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