A mixture-fraction-based thermodynamic equilibrium approach for modeling gas-phase combustion was adapted and used in FIRETEC, a wildfire computational fluid dynamics model. The motivation behind this work was the desire to incorporate the features of complex chemistry calculations from the thermodynamic equilibrium model into FIRETEC without significantly increasing the computational burden of the program. In order to implement the mixture-fraction-based thermodynamic equilibrium approach, a sub-grid pocket model was developed to simulate the local mixture fraction of sub-grid flame sheets. Numerical simulations of wildfires were performed using FIRETEC with the new sub-grid, mixture-fraction-based pocket model to model gas-phase combustion. The thermodynamic equilibrium model was used to calculate flame temperatures and combustion products, including CO2 and CO, for sub-grid, gas-phase combustion in FIRETEC simulations. Fire spread rates from simulations using the new sub-grid combustion model were 25-100% higher than fire spread rates from previous FIRETEC simulations, but the successes of modeling propagating fire lines and calculating detailed equilibrium combustion products from simulated sub-grid flame sheets demonstrated the feasibility of this new approach. Future work into the fine-tuning of pocket model parameters and modifying the conservation equation for energy in FIRETEC was recommended.

Wildfire modeling is a complex, computationally costly endeavor, but with droughts worsening and fires burning across the western United States, obtaining accurate wildfire predictions is more important than ever. In this paper, we present a novel approach to wildfire modeling using data assimiliation. We model wildfire spread with a modification of the partial differential equation model described by Mandel et al. in their 2008 paper. Specifically, we replace some constant parameter values with geospatial functions of fuel type. We combine deep learning and remote sensing to obtain real-time data for the model and employ the Nelder-Mead method to recover optimal model parameters with data assimilation. We demonstrate the efficacy of this approach on computer-generated fires, as well as real fire data from the 2021 Dixie Fire in California. On generated fires, this approach resulted in an average Jaccard index of 0.996 between the predicted and actual fire perimeters and an average Kulczynski measure of 0.997. On data from the Dixie Fire, the average Jaccard index achieved was 0.48, and the average Kulczynski measure was 0.66.