QES-Fire: a dynamically coupled fast-response wildfire model
Matthew J. Moody A , Jeremy A. Gibbs B , Steven Krueger C , Derek Mallia C , Eric R. Pardyjak A , Adam K. Kochanski D , Brian N. Bailey E and Rob Stoll
A *
+ Author Affiliations
- Author Affiliations
A Department of Mechanical Engineering, University of Utah, Salt Lake City, Utah, USA.
B National Oceanic and Atmospheric Administration/Oceanic and Atmospheric Research National Severe Storms Laboratory, Norman, Oklahoma, USA.
C Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah, USA.
D Department of Meteorology and Climate Science, San Jose State University, San Jose, California, USA.
E Department of Plant Sciences, University of California Davis, Davis, California, USA.
* Correspondence to: rstoll@eng.utah.edu
International Journal of Wildland Fire 31(3) 306-325 https://doi.org/10.1071/WF21057
Submitted: 30 April 2021 Accepted: 21 January 2022 Published: 18 March 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC)
Abstract
A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model’s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire’s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data.
Keywords: buoyant plume, diagnostic wind solver, fire–atmosphere coupling, level set method, merging plumes, plume rise model, rate of spread, simplified fire spread physics.
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