Publication Date
10-18-2023
Document Type
Article
Publication Title
International Journal of Wildland Fire
Volume
32
Issue
12
DOI
10.1071/WF22225
First Page
1711
Last Page
1725
Abstract
Background. Wildfires are becoming more severe, so we need improved tools to predict them over a wide range of conditions and scales. One approach towards this goal entails the use of coupled fire/atmosphere modelling tools. Although significant progress has been made in advancing their physical fidelity, existing tools have not taken full advantage of emerging programming paradigms and computing architectures to enable high-resolution wildfire simulations. Aims. The aim of this study was to present a new framework that enables landscape-scale wildfire simulations with physical representation of combustion at an affordable cost. Methods. We developed a coupled fire/atmosphere simulation framework using TensorFlow, which enables efficient and scalable computations on Tensor Processing Units. Key results. Simulation results for a prescribed fire were compared with experimental data. Predicted fire behavior and statistical analysis for fire spread rate, scar area, and intermittency showed overall reasonable agreement. Scalability analysis was performed, showing close to linear scaling. Conclusions. While mesh refinement was shown to have less impact on global quantities, such as fire scar area and spread rate, it benefits predictions of intermittent fire behavior, buoyancy-driven dynamics, and small-scale turbulent motion. Implications. This new simulation framework is efficient in capturing both global quantities and unsteady dynamics of wildfires at high spatial resolutions.
Keywords
fire management, fire propagation, fire/atmospheric coupling, large-eddy simulation, tensor processing units, TensorFlow, wildfire modelling, wildland fire prediction
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Meteorology and Climate Science
Recommended Citation
Qing Wang, Matthias Ihme, Rod R. Linn, Yi Fan Chen, Vivian Yang, Fei Sha, Craig Clements, Jenna S. McDanold, and John Anderson. "A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow" International Journal of Wildland Fire (2023): 1711-1725. https://doi.org/10.1071/WF22225
