RESEARCH ARTICLE (Open Access)

A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow

Qing Wang

^A ^* , Matthias Ihme ^A ^B , Rod R. Linn ^C , Yi-Fan Chen ^A , Vivian Yang ^A , Fei Sha ^A , Craig Clements ^D , Jenna S. McDanold ^C and John Anderson ^A

+ Author Affiliations

- Author Affiliations

^A Research Google, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.

^B Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, CA 94305, USA.

^C Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

^D Department of Meteorology, San Jose State University, 1 Washington Square, San Jose, CA 95192, USA.

^* Correspondence to: wqing@google.com

International Journal of Wildland Fire 32(12) 1711-1725 https://doi.org/10.1071/WF22225

Submitted: 15 December 2022 Accepted: 17 September 2023 Published: 18 October 2023

© 2023 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 4.0 International License (CC BY).

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/atmospheric coupling, fire management, fire propagation, large-eddy simulation, tensor processing units, TensorFlow, wildfire modelling, wildland fire prediction.

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