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

BackgroundWildfires 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.AimsThe 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.MethodsWe developed a coupled fire/atmosphere simulation framework using TensorFlow, which enables efficient and scalable computations on Tensor Processing Units.Key resultsSimulation 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.ConclusionsWhile 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.ImplicationsThis new simulation framework is efficient in capturing both global quantities and unsteady dynamics of wildfires at high spatial resolutions. This work presents a new wildfire simulation framework implemented in TensorFlow for landscape-scale wildfire simulations with physical representation of the combustion at affordable computational cost on the Tensor Processing Unit (TPU) hardware architecture. We validate this simulation framework and demonstrate its efficiency with simulations of the prescribed fire experiment.