A hybrid stochastic Lagrangian-cellular automata framework for modelling fire propagation in inhomogeneous terrains

A stochastic model motivated by the Lagrangian transported probability density function method for turbu-lent reacting flows and the cellular automata approach for forest fires was put together to simulate propaga-tion of fires in terrains with inhomogeneous composition. In contrast to the usual cellular automata models for fires where the probability of ignition is prescribed, here the ignition of cells is determined by a random walk that mimics turbulent convection and diffusion of the hot gases and firebrands from upwind and neigh-bouring fire fronts. Radiation is also included. The model is aimed at speed of computation while approx-imating the key physics through only a few terrain-related inputs and tunable parameters representing fire intensity, hot gas and ember decay timescales, cell ignition delay and local turbulence. These parameters were calibrated against controlled fire experiments and the model was then used to give reasonable predictions for fires of increasing complexity. The presented framework allows improvements for more accurate representa-tion of the flammable material characteristics, fire-induced flow modifications, and most other phenomena present in fires, hence providing an extendable and simple yet physically-realistic novel modelling approach.& COPY; 2022 The Author(s). Published by Elsevier Inc. on behalf of The Combustion Institute. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

This article proposes a new stochastic model to simulate the propagation of fires, which combines the Lagrangian transported probability density function method for turbulent reacting flows and the cellular automata approach for forest fires. Unlike conventional cellular automata models for fires, the ignition of cells in this model is determined by a random walk that mimics turbulent convection and diffusion of hot gases and firebrands from upwind and neighboring fire fronts. The model aims to approximate the key physics while speeding up computation by using only a few terrain-related inputs and tunable parameters.