Efficient Simulations of Propagating Flames Using Adaptive Mesh Refinement

TL;DR

This paper introduces an adaptive mesh refinement technique in a fire simulation solver, enabling efficient and accurate modeling of wildland fire spread across complex terrains while significantly reducing computational costs.

Contribution

The study develops and demonstrates an adaptive mesh refinement method integrated into fireFoam, improving simulation efficiency for propagating wildfires without prior knowledge of fire spread.

Findings

AMR accurately tracks fire spread regions

Reduces computational cost compared to static mesh refinement

Successfully simulates large-scale fire propagation on complex terrains

Abstract

Wildland fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and "outer-loop" techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to wildland fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena. The AMR functionality in the extended solver, called wildFireFoam, allows us to dynamically track regions of interest and to avoid inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability for fire spread on vertical panels and for large-scale fire propagation on a variable-slope surface that is representative of real topography. We show that the AMR solver reproduces results obtained using much larger statically refined meshes, at a substantially reduced computational cost.