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 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 involving pyrolyzing solid surfaces. The AMR functionality in the extended solver, called fireDyMFoam, is load balanced, models gas, solid, and liquid phases, and allows us to dynamically track regions of interest, thus avoiding inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability and computational efficiency for fire spread on vertical panels, showing that the AMR solver reproduces results obtained using much larger statically refined meshes, but at a substantially reduced computational cost. We then leverage AMR in an optimization framework for fire suppression based on the open-source Dakota toolkit, which is made more computationally tractable through the use of fireDyMFoam, minimizing a cost function that balances water use and solid-phase mass loss. The extension of fireFoam developed here thus enables the use of higher fidelity simulations in optimization problems for the suppression of fire spread in both built and natural environments.

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 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 involving pyrolyzing solid surfaces. The AMR functionality in the extended solver, called fireDyMFoam, is load balanced, models gas, solid, and liquid phases, and allows us to dynamically track regions of interest, thus avoiding inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability and computational efficiency for fire spread on vertical panels, showing that the AMR solver reproduces results obtained using much larger statically refined meshes, but at a substantially reduced computational cost. We then leverage the computational efficiency of the AMR solver to demonstrate an optimization framework for fire suppression based on the open-source Dakota toolkit.

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Computational simulations have the potential to provide low-cost, low-risk insights into wildland fire structure and dynamics. Simulation accuracy is limited, however, by the difficulty of modeling physical processes that span a wide range of spatial scales. These processes include heat transfer via radiation and turbulent advection, as well as both solid- and gas-phase chemistry. In the present study, we perform large eddy simulation (LES) with adaptive mesh refinement to model the multi-phase pyrolysis and combustion of dry Douglas fir, where temperature-based lookup tables corresponding to a multi-step pyrolysis mechanism are used to represent the composition of gas-phase pyrolysis products. Gas-phase and surface temperatures, mass loss, and water vapor mole fraction from the LES are shown to compare favorably with experimental measurements of a radiatively heated Douglas fir fuel sample undergoing pyrolysis and combustion beneath a cone calorimeter. Using frequency comb laser diagnostics, optical and infrared cameras, and a load cell, the experiments provide simultaneous in situ, time-resolved measurements of chemical composition, temperature, and mass loss. The present study thus combines cutting edge computational and experimental techniques with multi-step chemical pyrolysis modeling to provide a validated computational tool for the prediction of solid fuel pyrolysis and combustion relevant to wildland fires.