Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Efficient Simulations of Propagating Flames and Fire Suppression Optimization Using Adaptive Mesh Refinement

Version 1 : Received: 25 June 2021 / Approved: 28 June 2021 / Online: 28 June 2021 (15:30:15 CEST)

How to cite: Lapointe, C.; Wimer, N.T.; Simons-Wellin, S.; Glusman, J.F.; Rieker, G.B.; Hamlington, P.E. Efficient Simulations of Propagating Flames and Fire Suppression Optimization Using Adaptive Mesh Refinement. Preprints 2021, 2021060679 (doi: 10.20944/preprints202106.0679.v1). Lapointe, C.; Wimer, N.T.; Simons-Wellin, S.; Glusman, J.F.; Rieker, G.B.; Hamlington, P.E. Efficient Simulations of Propagating Flames and Fire Suppression Optimization Using Adaptive Mesh Refinement. Preprints 2021, 2021060679 (doi: 10.20944/preprints202106.0679.v1).

Abstract

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.

Subject Areas

numerical simulations; adaptive mesh refinement; fire spread; optimization

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