Abstract
Modeling flow in vegetative fuel beds is a key component in any detailed physics-based tool for simulating wildland fire dynamics. Current approaches for drag modeling, particularly those employed in multiphase computational fluid dynamics (CFD) models, tend to take a relatively simple form and have been applied to a wide range of fuel structures. The suitability of these approaches has not been rigorously tested for conditions which may be encountered in a wildland fire context. Here, we focus on beds of Pinus rigida needle litter and undertake a two-part study to quantify the drag and evaluate the capabilities of a multiphase large eddy simulation CFD model, the NIST Fire Dynamics Simulator. In the first part, bed drag was measured in a wind tunnel under a range of conditions. The results were fit to a Forchheimer model, and the bed permeability was quantified. A traditional approach employed in the multiphase formulation was compared to the parameterized Forchheimer equation and was found to over-predict the drag by a factor of 1.2–2.5. In the second part, the development of a velocity profile above and within a discrete fuel layer was measured. Using the Forchheimer equation obtained in the first part of the study, the CFD model was able to replicate a qualitatively consistent velocity profile development. Within the fuel bed, the model appeared to under-predict the velocity magnitudes, which may be the result of unresolved pore-scale flow dynamics.
Highlights
A strong understanding of the underlying processes that drive wildland fires is necessary for providing robust, science-based solutions to the challenges they presented to society
A common approach to incorporating the influence of wildland fuels into more established Computational Fluid Dynamics (CFD) frameworks is known as the multiphase formulation
Vegetation is treated as an assemblage of subgrid-scale particles, typically assumed to be thermally-thin, and their influence is spatially averaged into a porous media
Summary
A strong understanding of the underlying processes that drive wildland fires is necessary for providing robust, science-based solutions to the challenges they presented to society. Examples include the importance of fuel structure across a range of scales (Dahale et al 2013; Pimont et al 2011), the importance of flame structure (Linn et al 2012; Frangieh et al 2020), and the role of char oxidation (El Houssami et al 2016; Perez-Ramirez et al 2017) As these models evolve, it is important to ensure that the underlying submodels are formulated appropriately and are tested rigorously against relevant experimental data (Mell et al 2010; Morvan 2011). A number of specific CFD models can be found today, but they generally trace their origin to the work of Mell et al (2010), Grishin (1997) and Larini et al (1998) In this approach, vegetation is treated as an assemblage of subgrid-scale particles, typically assumed to be thermally-thin, and their influence is spatially averaged into a porous media. Processes which fundamentally occur at the scale of individual particles must be submodeled, generally employing a combination of simplifying assumptions and empiricism, a number of which have yet to be rigorously verified
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