Numerical simulation and experiments of burning douglas fir trees

Mell, William E.; Maranghides, Alexander; McDermott, Randall J.; Manzello, Samuel L.

doi:10.1016/j.combustflame.2009.06.015

Cited by 235 publications

(267 citation statements)

References 32 publications

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“…One type of coupled model combines CFD models used to simulate airflows in fine grid spacing (~1 m) in small domains (under~1 km 3 ) with equations that parameterize the combustion of wildland fuels but omit extensions that would treat weather processes (e.g., High-Resolution Model for Strong Gradient Applications (HIGRAD)/FIRETEC [10] subsequently referred to as FIRETEC, and the Wildland Fire Dynamic Simulator (WFDS) [23,24]). Another type combines a different type of CFD model-a numerical weather prediction (NWP) model-with empirical or semi-empirical relationships describing fire behavior.…”

Section: Simulating Weather Wellmentioning

confidence: 99%

Some Requirements for Simulating Wildland Fire Behavior Using Insight from Coupled Weather—Wildland Fire Models

Coen

2018

Fire

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A newer generation of models that interactively couple the atmosphere with fire behavior have shown an increased potential to understand and predict complex, rapidly changing fire behavior. This is possible if they capture intricate, time-varying microscale airflows in mountainous terrain and fire-atmosphere feedbacks. However, this benefit is counterbalanced by additional limitations and requirements, many arising from the atmospheric model upon which they are built. The degree to which their potential is realized depends on how coupled models are built, configured, and applied. Because these are freely available to users with widely ranging backgrounds, I present some limitations and requirements that must be understood and addressed to achieve meaningful fire behavior simulation results. These include how numerical weather prediction models are formulated for specific scales, their solution methods and numerical approximations, optimal model configurations for common scenarios, and how these factors impact reproduction of fire events and phenomena. I discuss methods used to adjust inadequate outcomes and advise on critical interpretation of fire modeling results, such as where errors from model limitations may be misinterpreted as natural unpredictability. I discuss impacts on other weather model-based applications that affect understanding of fire behavior and effects.

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Section: Simulating Weather Wellmentioning

confidence: 99%

Some Requirements for Simulating Wildland Fire Behavior Using Insight from Coupled Weather—Wildland Fire Models

Coen

2018

Fire

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“…Turbulence in the gas-phase is solved through a large eddy simulation (LES) approach). WFDS was validated for both a grassland fire evolution (the system considered in this work) in [25], and crown fires [34].…”

Section: Wfdsmentioning

confidence: 99%

“…The grassfire propagation is simulated through a 3D model, called Wildland Fire Dynamics Simulator (WFDS) [25,34]. WFDS is a 3D full physical simulator developed at the U.S. National Institute of Standards and Technology (NIST).…”

Section: Wfdsmentioning

confidence: 99%

Entropy Generation Analysis of Wildfire Propagation

Guelpa

Verda

2017

Entropy

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Entropy generation is commonly applied to describe the evolution of irreversible processes, such as heat transfer and turbulence. These are both dominating phenomena in fire propagation. In this paper, entropy generation analysis is applied to a grassland fire event, with the aim of finding possible links between entropy generation and propagation directions. The ultimate goal of such analysis consists in helping one to overcome possible limitations of the models usually applied to the prediction of wildfire propagation. These models are based on the application of the superimposition of the effects due to wind and slope, which has proven to fail in various cases. The analysis presented here shows that entropy generation allows a detailed analysis of the landscape propagation of a fire and can be thus applied to its quantitative description.

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“…Canham et al (1999) used cylinders whose radii were the average of the two longest perpendicular radii of the outermost crown projection to model nine species (both coniferous and deciduous). Mell et al (2009) used cones whose diameters were based on the furthest extent of branch tips at the bottom of the crown (not necessarily the absolute lowest branches) to model tree-farm grown P. menziesii. Mawson et al (1976) used cones based on the radius at the bottom of the crown, but noted that along the vertical extent of the crown, the widths of the actual trees exceeded the extent of the modeled profile.…”

Section: Crown Profile Modelingmentioning

confidence: 99%

Conifer crown profile models from terrestrial laser scanning

Ferrarese¹,

Affleck²,

Seielstad³

2015

Silva Fenn.

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• Crown models are derived from terrestrial laser data for 3 NW USA conifer species.• Crown models require only crown length for implementation.• Beta and Weibull curves fit to 95th percentile widths describe crown extent.• Crown profile curves are species-specific and not interchangeable.• Crown shape is not strongly conditioned by tree size or site. AbstractRegional crown profile models were derived for three conifer species of the interior northwestern USA from terrestrial laser scans of eighty-six trees across a range of sizes and growing conditions. Equations were developed to predict crown shape from crown length for Pseudotsuga menziesii, Pinus ponderosa, and Abies lasiocarpa from parametric curves applied to crown-length normalized laser point clouds. The 95th width percentile adequately described each crown's outer limit; alternate width percentiles produced little profile shape variation. For P. menziesii and P. ponderosa, a scaling parameter-modified beta curve gave the most accurate fit (using cross-validated Mean Absolute Error) to aggregated 95th width percentile points. For A. lasiocarpa, beta and Weibull curves (equivalently modified) produced similar results. For all species, modified beta and Weibull curves fit crown points with less error than conic or cylindrical profiles. Crown profile curves were species-specific; interchanging among species increased error significantly. Laser-derived crown base metrics provided objectivity and consistency, but underestimated field-derived base heights through inclusion of dead branches. Profile curve parameters were not correlated with tree or stand characteristics suggesting that crown shape is not strongly conditioned by size and site factors. However, laser sampling necessarily favored more open growing conditions, potentially under-representing variations in crown shape associated with social position. Overall, Terrestrial Laser Scanning (TLS) lends itself to detailed measurements of external crown architecture with occlusion-imposed limits to characterization of internal features. Yet, the time and cost of collecting and processing individual tree data precludes use of TLS as a common field sampling tool.

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Numerical simulation and experiments of burning douglas fir trees

Cited by 235 publications

References 32 publications

Some Requirements for Simulating Wildland Fire Behavior Using Insight from Coupled Weather—Wildland Fire Models

Some Requirements for Simulating Wildland Fire Behavior Using Insight from Coupled Weather—Wildland Fire Models

Entropy Generation Analysis of Wildfire Propagation

Conifer crown profile models from terrestrial laser scanning

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