Real-time wildland fire spread modeling using tabulated flame properties

被引:11
|
作者
de Gennaro, Matthieu [1 ,2 ]
Billaud, Yann [3 ]
Pizzo, Yannick [2 ]
Garivait, Savitri [4 ]
Loraud, Jean-Claude [2 ]
El Hajj, Mahmoud [1 ]
Porterie, Bernard [2 ]
机构
[1] NOVELTIS, 153 Rue Lac, Labege, France
[2] Aix Marseille Univ, CNRS, IUSTI, UMR 7343, Marseille, France
[3] Univ Poitiers, CNRS, ENSMA, Inst Pprime, Poitiers, France
[4] King Mongkuts Univ Technol Thonburi, JGSEE, Bangkok, Thailand
关键词
Wildfires; Tabulation; Genetic algorithm; Real-time simulation; Front-tracking method; SIMULATING FIRE; FOREST-FIRE; PREDICTION;
D O I
10.1016/j.firesaf.2017.03.006
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper is an extension of previous papers [1,2] on a raster-based fire spread model which combines a network model to represent vegetation distribution on land and a physical model of the heat transfer from burning to unburnt vegetation items, and takes into account local conditions of wind, topography, and vegetation. The physical model, still based on the unsteady energy conservation in every fuel element and detailed local and non-local heat transfer mechanisms (radiation from the flaming zone and embers; surface convection, and radiative cooling from the heated fuel element to the environment), now includes wind-driven convection through the fuel bed. To address the challenge of real-time fire spread simulations, the model is also extended in two ways. First, the Monte Carlo method is used in conjunction with a genetic algorithm to create a database of radiation view factors from the flame to the fuel surface for a wide variety of flame properties and environment conditions. Second, the front-tracking method, drafted in [2], is extended to polydisperse networks and implemented in the new version of the model, called SWIFFT. Finally, the SWIFFT model is validated against data from different fire scenarios, showing it is capable of capturing the trends observed in experiments in terms of rate of spread, and area and shape of the burn, with reduced computational resources.
引用
收藏
页码:872 / 881
页数:10
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