scholarly journals A FIRE SPREAD MODEL BASED ON EXPERIMENTS CONSIDERING RANDOM DISTRIBUTION OF COMBUSTIBLES

2005 ◽  
Vol 70 (594) ◽  
pp. 1-6 ◽  
Author(s):  
Yoshikazu DEGUCHI ◽  
Mamoru KOHNO ◽  
Makoto TSUJIMOTO ◽  
Tatsuhiro GOTO
Keyword(s):  
Random Distribution ◽  
Fire Spread ◽  
Model Based ◽  
Spread Model ◽  
Fire Spread Model

Forest fire spread model based on the grey system theory

2018 ◽  
Vol 76 (5) ◽  
pp. 3602-3614 ◽  
Author(s):  
Chundong Lv ◽  
Jia Wang ◽  
Fanfei Zhang
Keyword(s):  
System Theory ◽  
Forest Fire ◽  
Fire Spread ◽  
Grey System Theory ◽  
Grey System ◽  
Model Based ◽  
Spread Model ◽  
Fire Spread Model

Modeling and Estimating Post-Earthquake Fire Spread

2012 ◽  
Vol 28 (2) ◽  
pp. 795-810 ◽  
Author(s):  
Geoff Thomas ◽  
David Heron ◽  
Jim Cousins ◽  
Mairéad de Róiste
Keyword(s):  
Fire Spread ◽  
Spontaneous Ignition ◽  
Broken Windows ◽  
Conditional Probabilities ◽  
Existing Buildings ◽  
Spread Model ◽  
Building Information ◽  
Piloted Ignition ◽  
Further Development ◽  
Fire Spread Model

This paper describes the development of a GIS-based dynamic fire-spread model, with seven distinct modes of fire spread: direct contact, spontaneous ignition of claddings, piloted ignition of claddings, spontaneous ignition through windows, piloted ignition through broken windows, fire spread via non-fire-rated roofs and branding. All except the first two modes include in-built probabilities, but these can be selected individually and given user-defined values. Fire spread modes can be added to the model or altered to suit available building information. Critical details of buildings are obtained from an existing-buildings database, street surveys, or deduced using conditional probabilities from available data. Results show that comparison with actual fires is reasonable. The model could be extended with further development for use as a real time firefighting tool.

scholarly journals Coupled atmosphere-wildland fire modeling with WRF-Fire version 3.3

2011 ◽  
Vol 4 (1) ◽  
pp. 497-545 ◽  
Author(s):  
J. Mandel ◽  
J. D. Beezley ◽  
A. K. Kochanski
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Wildland Fire ◽  
Surface Wind ◽  
Numerical Algorithms ◽  
Fire Spread ◽  
Weather Research And Forecasting ◽  
Time Step ◽  
Spread Model ◽  
Fire Spread Model

Abstract. We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various kinds of ignition. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.

Fire behaviour in wheat crops – effect of fuel structure on rate of fire spread

2020 ◽  
Vol 29 (3) ◽  
pp. 258 ◽  
Author(s):  
Miguel G. Cruz ◽  
Richard J. Hurley ◽  
Rachel Bessell ◽  
Andrew L. Sullivan
Keyword(s):  
Experimental Study ◽  
Prediction Models ◽  
Fire Spread ◽  
Propagation Rate ◽  
Forward Rate ◽  
Fire Behaviour ◽  
Natural Grass ◽  
Spread Model ◽  
Crop Condition ◽  
Fire Spread Model

A field-based experimental study was conducted in 50×50m square plots to investigate the behaviour of free-spreading fires in wheat to quantify the effect of crop condition (i.e. harvested, unharvested and harvested and baled) on the propagation rate of fires and their associated flame characteristics, and to evaluate the adequacy of existing operational prediction models used in these fuel types. The dataset of 45 fires ranged from 2.4 to 10.2kmh−1 in their forward rate of fire spread and 3860 and 28000 kWm−1 in fireline intensity. Rate of fire spread and flame heights differed significantly between crop conditions, with the unharvested condition yielding the fastest spreading fires and tallest flames and the baled condition having the slowest moving fires and lowest flames. Rate of fire spread in the three crop conditions corresponded directly with the outputs from the models of Cheney et al. (1998) for grass fires: unharvested wheat → natural grass; harvested wheat (~0.3m tall stubble) → grazed or cut grass; and baled wheat (<0.1m tall stubble) → eaten-out grass. These models produced mean absolute percent errors between 21% and 25% with reduced bias, a result on par with the most accurate published fire spread model evaluations.

FIRE SPREAD MODEL FOR A LINEAR FRONT IN A HORIZONTAL SOLID POROUS FUEL BED IN STILL AIR

2004 ◽  
Vol 176 (2) ◽  
pp. 135-182 ◽  
Author(s):  
G. C. VAZ ◽  
J. C. S. ANDRÉ ◽  
D. X. VIEGAS
Keyword(s):  
Fire Spread ◽  
Spread Model ◽  
Fire Spread Model

scholarly journals Fire spread model for old towns based on cellular automaton

2008 ◽  
Vol 13 (5) ◽  
pp. 736-740 ◽  
Author(s):  
Nan Gao ◽  
Wenguo Weng ◽  
Wei Ma ◽  
Shunjiang Ni ◽  
Quanyi Huang ◽  
...  
Keyword(s):  
Cellular Automaton ◽  
Fire Spread ◽  
Spread Model ◽  
Fire Spread Model

Sensitivity of a surface fire spread model and associated fire behaviour fuel models to changes in live fuel moisture

2007 ◽  
Vol 16 (4) ◽  
pp. 503 ◽  
Author(s):  
W. Matt Jolly
Keyword(s):  
Fire Spread ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Surface Fire ◽  
Fuel Model ◽  
Spread Model ◽  
Fuel Models ◽  
Live Fuel Moisture ◽  
Behaviour Models ◽  
Fire Spread Model

Fire behaviour models are used to assess the potential characteristics of wildland fires such as rates of spread, fireline intensity and flame length. These calculations help support fire management strategies while keeping fireline personnel safe. Live fuel moisture is an important component of fire behaviour models but the sensitivity of existing models to live fuel moisture has not been thoroughly evaluated. The Rothermel surface fire spread model was used to estimate key surface fire behaviour values over a range of live fuel moistures for all 53 standard fuel models. Fire behaviour characteristics are shown to be highly sensitive to live fuel moisture but the response is fuel model dependent. In many cases, small changes in live fuel moisture elicit drastic changes in predicted fire behaviour. These large changes are a result of a combination of the model-calculated live fuel moisture of extinction, the effective wind speed limit and the dynamic load transfer function of some of the fuel models tested. Surface fire spread model sensitivity to live fuel moisture changes is discussed in the context of predicted fire fighter safety zone area because the area of a predicted safety zone may increase by an order of magnitude for a 10% decrease in live fuel moisture depending on the fuel model chosen.

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