کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5027465 | 1470628 | 2017 | 12 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A Probabilistic Cellular Automata Framework for Assessing the Impact of WUI Fires on Communities A Probabilistic Cellular Automata Framework for Assessing the Impact of WUI Fires on Communities](/preview/png/5027465.png)
The 'wildland-urban interface' (WUI) is a term commonly used to describe areas where wildfires and the built environment have the potential to interact resulting in loss of properties and potential loss of life. Significant residential losses associated with wildland interface fires have occurred worldwide in recent years and substantial research has been conducted on developing numerical models of ignition due to convection and ember attacks. These studies provide substantial insight into the behaviour and growth of wildland fires, which have been further utilized to build fire exposure rating of structures. The FireWise program in the United States and the FireSmart manual in Canada are two key examples of provisions developed for determining fire exposure ratings for a structure. While previous studies provide significant contribution to modelling fire propagation, a much more comprehensive model is required, which would encompass all the key variables associated with WUI fires. This paper aims at extending previously conducted efforts by developing a simulation-based model. A typical fire propagation simulation requires solving the coupled fluid-thermal differential equations which results in extreme run times making it unsuitable for general purposes, however the model in this study utilizes theory of cellular automata, which reduces the processing times substantially by simplifying the underlying equations involved. Cellular automata utilize a specific set of rules to model propagation by convection as well as ember travel. In addition, the model also considers key parameters such as humidity, nature of vegetation and topology while evaluating the propagation paths. Due to the flexible nature of the model its accuracy can be tuned to a certain extent by optimizing the propagation rules using real-event data
Journal: Procedia Engineering - Volume 198, 2017, Pages 1111-1122