Spread vector induced cellular automata model for real-time crown fire behavior simulation. (October 2018) - Liu, Yuxuan; Liu, Hu; Zhou, Yaoming; Sun, Cong
Abstract: Cellular automata (CA) approaches in wildfire modelling are computationally more efficient than vector-based methods. Despite this advantage, these methods suffer from the lack of physical basis and distortion in simulated fire shapes. To overcome these drawbacks and to perform a real-time interactive crown fire simulation for training use, a new CA model is proposed in which a new spread mechanism is introduced based on the physical equations of reaction and radiation. Then, through a series of crown fire simulations in uniform and heterogeneous synthetic fuel beds, the functional validation of the model and the sensitivity analyses on vegetation parameters are performed. Finally, a case study is discussed to illustrate the application of the model with real-world data. Without any mathematical correction, this method is intrinsically free from the distortion problems found in several CA approaches. Highlights: Simulation of spatio-temporal evolution of fire bands with more simulated fire properties. Improvement in simulated fire shape distortions without any mathematical correction. Model can be integrated in real-time fire-fighting training systems to perform interactive crown fire simulations. Adaptibility and extendibility of the model framework with more complete and precise physical equations.