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An adaptive small-world network framework for fire spread modeling
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DOI：10.1016/j.firesaf.2026.104910.png)
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Abstract
En 中文
The small-world network (SWN) has been adapted for wildland fire spread prediction by incorporating heat transfer, ignition, and combustion dynamics. However, previous studies have not considered the effects of time step and cell resolution on simulation results. This work proposes an adaptive small-world network (ASWN) framework for fire spread modeling. The adaptive cell resolution method dynamically refines the local preheating cells around burning cells. This helps the model identify fuel ignition at the sub-cell scale. The adaptive time step method dynamically adjusts the time step. This aligns the transition time of preheating cells to the burning state with the actual physical ignition time. The ASWN framework is validated using laboratory-scale upslope fire spread experiments. The results show that the ASWN accurately reproduces the fire front shape. Compared to the SWN, the ASWN performs better in terms of simulation accuracy and efficiency. The average relative error of the ASWN for the upslope fire spread rate is less than 15%. Moreover, sensitivity analysis indicates that uncertainties in flame temperature and combustion time of the burning cell significantly affect the predicted rate of fire spread, especially under slow spread conditions.
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