Spotting refers to the transport of burning pieces of firebrand by wind which, at the time of landing, may ignite new fires beyond the direct ignition zone of the main fire. Spot fires that occur far from the original burn unit are rare but have consequential ramifications since their prediction and control remains challenging. To facilitate their prediction, we examine three methods for quantifying the landing distribution of firebrands: crude Monte Carlo simulations, importance sampling, and large deviation theory (LDT). In particular, we propose an LDT method that accurately and parsimoniously quantifies the low probability events at the tail of the landing distribution. In contrast, Monte Carlo and importance sampling methods are most efficient in quantifying the high probability landing distances near the mode of the distribution. However, they become computationally intractable for quantifying the tail of the distribution due to the large sample size required. We also show that the most probable landing distance grows linearly with the mean characteristic velocity of the wind field. Furthermore, defining the relative landed mass as the proportion of mass landed at a given distance from the main fire, we derive an explicit formula which allows computing this quantity as a function of the landing distribution at a negligible computational cost. We numerically demonstrate our findings on two prescribed wind fields.
翻译:点火指燃烧的火花碎片由风燃烧,在着陆时可能会在主火的直接点火区之外点燃新的火灾。远离原燃烧装置的点火十分罕见,但因其预测和控制仍然具有挑战性而产生后果。为了便于预测,我们研究了三种方法,以量化火花斑点的着陆分布:粗蒙特卡洛模拟、重要取样和大偏差理论(LDT)。特别是,我们提议了一种LDT方法,精确和精确地量化着陆分布尾端的低概率事件。相比之下,蒙特卡洛和重要取样方法在量化靠近分布模式的高概率着陆距离方面效率最高。然而,由于所需的大样品大小,它们变得在计算分布的尾部时难以计算。我们还表明,最可能的着陆距离随着风场的平均特征速度而线直线增长。此外,将相对着陆质量定义为从主火的某一距离着陆点降下的重量比例,我们得出一个明确的公式,可以计算出这一数量,作为着陆场上方值的轨道分布的函数,我们以微值计算得出了数字的计算成本。