论文标题
量化稀有事件的发现:野火传播了多远?
Quantifying rare events in spotting: How far do wildfires spread?
论文作者
论文摘要
发现发现的是通过风将燃烧的火力运输到降落时,可能会在主要火灾的直接点火区域上点燃新大火。远离原始燃烧单元的射击火灾很少见,但由于其预测和控制仍然具有挑战性,因此产生了巨大的影响。为了促进他们的预测,我们研究了三种量化火焰的着陆分布的方法:粗蒙特卡洛模拟,重要性采样和大偏差理论(LDT)。特别是,我们提出了一种LDT方法,该方法可以准确和脱颖而出地量化着陆分布尾部的低概率事件。相反,蒙特卡洛和重要性采样方法在量化分布模式附近的高概率着陆距离方面最有效。但是,由于所需的样本量较大,它们在计算上很难量化分布的尾部。我们还表明,最可能的着陆距离随风场的平均特征速度线性增长。此外,将相对降落的质量定义为质量的比例,降落在距主要火灾的给定距离处,我们得出了一个明确的公式,该公式允许以微不足道的计算成本计算该数量作为着陆分布的函数。我们在数值上证明了我们在两个规定的风场上的发现。
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.