Abstract As the fire research community develops multi-scale models to predict the spread of wildland fires, there is a need for well-controlled, small-scale experiments which provide benchmark data and fundamental understanding of the effects of key parameters on the combustion of wildland fuels. Here, we implement a unique suite of non-intrusive, quantitative diagnostics that simultaneously measure mass loss, surface temperature, in situ gas temperature and water vapor concentration, including the first implementation of dual frequency comb spectroscopy for the study of solid fuels. As a demonstration, we examine the effect of fuel moisture content on the burning of Douglas fir wood samples that are ignited under constant radiative heat flux from a cone calorimeter to provide benchmark data for multi-scale models. Peak flame temperatures 3 mm above the sample surface measured with the dual frequency comb laser spectrometer are in excess of 1200 K. The flame temperature decreases by over 150 K as fuel moisture content increases from 0.7% to 40%. This work demonstrates a platform for simultaneous, quantitative measurements of well-controlled small-scale experiments which provide benchmark data for the development of advanced modeling and simulations capable of capturing the complex physics present in wildland fire systems.

中文翻译：

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固体燃料热解和燃烧的双频梳光谱：量化花旗松中水分含量的影响

摘要 随着火灾研究界开发多尺度模型来预测野地火灾的蔓延，需要控制良好的小规模实验，以提供基准数据和对关键参数对野地燃烧的影响的基本理解。燃料。在这里，我们实施了一套独特的非侵入式定量诊断，可同时测量质量损失、表面温度、原位气体温度和水蒸气浓度，包括首次实施双频梳光谱用于固体燃料研究。作为演示，我们检查了燃料水分含量对花旗松木材样品燃烧的影响，这些样品在来自锥形量热计的恒定辐射热通量下点燃，为多尺度模型提供基准数据。用双频梳状激光光谱仪测量的样品表面上方 3 毫米的峰值火焰温度超过 1200 K。随着燃料水分含量从 0.7% 增加到 40%，火焰温度降低超过 150 K。这项工作展示了一个同步、定量测量控制良好的小规模实验的平台，为开发能够捕捉野地火灾系统中存在的复杂物理的高级建模和模拟提供基准数据。