木粉尘最小点火能的理论建模

于莉莉; 周曦禾; 贾敬胜; 夏常磊; 喻孜

doi:10.12326/j.2096-9694.2025055

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专辑

木粉尘最小点火能的理论建模
Theoretical Modeling of the Minimum Ignition Energy of Wood Dust
1.南京林业大学理学院，江苏南京 210037
2.南京林业大学材料科学与工程学院，江苏南京 210037
2025年39卷第5期页码：32-38
DOI： 10.12326/j.2096-9694.2025055
稿件说明：

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于莉莉,周曦禾,贾敬胜等.木粉尘最小点火能的理论建模[J].木材科学与技术,2025,39(05):32-38. DOI： 10.12326/j.2096-9694.2025055.

YU Lili,ZHOU Xihe,JIA Jingsheng,et al.Theoretical Modeling of the Minimum Ignition Energy of Wood Dust[J].Chinese Journal of Wood Science and Technology,2025,39(05):32-38. DOI： 10.12326/j.2096-9694.2025055.

摘要

针对现有木粉尘最小点火能（minimum ignition energy，MIE）模型普遍存在未耦合燃烧动力学与热传导过程、预测精度不足等问题，提出一种“球层能量迭代模型”。该模型将点火空间划分为多个球层，各层粉尘在短时间内依次燃烧，形成爆炸传播过程。模型采用阿伦尼乌斯（Arrhenius）公式计算燃烧反应时间，并考虑层间热传导的能量平衡，通过逐层迭代计算点火源所需补充的能量，从而确定MIE。模型在0~500 μm粒径和0.25~2.0 g/L浓度范围内对中密度纤维板粉尘的MIE进行了计算，与BS EN 13821-2002标准下的实验数据（文献［

］）对比，结果显示：相较于传统机理模型高达60%的偏差（文献［

］），本研究模型计算的理论值与实验数据的平均偏差仅为4%，最大偏差为7%，验证模型在精度与适用性方面的优越性。

Abstract

To address the limitations of existing theoretical models for predicting the minimum ignition energy (MIE) of wood dust

such as the lack of coupling between combustion dynamics and heat transfer and the insufficient prediction accuracy

this study proposes a “Spherical Layer Energy Iteration Model”. In the model

the ignition space is divid

ed into multiple spherical layers

assuming that the dust in each layer burns continuously within a short period to form an explosion propagation process. By introducing the Arrhenius equation to calculate the combustion reaction duration and incorporating interlayer heat conduction energy balance

the model iteratively computes the supplementary energy required from the ignition source layer by layer

ultimately obtaining the MIE. The model was applied to calculate the MIE of medium density fiberboard wood dust with particle size between 0~500 μm and mass concentration from 0.25~2.0 g/L. When compared with experimental data obtained in accordance with the BS EN 13821-2002 standard (Ref. [

]

)

the proposed model exhibited an average deviation of only 4% and a maximum deviation of 7%

whereas traditional mechanistic models showed deviations of about 60% (Ref. [

]

). This outcome validates the model’s significant improvement in accuracy and applicability.

关键词

Keywords

references

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