Prediction model for self-similar propagation and blast wave generation of premixed flames

Prediction model for self-similar propagation and blast wave generation of premixed flames
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DOI:
10.1016/j.ijhydene.2015.06.123
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发表时间:
2015-09
影响因子:
7.2
通讯作者:
Wookyung Kim;T. Mogi;K. Kuwana;R. Dobashi
Wookyung Kim;T. Mogi;K. Kuwana;R. Dobashi
中科院分区:
工程技术2区
文献类型:
--
作者:
Wookyung Kim;T. Mogi;K. Kuwana;R. Dobashi

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本文提出了一种预测无底限瓦斯爆炸过程中火焰速度和爆炸压力的简单模型。该模型是对Gostintsev等人提出的分形模型的改进。在原始模型中,火焰半径r作为时间t的函数表示为r/(κ/ε S L)= c g [t/(κ/ε 2 S L 2)] α,其中κ为热扩散系数,ε为体积膨胀比,S L为层流燃烧速度,c g为模型常数,α为加速度指数。该模型利用混合气体的性质来表示模型常数c g。在本研究中,对氢气/空气、甲烷/空气和丙烷/空气的混合物进行了气体爆炸的现场实验,这些混合物被限制在1或27立方米的正立方塑料帐篷中。实验结果证明了爆炸的自相似性质,并对实验加速度指数与分形维数的关系进行了评价。该模型采用自相似概念和声学理论建立。将预测的火焰速度和爆炸压力与大范围条件下的氢气/空气、甲烷/空气和丙烷/空气爆炸实验数据进行了比较。模型预测结果与实验数据吻合较好,验证了模型的有效性。
This paper presents a simple model to predict the flame speed and the blast pressure during an unconfined gas explosion. The proposed model is a modification to the fractal-based model proposed by Gostintsev et al. In the original model, the flame radius, r, is expressed as a function of time, t, as r/(κ/ε S L)= c g [t/(κ/ε 2 S L 2)] α, where κ is the thermal diffusivity, ε is the volumetric expansion ratio, S L is the laminar burning velocity, c g is the model constant, and α is the acceleration exponent. The present model expresses model constant c g using the properties of gas mixture. In this study, field experiments of gas explosion are conducted for hydrogen/air, methane/air, and propane/air mixtures confined in a 1-or 27-m 3 regular cubic plastic tent. The experimental results demonstrate the nature of self-similarity in the explosions and the experimental acceleration exponent associated with a fractal dimension is evaluated. The model is developed by using the concept of self-similarity and an acoustic theory. The predicted flame speed and the blast pressure are compared with experimental data of larger-scale hydrogen/air, methane/air and propane/air explosions under a wide range of conditions. The model predictions agree reasonably well with the experimental data, validating the proposed model.