锥形量热计下胶合板受热流影响的热解模拟

T. Fateh, F. Richard, T. Rogaume
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引用次数: 6

摘要

本文基于固体质量损失率(MLR)模型对胶合板的热分解进行了研究。本文采用的多尺度方法允许首先在小尺度上建立固体热分解过程中的动力学机制,然后在更大尺度上对其进行验证。在小尺度上,利用热重分析(TGA)和红外光谱(FTIR)技术,在氮气和空气中进行了5种加热速率下的气体分析。热重分析结果也被用来提出样品热分解的动力学机制。采用遗传算法(GA)优化技术对不同鉴定反应的动力学参数进行了估计。质量损失率模型的预测结果与实验数据吻合较好。在更大的范围内,在一个锥形量热仪中进行了实验,并在空气气氛下进行了FTIR气体分析。采用TGA尺度下建立的热解模型对锥形量热计实验进行了数值模拟,并考虑了样品的传热建模。在TGA尺度上确定的凝聚相物种(由原始材料热分解得到的产物)的热性能(例如导热系数和比热容)在入射热通量为30 kW.m -2的情况下,使用与小尺度上使用的相同的优化技术:GA方法进行了估计。同样的工作也用一种更简单的众所周知的用于炭化材料的热解模型进行了。将所得结果与第一种机制的结果进行了比较,以说明模型的复杂性对胶合板热分解预测的影响。两种热解模型的传热模型保持一致。只有确定的缩合相反应的数目和种类不同。详细机制(5个步骤)在质量损失率预测方面优于简单机制(3个步骤),而在样品背面温度预测方面则较差。然而,对于缩合相物质质量分数的预测,三步模型给出了不切实际的结果。事实上,三步模型预测胶合板在试验结束时没有完全燃烧(对于30 kW.m -2),这在实验中没有观察到。
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Modeling of the pyrolysis of plywood exposed to heat fluxes under cone calorimeter
In this paper, the thermal decomposition of plywood is investigated based on the solid mass loss rate (MLR) modeling. The multi-scale approach followed here allows first to establish, at a small scale, the kinetic mechanism during the solid thermal decomposition and then validate it at a larger scale. At small scale, experiments were conducted by using Thermo-gravimetric analysis (TGA) coupled to gas analysis with the FTIR technique under nitrogen and air atmospheres for five heating rates. Thermo-gravimetric results were also used to propose a kinetic mechanism for the thermal decomposition of the sample. The kinetic parameters of the different identified reactions were estimated by using an optimization technique, namely the Genetic Algorithms (GA) method. The mass loss rate model predictions show a good agreement with the experimental data. At a larger scale, experiments were carried out in a cone calorimeter coupled to FTIR gas analysis under air atmosphere. The pyrolysis model developed at the TGA scale was used in numerical simulations of cone calorimeter experiments taking into account the heat transfer modeling into the sample. The thermal properties (e.g. thermal conductivity and specific heat capacity) of the condensed phase species (which are products given by the thermal decomposition of the virgin material) identified at the TGA scale were estimated for an incident heat flux of 30 kW.m -2 with the same optimization technique used at the small scale: the GA method. The same work has also been conducted with a simpler well known pyrolysis model developed for charring materials. The results have been compared to those of the first mechanism in order to show the influence of the complexity of the model on the prediction of the thermal decomposition of plywood. The heat transfer model was kept the same for both pyrolysis models. Only the number of identified condensed phase reactions and species is different. The detailed mechanism (5 steps) gives better results than the simpler one (3steps) concerning the mass loss rate prediction and worse results for the temperature prediction of the back surface of the sample. However the 3 steps model gives unrealistic results concerning the prediction of the condensed phase species mass fractions. In fact, the 3 steps model predicts that plywood is not fully burned at the end of the test (for 30 kW.m -2 ) which was not observed in the experiment.
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