To enable intelligent design of fire resistant polymeric materials, it is highly important to have a quantitative understanding of the relation between the composition of these materials and chemical and physical properties that control the process of fire growth. This paper presents a methodology that provides a capability to determine the core subset of these properties including the kinetics and thermodynamics of the thermal decomposition of the condensed-phase constituents and enthalpy of combustion of gaseous pyrolyzates. This methodology is based on three experimental techniques – Thermogravimetric Analysis, Differential Scanning Calorimetry and Microscale Combustion Calorimetry – and inverse numerical modeling of the results of these tests. The material system analyzed in this study is polyamide 66 reinforced with chopped glass fiber and flame retarded with red phosphorous. This system shows a complex decomposition behavior that is highly dependent on the phosphorus concentration. First, a semi-global thermal decomposition mechanism consisting of a set of first- and second-order (two components) reactions was developed using three material specimens containing different phosphorus concentrations. Subsequently, the extrapolative power of the developed reaction model was demonstrated by accurately predicting the experimental measurements obtained for several compositions not used in the model development process.

为实现耐火聚合物材料的智能设计，对这些材料的成分与控制火势增长过程的化学和物理特性之间的关系进行定量了解非常重要。本文介绍了一种方法，该方法提供了确定这些性质的核心子集的能力，包括冷凝相成分热分解的动力学和热力学以及气态热解产物的燃烧焓。该方法基于三种实验技术-热重分析，差示扫描量热法和微尺度燃烧量热法-以及这些测试结果的逆数值建模。在这项研究中分析的材料系统是用短切玻璃纤维增​​强的聚酰胺66和用红磷阻燃的聚酰胺66。该系统显示出高度依赖于磷浓度的复杂分解行为。首先，使用包含不同磷浓度的三个材料样本，开发了由一组一阶和二阶（两个组分）反应组成的半全局热分解机理。随后，通过准确预测针对模型开发过程中未使用的几种组合物获得的实验测量值，证明了开发的反应模型的外推能力。使用包含不同磷浓度的三个材料样本，开发了由一组一阶和二阶（两个组分）反应组成的半全局热分解机理。随后，通过准确预测针对模型开发过程中未使用的几种组合物获得的实验测量值，证明了开发的反应模型的外推能力。使用包含不同磷浓度的三个材料样本，开发了由一组一阶和二阶（两个组分）反应组成的半全局热分解机理。随后，通过准确预测对模型开发过程中未使用的几种组合物的实验测量值，证明了开发的反应模型的外推能力。