Effective thermal diffusivity models are useful for predicting thermal diffusivities of heterogeneous materials. The literature contains models that may be broadly categorised into four different types: (1) effective thermal diffusivity for highly specific applications (e.g. empirical curve fitting of measured data); (2) effective thermal diffusivity as a weighted averages of the components’ thermal diffusivities and volume fractions; (3) effective thermal diffusivity calculated from effective thermal conductivity, effective density and effective specific heat capacity known as the ‘lumped parameter’ approach (which is the most commonly employed method); (4) comparison of times for a fixed quantity of heat to be transferred to a composite material with the heat transfer time for a material with an effective thermal diffusivity. The latter three modelling methods were tested on theoretical composite materials, and none performed consistently better than the others, suggesting there is scope for further work in this area. Of the three methods, the least accurate on average was the lumped parameter method. Given that this relationship is often used to derive thermal conductivity data from thermal diffusivity data (or vice versa), it is possible that significant error is introduced to the derived property in addition to any measurement error, which is often not acknowledged.

有效的热扩散率模型可用于预测异质材料的热扩散率。文献包含的模型可大致分为四种不同类型： (1) 用于高度特定应用的有效热扩散率（例如测量数据的经验曲线拟合）；(2) 有效热扩散率作为组件热扩散率和体积分数的加权平均值；(3) 由有效热导率、有效密度和有效比热容计算的有效热扩散率，称为“集总参数”方法（这是最常用的方法）；(4) 将固定热量传递到复合材料的时间与具有有效热扩散率的材料的传热时间进行比较。后三种建模方法在理论复合材料上进行了测试，没有一种方法的性能始终优于其他方法，这表明该领域还有进一步工作的空间。在这三种方法中，平均而言最不准确的是集中参数法。鉴于这种关系通常用于从热扩散率数据中导出热导率数据（反之亦然），除了任何通常不被承认的测量误差外，可能还会在导出的属性中引入显着误差。