Abstract Shape-anisotropic granules and their surrounding interphase networks are significant constituents in granular materials. Structural and physical configurations of these constituents significantly affect the overall thermal performance of granular materials, specifically microstructure-dependent thermal conductive properties. It has been a key but unresolved issue how to quantitatively understand the microstructure evolution of conductive interphase interacted by densely packed non-spherical particles triggering the change of thermal conductivity of granular materials. In this work, we devise a powerful scheme by using the discrete element method (DEM) and the dual-probability-Brownian motion simulation (DP-BMS) to accurately and efficiently predict the effective thermal conductivity of granular materials composed of homogeneous matrix, conductive (soft) interphase around randomly-dispersed elliptical particles over a broad range of aspect ratios with widespread applications, such as cracks, pores, fibers, cellulose whiskers, silicate nanorods, and aggregates. Comparison against extensive numerical and theoretical data validates that such the scheme can well predict the effective thermal conductivity of multiphase granular materials with densely packed non-spherical particles that is just the intrinsic limitation of the classical micromechanical homogenization theories. In this scheme, the DEM provides a direct means of investigating the time-dependent microstructure evolution of granular materials with elliptical particles from a loose parking state to a dense packing state. The DP-BMS provides an effective technique for predicting the effective thermal conductive transport properties of multiphase granular materials. By comparing with traditional numerical strategies like the finite element method (FEM) and random walk model (RWM), the DP-BMS is more user-friendly and efficient to accurately predict the effective thermal conductivity. This scheme can be regarded as a general procedure that is readily applicable to predictions of other transport properties of two-dimensional or three-dimensional multiphase granular materials. Furthermore, we use the scheme to probe the influences of the shape and high packing density of particles and the thickness and fraction of soft interphase on the effective thermal conductivity of granular materials. The results elucidate rigorous component-structure–property relations, which can provide sound guidance for composite design and microstructure optimization.

中文翻译：

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DEM 和双概率-布朗运动方案用于具有密集非球形颗粒和软界面网络的多相颗粒材料的热导率

摘要 形状各向异性颗粒及其周围的界面网络是颗粒材料的重要组成部分。这些成分的结构和物理配置显着影响颗粒材料的整体热性能，特别是与微观结构相关的导热性能。如何定量理解致密填充的非球形颗粒相互作用导致颗粒材料导热系数变化的导电界面的微观结构演变一直是一个关键但尚未解决的问题。在这项工作中，我们通过使用离散元法 (DEM) 和双概率布朗运动模拟 (DP-BMS) 设计了一个强大的方案，以准确有效地预测由均质基体组成的颗粒材料的有效热导率，在广泛的纵横比范围内随机分散的椭圆形颗粒周围的导电（软）界面，具有广泛的应用，例如裂缝、孔、纤维、纤维素晶须、硅酸盐纳米棒和聚集体。与大量数值和理论数据的比较证实，该方案可以很好地预测具有密集堆积的非球形颗粒的多相颗粒材料的有效热导率，这正是经典微机械均化理论的内在局限性。在该方案中，DEM 提供了一种直接方法来研究具有椭圆形颗粒的粒状材料从松散停放状态到密集堆积状态的随时间变化的微观结构演变。DP-BMS 提供了一种有效的技术来预测多相颗粒材料的有效导热传输特性。通过与有限元法（FEM）和随机游走模型（RWM）等传统数值策略相比，DP-BMS在准确预测有效热导率方面更加人性化和高效。该方案可以被视为一种通用程序，它很容易适用于二维或三维多相颗粒材料的其他输运特性的预测。此外，我们使用该方案探讨了颗粒的形状和高堆积密度以及软界面的厚度和分数对颗粒材料有效导热系数的影响。结果阐明了严格的成分-结构-性质关系，