Mechanical Properties of Granular Metamaterials

本项目的目的是预测颗粒状超材料的力学性能。粒状填料由未结合的宏观固体颗粒组成。由球形或略非球形但呈凸状的颗粒组成的颗粒填料已被广泛研究。然而，颗粒填料的力学性能主要取决于单个颗粒之间的局部相互作用，因此也取决于颗粒的形状。因此，与球形和凸形颗粒形状的显著偏差可以诱导颗粒填料迄今未被探索的特性，并提出了一种新的颗粒材料家族，其机械特性可以通过调整构成颗粒的形状来调整：颗粒超材料。近年来，这种复杂形状颗粒的颗粒填料已被研究，例如作为建筑材料。由于颗粒形状的复杂性，单个颗粒的形状与填料宏观力学性能之间的一般本构关系是不可行的。因此，我们计划通过大量的数值模拟来解决这个问题，这些数值模拟将通过模型实验来验证。所需的实验将进行定义的模型粒子和加载几何（剪切和压缩）。为了校准模拟方法，我们将通过x射线计算机断层扫描捕获颗粒填料，然后通过从ct数据中分割单个复杂形状的颗粒来获得颗粒填料的结构、拓扑结构和接触网络。一旦可靠，模拟将用于大量生产各种粒子形状的数据。这些数据将允许训练一个人工神经网络，该网络将输出颗粒状超材料的机械特性，作为构成颗粒形状的函数

The aim of this project is to predict the mechanical properties of granular metamaterials. Granular packings consist of unbound macroscopic, solid particles. Granular packings composed of spherical or slightly non-spherical but convex particles have been widely studied. The mechanical properties of granular packings are, however, mainly defined by the local interactions between the individual particles and, thus, by the shape of the particles. Therefore, significant deviations from spherical and convex particle shapes can induce so far unexplored properties of the granular packings and suggests a new family of granular material whose mechanical properties can be tuned by tailoring the shape of the constituting particles: granular metamaterials. Recently, such granular packings of complex shaped particles have been investigated e.g. as construction material. Due to the complexity introduced by the particle shape, general constitutive relations between the shape of the individual particles and the macro-mechanical properties of the packing is not feasible. We therefore plan to approach the problem by means of extensive numerical simulations which will be validated by model experiments. The required experiments will be performed for defined model particles and loading geometries (shear and compression). To calibrate the simulation methods, we will capture the granular packings by means of X-ray computed tomography and subsequently obtain the structure, the topology and the contact network of the packing by segmenting the individual, complex shaped particles from the CT-data. Once reliable, the simulations will be used for the mass production of data for a wide range of particle shapes. This data will permit to train an artificial neural network that will output the mechanical properties of a granular metamaterial as a function of the shape of the constituting particles