Methods for Control of Granular Material Attributes

Citation

Buarque de Macedo, Robert Andrew (2023) Methods for Control of Granular Material Attributes. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/1h8f-se14. https://resolver.caltech.edu/CaltechTHESIS:08172022-000450303

Abstract

A granular material is a collection of discrete, solid particles. This substance is ubiquitous in nature and industry, with examples ranging from soils, jointed rocks, foodstuffs, ball bearings, powders, and even asteroids. As such, understanding granular materials is necessary for making sense of the physical world. Tremendous progress has been made in directly simulating granular materials in the previous decades, in particular via the discrete element method (DEM). Nevertheless, there remains ample opportunity for manipulating granular materials to achieve specific outcomes by leveraging the DEM. The research presented in this thesis utilizes DEM simulations to develop tools and strategies for manipulating granular material to achieve desired attributes. These attributes include the shape of individual grains, the structure of granular tunnels, and mesoscopic packing characteristics such as packing fraction and coordination number. Optimization of granular materials is considered at 3 different scales: at the single grain scale (100 grains), at the scale of granular structures such as arches (101 grains), and at the mesoscopic scale (103 grains). The first component of this thesis considers automated design of individual grain shapes that embody user-specified morphological properties via genetic algorithms. Next, excavation in granular materials is considered. It is studied how ants can so successfully manipulate granular materials to achieve stable systems by mapping the forces around real ant tunnels. Ant tunnels are simulated using a DEM which can handle arbitrary shaped grains: the Level-Set Discrete Element Method (LS-DEM). Finally, tools are developed for controlling mesoscopic attributes of granular materials as a function of grain shape. To do so, genetic algorithms and a deep generative model are combined with LS-DEM. The methodologies introduced in this thesis serve as a foundation for controlling granular material attributes. Such techniques can be leveraged to engineer granular materials, with applications ranging from swarm robotics, robotic grippers, mechanically tunable fabrics for armor, and robotic excavation.

Thesis Files