Mechanical and Civil Engineering Seminar

PhD Thesis Defense

Abstract: Granular materials are ubiquitous in both nature and technology. They play a key role in applications ranging from storing food and energy, to building reusable habitats and soft robots. Yet, predicting the continuum mechanical response of granular materials continues to present extraordinary challenges, despite the apparently simple laws that govern particle-scale interactions. This is largely due to the complex history dependence arising from the continuous rearrangement of their internal structure, and the nonlocality emerging from their self-organization. There is an urge to develop methods that adequately address these two aspects, while bridging the long-standing divide between the grain- and the continuum scale.

This dissertation introduces novel theoretical and computational approaches for behavior prediction in granular solids. To begin with, we develop a framework for investigating their incremental behavior from the perspective of plasticity theory. It relies on systematically probing, through level-set discrete element calculations, the response of granular assemblies from the same initial state to multiple directions is stress space. We then extract the state- and history-dependent elasticity and plastic flow, and investigate the evolution of pertinent internal variables. Next, inspired by the abundance of generated high-fidelity micromechanical data, we develop an alternative data-driven approach for behavior prediction. This new multiscale modeling paradigm completely bypasses the need to define a constitutive law. Instead, the problem is directly formulated on a material data set, generated by grain-scale calculations, while pertinent constraints and conservation laws are enforced. We particularly focus on the sampling of the mechanical phase space, and develop two methods for parametrizing material history, one thermodynamically motivated and one statistically inspired. In the remainder of the thesis, we direct our attention to the understanding and modeling of nonlocality. In particular, we first delve into a complex network analysis of a granular assembly undergoing shear banding, and study the self-organized and cooperative evolution of topology, kinematics and kinetics within the band. We characterize the evolution of fundamental topological structures called force cycles, and propose a novel order parameter for the system, the minimal cycle coefficient. We also analyze the statistics of nonaffine kinematics, which involve rotational and vortical particle motion. Finally, inspired by these findings, we extend the previously introduced data-driven paradigm to include nonaffine kinematics within a weakly nonlocal micropolar continuum description. By formulating the problem on a phase space augmented by higher-order kinematics and their conjugate kinetics, we bypass for the first time the need to define an internal length scale, which is instead discovered from the data. By carrying out a data-driven prediction of shear banding, we find that this nonlocal extension of the framework resolves the ill-posedness inherent to the classical continuum description. Our theoretical developments are finally validated by comparing with available experimental data.

Please virtually attend this thesis defense:

Zoom Link: https://caltech.zoom.us/j/86098882755?pwd=ZUlHcTk0Q0NkUHdidUNEUUdjamJkUT09