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Multiscale, Data-Driven and Nonlocal Modeling of Granular Materials

Citation

Karapiperis, Konstantinos (2021) Multiscale, Data-Driven and Nonlocal Modeling of Granular Materials. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/7rtg-x780. https://resolver.caltech.edu/CaltechTHESIS:12182020-181342301

Abstract

Granular materials are ubiquitous in both nature and technology. They play a key role in many 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 clearly 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. We specifically study assemblies of sand particles characterized by X-ray computed tomography, as well as morphologically simpler counterparts of the same systems. Naturally arising from this investigation is the concept of a granular genome. 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. We base our investigation on data derived from a discrete element simulation of a sample of sand subjected to triaxial compression and undergoing shear banding. By representing the granular system as a complex network, we study the self-organized and cooperative evolution of topology, kinematics and kinetics within the shear band. We specifically characterize the evolution of fundamental topological structures called force cycles, and propose a novel order parameter for the system, the minimal cycle coefficient. We find that this coefficient governs the stability of force chains, which succumb to buckling as they grow beyond a characteristic maximum length. 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. Finally, by comparing with available experimental data on the same problem, we are able to validate our theoretical developments.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Granular materials, data-driven modeling, multiscale analysis, discrete element method, nonlocality
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Applied Mechanics
Minor Option:Applied And Computational Mathematics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Andrade, Jose E.
Thesis Committee:
  • Lapusta, Nadia (chair)
  • Ortiz, Michael
  • Rosakis, Ares J.
  • Andrade, Jose E.
Defense Date:15 December 2020
Record Number:CaltechTHESIS:12182020-181342301
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:12182020-181342301
DOI:10.7907/7rtg-x780
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/s11440-020-00996-8DOIArticle adapted for Chapter I.
https://doi.org/10.1061/(ASCE)GT.1943-5606.0002232DOIArticle adapted for Chapter I.
https://doi.org/10.1016/j.cma.2020.113486DOIArticle adapted for Chapter I.
https://doi.org/10.1016/j.jmps.2020.104103DOIArticle adapted for Chapter III.
https://doi.org/10.1016/j.eml.2020.101041DOIArticle adapted for Chapter V.
https://doi.org/10.1016/j.jmps.2020.104239DOIArticle adapted for Chapter IV.
ORCID:
AuthorORCID
Karapiperis, Konstantinos0000-0002-6796-8900
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14036
Collection:CaltechTHESIS
Deposited By: Konstantinos Karapiperis
Deposited On:05 Jan 2021 19:30
Last Modified:12 Jan 2021 18:35

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