Energy minimization and preconditioning in the simulation of athermal granular materials in two dimensions

Haolei Wang; Lei Zhang; Haolei Wang; Lei Zhang

doi:10.3934/era.2020023

Electronic Research Archive

2020, Volume 28, Issue 1: 405-421. doi: 10.3934/era.2020023

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Energy minimization and preconditioning in the simulation of athermal granular materials in two dimensions

Haolei Wang ,
Lei Zhang ^,

School of Mathematical Sciences, Institute of Natural Sciences, and MOE-LSC, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

Received: 01 November 2019 Revised: 01 January 2020
Primary: 74G65, 65F08; Secondary: 82B26, 68U20

Granular materials are heterogenous grains in contact, which are ubiquitous in many scientific and engineering applications such as chemical engineering, fluid mechanics, geomechanics, pharmaceutics, and so on. Granular materials pose a great challenge to predictability, due to the presence of critical phenomena and large fluctuation of local forces. In this paper, we consider the quasi-static simulation of the dense granular media, and investigate the performances of typical minimization algorithms such as conjugate gradient methods and quasi-Newton methods. Furthermore, we develop preconditioning techniques to enhance the performance. Those methods are validated with numerical experiments for typical physically interested scenarios such as the jamming transition, the scaling law behavior close to the jamming state, and shear deformation of over jammed states.
- Granular materials,
- jamming,
- scaling law,
- quasi-static evolution,
- preconditioned energy minimization
Citation: Haolei Wang, Lei Zhang. Energy minimization and preconditioning in the simulation of athermal granular materials in two dimensions[J]. Electronic Research Archive, 2020, 28(1): 405-421. doi: 10.3934/era.2020023

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Abstract

Granular materials are heterogenous grains in contact, which are ubiquitous in many scientific and engineering applications such as chemical engineering, fluid mechanics, geomechanics, pharmaceutics, and so on. Granular materials pose a great challenge to predictability, due to the presence of critical phenomena and large fluctuation of local forces. In this paper, we consider the quasi-static simulation of the dense granular media, and investigate the performances of typical minimization algorithms such as conjugate gradient methods and quasi-Newton methods. Furthermore, we develop preconditioning techniques to enhance the performance. Those methods are validated with numerical experiments for typical physically interested scenarios such as the jamming transition, the scaling law behavior close to the jamming state, and shear deformation of over jammed states.

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