Stochastic and deterministic models for dense granular flow

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2008.

Bibliographic Details
Main Author: Kamrin, Kenneth Norman
Other Authors: Martin Z. Bazant.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2008
Subjects:
Online Access:http://hdl.handle.net/1721.1/43736
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author Kamrin, Kenneth Norman
author2 Martin Z. Bazant.
author_facet Martin Z. Bazant.
Kamrin, Kenneth Norman
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description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2008.
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spelling mit-1721.1/437362019-04-12T09:19:52Z Stochastic and deterministic models for dense granular flow Kamrin, Kenneth Norman Martin Z. Bazant. Massachusetts Institute of Technology. Dept. of Mathematics. Massachusetts Institute of Technology. Dept. of Mathematics. Mathematics. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2008. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 245-254). Granular materials such as sand or gravel surround us everyday and yet remain poorly understood. In this thesis, two models are developed for dense granular flow, each capable of predicting flows with accuracy in multiple environments. The models are based on differing perspectives of grain-level dynamics, with one deriving flow from a stochastic mechanism and the other from a deterministic deformation law. The Stochastic Flow Rule (SFR): This work models granular flow as a sequence of localized collective grain displacements. As in the Spot Model for drainage (Bazant 2001), grain clusters move as dictated by "spots" which travel through the material as biased random-walkers. The SFR derives spot motion directly from the material stresses, thus generalizing and extending the Spot Model beyond drainage to any quasi-2D geometry with a computable stress field. Limit-State Mohr-Coulomb Plasticity is used to approximate the stress profile in a slow flowing granular assembly. The SFR then describes quantitatively how to convert the slip-line field and stresses into the necessary parameters to fully define a spot's random trajectory through the material and generate a steady flow profile. Results are compared to known flow data. Nonlinear Granular Elasto-Plasticity: This work models granular deformation at the meso-scale as a deterministic consequence of the local stresses and state parameters. Recently proposed models for granular elasticity (Jiang and Liu 2003) and plastic flow (Jop et al. 2006) are combined into one universal granular continuum law, capable of predicting both flowing regions and stagnant zones simultaneously in any arbitrary 3D flow geometry. (cont.) The unification is performed by first motivating physically, and then implementing a Kroner-Lee elasto-plastic decomposition. The model is then numerically solved in multiple geometries and results are compared to experiments and discrete simulations. by Kenneth Norman Kamrin. Ph.D. 2008-12-11T16:56:02Z 2008-12-11T16:56:02Z 2008 2008 Thesis http://hdl.handle.net/1721.1/43736 261341210 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 254 p. application/pdf Massachusetts Institute of Technology
spellingShingle Mathematics.
Kamrin, Kenneth Norman
Stochastic and deterministic models for dense granular flow
title Stochastic and deterministic models for dense granular flow
title_full Stochastic and deterministic models for dense granular flow
title_fullStr Stochastic and deterministic models for dense granular flow
title_full_unstemmed Stochastic and deterministic models for dense granular flow
title_short Stochastic and deterministic models for dense granular flow
title_sort stochastic and deterministic models for dense granular flow
topic Mathematics.
url http://hdl.handle.net/1721.1/43736
work_keys_str_mv AT kamrinkennethnorman stochasticanddeterministicmodelsfordensegranularflow