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Causal rigid particles whose action includes an {\it arbitrary} dependence on the world-line extrinsic curvature are considered. General classes of solutions are constructed, including {\it causal tachyonic} ones. The Hamiltonian…

High Energy Physics - Theory · Physics 2009-10-22 Jan Govaerts

It is recalled that stress-strain incremental modelling is a common feature of most theoretical description of the mechanical behaviour of granular material. An other commonly accepted characteristics of the mechanical behaviour of granular…

Soft Condensed Matter · Physics 2007-05-23 P. Evesque

A bonded particle model is used to explore how variations in the material properties of brittle, isotropic solids affect critical behavior in fragmentation. To control material properties, a new model is proposed which includes breakable…

Soft Condensed Matter · Physics 2023-09-14 Joel T. Clemmer , Mark O. Robbins

The consolidation of suspended particulate matter under external forces such as pressure or gravity is of widespread interest. In this work, we derive a constitutive relation to describe the deformation of a {\it two-dimensional} strongly…

Soft Condensed Matter · Physics 2014-02-26 Saikat Roy , Mahesh S Tirumkudulu

We report on a numerical study of the shear flow of a simple two-dimensional model of a granular material under controlled normal stress between two parallel smooth, frictional walls, moving with opposite velocities $\pm$V . Discrete…

Soft Condensed Matter · Physics 2012-10-15 Zahra Shojaaee , Jean-Noël Roux , François Chevoir , Wolf Dietrich

It is shown here that fracture after a brief plastic strain, typically of a few percents, is a necessary consequence of the polycrystalline nature of the materials. The polycrystal undergoing plastic deformation is modeled as a flowing…

Materials Science · Physics 2018-08-28 Miguel Lagos , César Retamal , Rodrigo Valle

We present a physical model for turbulent friction on rough surfaces with regularly distributed roughness elements. Wall shear stresses are expressed as functions of physical quantities. Surfaces with varying roughness densities and…

Fluid Dynamics · Physics 2016-10-24 Zhuoqun Li , Xiaojing Zheng

Mutual information between local stress and local non-affine deformation is proposed as a collective field variable quantifying the {\em local softness} of soft materials. The liquid-solid transition in a simple liquid is considered as a…

Soft Condensed Matter · Physics 2025-02-18 Tamoghna Das

The renormalization group (RG) is an essential technique in statistical physics and quantum field theory, which considers scale-invariant properties of physical theories and how these theories' parameters change with scaling. Deep learning…

Statistical Mechanics · Physics 2023-08-23 Kelsie Taylor

Granular fluids consist of collections of activated mesoscopic or macroscopic particles (e.g., powders or grains) whose flows often appear similar to those of normal fluids. To explore the qualitative and quantitative description of these…

Statistical Mechanics · Physics 2007-07-26 James W. Dufty

Despite extensive studies on either smooth granular-fluid flow or the solid-like deformation at the slow limit, the change between these two extremes remains largely unexplored. By systematically investigating the fluctuations of tightly…

Soft Condensed Matter · Physics 2021-03-31 Jih-Chiang , Tsai , Guan-Hao Huang , Cheng-En Tsai

Granular elasticity, an elasticity theory useful for calculating static stress distribution in granular media, is generalized to the dynamic case by including the plastic contribution of the strain. A complete hydrodynamic theory is derived…

Soft Condensed Matter · Physics 2010-10-14 Yimin Jiang , Mario Liu

A renormalization group transformation suitable for spin glass models and, more generally, for disordered models, is presented. The procedure is non-standard in both the nature of the additional interactions and the coarse graining…

Disordered Systems and Neural Networks · Physics 2009-11-07 G. Parisi , R. Petronzio , F. Rosati

Granular materials often segregate under mechanical agitation, which differs from the expectation of mixing. It is well known that a bidisperse mixture of granular materials in a partially filled rotating cylinder exhibits alternating bands…

Soft Condensed Matter · Physics 2023-06-09 Kurumi Kondo , Hiroyuki Ebata , Shio Inagaki

Many biological tissues feature a heterogeneous network of fibers whose tensile and bending rigidity contribute substantially to these tissues' elastic properties. Rigidity percolation has emerged as a important paradigm for relating these…

We study, using simulations, the steady-state flow of dry sand driven by gravity in two-dimensions. An investigation of the microscopic grain dynamics reveals that grains remain separated but with a power-law distribution of distances and…

Soft Condensed Matter · Physics 2016-08-31 Colin Denniston , Hao Li

As dense granular materials are sheared, a shear band and an anisotropic force network form. The approach to steady state behavior depends on the history of the packing and the existing force and contact network. We present experiments on…

Soft Condensed Matter · Physics 2009-11-10 Brian Utter , R. P. Behringer

A perturbative renormalization group method is used to obtain steady-state density profiles of a particle non-conserving asymmetric simple exclusion process. This method allows us to obtain a globally valid solution for the density profile…

Statistical Mechanics · Physics 2017-04-05 Sutapa Mukherji

Statistical mechanics describes interaction between particles of a physical system. Particle properties of the system can be modelled with a random field on a lattice and studied at different distance scales using renormalization group…

Probability · Mathematics 2015-04-29 Farida Kachapova , Ilias Kachapov

We give a statistical-mechanical theory of stress transmission in disordered arrays of rigid grains with perfect friction. Starting from the equations of microscopic force and torque balance we derive the fundamental equations of stress…

Disordered Systems and Neural Networks · Physics 2009-10-31 S. F. Edwards , D. V. Grinev