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In the context of the recently developed "equation-free" approach to the computer-assisted analysis of complex systems, we illustrate the computation of coarsely self-similar solutions. Dynamic renormalization and fixed point algorithms for…

计算物理 · 物理学 2007-05-23 L. Chen , P. G. Debenedetti , C. W. Gear , I. G. Kevrekidis

Equation-free approaches have been proposed in recent years for the computational study of multiscale phenomena in engineering problems where evolution equations for the coarse-grained, system-level behavior are not explicitly available. In…

动力系统 · 数学 2007-05-23 Yu Zou , Ioannis G. Kevrekidis , Roger G. Ghanem

To acquire the ability to numerically study the rheology of particulate two-phase flows that lack scale separation, we present a general method to average or coarse-grain the equations of motion of a mixture of a continuous fluid of…

流体动力学 · 物理学 2026-01-22 Thomas Pähtz , Yulan Chen , Rui Zhu , Katharina Tholen , Zhiguo He

Complex fluids exhibit structure on a wide range of length and time scales, and hierarchical approaches are necessary to investigate all facets of their often unusual properties. The study of idealized coarse-grained models at different…

软凝聚态物质 · 物理学 2008-10-23 Friederike Schmid

We present an ``equation-free'' multiscale approach to the simulation of unsteady diffusion in a random medium. The diffusivity of the medium is modeled as a random field with short correlation length, and the governing equations are cast…

数值分析 · 数学 2007-05-23 Dongbin Xiu , Ioannis Kevrekidis

We establish, through coarse-grained computation, a connection between traditional, continuum numerical algorithms (initial value problems as well as fixed point algorithms) and atomistic simulations of the Larson model of micelle…

软凝聚态物质 · 物理学 2009-11-10 Dmitry I. Kopelevich , Athanassios Z. Panagiotopoulos , Ioannis G. Kevrekidis

We use three-dimensional particle dynamics simulations, coupled with volume-averaged gas phase hydrodynamics, to study vertically vibrated gas-fluidized beds of fine, cohesive powders. The volume-averaged interstitial gas flow is restricted…

材料科学 · 物理学 2007-05-23 Sung Joon Moon , I. G. Kevrekidis , S. Sundaresan

We develop coarse-grained particle approaches for studying the elastic mechanics of vesicles with heterogeneous membranes having phase-separated domains. We perform simulations both of passive shape fluctuations and of active systems where…

软凝聚态物质 · 物理学 2023-02-28 David A. Rower , Paul J. Atzberger

A numerical procedure was developed for solving equations for compressible granular multiphase flows in which the particle volume fraction can range dynamically from very dilute to very dense. The procedure uses a low-dissipation and…

计算物理 · 物理学 2014-10-20 Ryan W. Houim , Elaine S. Oran

We present an equation-free dynamic renormalization approach to the computational study of coarse-grained, self-similar dynamic behavior in multidimensional particle systems. The approach is aimed at problems for which evolution equations…

动力系统 · 数学 2009-11-11 Yu Zou , Ioannis Kevrekidis , Roger Ghanem

The computational study of strongly-coupled, gas-solid flows at scales relevant to most environmental and engineering applications requires the use of `coarse-grained' methodologies such as the two-fluid model, particle-in-cell approach or…

流体动力学 · 物理学 2025-09-16 Lee Rosenberg , William Fullmer , Sarah Beetham

We present a multiscale simulation algorithm for amorphous materials, which we illustrate and validate in a canonical case of dense granular flow. Our algorithm is based on the recently proposed Spot Model, where particles in a dense random…

软凝聚态物质 · 物理学 2009-11-11 Chris H. Rycroft , Martin Z. Bazant , Gary S. Grest , James W. Landry

An important class of fluid-structure problems involve the dynamics of ordered arrays of immersed, flexible fibers. While specialized numerical methods have been developed to study fluid-fiber systems, they become infeasible when there are…

流体动力学 · 物理学 2019-07-17 David B. Stein , Michael J. Shelley

In this paper, a statistical physical derivation of thermodynamically consistent fluid mechanical equations is presented for non-isothermal viscous molecular fluids. The coarse-graining process is based on (i) the adiabatic expansion of the…

统计力学 · 物理学 2024-04-18 Gyula I. Tóth

We coarse-grain a model of closely-packed ellipses that can vary their aspect ratio to derive continuum equations for materials comprising confluent deformable particles such as epithelial cell layers. We show that contractile nearest…

软凝聚态物质 · 物理学 2025-01-14 Mehrana R. Nejad , Julia M Yeomans

In this work, we assess the internal dynamics of particles in liquid-solid fluidized beds using an unresolved CFD-DEM model. We use the Nearest Neighbors Method (NNM) and the mixing index based on the principal component analysis proposed…

流体动力学 · 物理学 2024-06-25 Victor Oliveira Ferreira , Bruno Blais , Gabriela Cantarelli Lopes

In this work, a coarse-graining method previously proposed by the authors in a companion paper based on solving diffusion equations is applied to CFD-DEM simulations, where coarse graining is used to obtain solid volume fraction, particle…

计算物理 · 物理学 2015-01-07 Rui Sun , Heng Xiao

We present a novel thermodynamically guided, low-noise, time-scale bridging, and pertinently efficient strategy for the dynamic simulation of microscopic models for complex fluids. The systematic coarse-graining method is exemplified for…

软凝聚态物质 · 物理学 2010-11-12 Patrick Ilg , Hans Christian Öttinger , Martin Kröger

Computational fluid dynamics and discrete element method (CFD-DEM) coupling is an efficient and powerful tool to simulate particle-fluid systems. However, current volume-averaged CFD-DEM relying on direct grid-based mapping between the…

流体动力学 · 物理学 2025-06-12 Yuxiang Liu , Lu Jing , Xudong Fu , Huabin Shi

We present a real-space formulation for coarse-graining Kohn-Sham Density Functional Theory that significantly speeds up the analysis of material defects without appreciable loss of accuracy. The approximation scheme consists of two steps.…

计算物理 · 物理学 2015-06-11 Phanish Suryanarayana , Kaushik Bhattacharya , Michael Ortiz
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