Representative Volume Element Approximations in Elastoplastic Spring Networks

Research output: Contribution to journalResearch articleContributedpeer-review

Abstract

We study the large-scale behavior of a small-strain lattice model for a network composed of elastoplastic springs with random material properties. We formulate the model as an evolutionary rate independent system. In an earlier work we derived a homogenized continuum model, which has the form of linearized elastoplasticity, as an evolutionary Gamma-limit as the lattice parameter tends to zero. In the present paper we introduce a periodic representative volume element (RVE) approximation for the homogenized system. As a main result we prove convergence of the RVE approximation as the size of the RVE tends to infinity. We also show that the hysteretic stress-strain relation of the effective system can be described with the help of a generalized Prandtl–Ishlinskii operator, and we prove convergence of a periodic RVE approximation for that operator. We combine the RVE approximation with a numerical scheme for rate-independent systems and obtain a computational scheme that we use to numerically investigate the homogenized system in the specific case when the original network is given by a two-dimensional lattice model. We simulate the response of the system to cyclic and uniaxial, monotonic loading, and numerically investigate the convergence rate of the periodic RVE approximation. In particular, our simulations show that the RVE error decays with the same rate as the RVE error in the static case of linear elasticity.

Details

Original languageEnglish
Pages (from-to)588-638
JournalMultiscale modeling & simulation
Volume22
Issue number1
Publication statusPublished - 20 Mar 2024
Peer-reviewedYes

External IDs

ORCID /0000-0003-1093-6374/work/156338038
Mendeley 6e1fc58e-7c1c-3398-a6be-1cd779e07387
Scopus 85188838324

Keywords

Keywords

  • elastoplasticity, spring network, Prandtl-Ishlinskii operator, representative volume element, numerical simulation, stochastic homogenization