Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Likely striping in stochastic nematic elastomers

Mihai, L. Angela ORCID: https://orcid.org/0000-0003-0863-3729 and Goriely, Alain 2020. Likely striping in stochastic nematic elastomers. Mathematics and Mechanics of Solids 25 (10) , pp. 1851-1872. 10.1177/1081286520914958

[thumbnail of LCE-stripes.pdf]
Preview
PDF - Accepted Post-Print Version
Download (1MB) | Preview

Abstract

For monodomain nematic elastomers, we construct generalised elastic-nematic constitutive models combining purely elastic and neoclassical-type strain-energy densities. Inspired by recent developments in stochastic elasticity, we extend these models to stochastic-elastic-nematic forms where the model parameters are dened by spatially-independent probability density functions at a continuum level. To investigate the behaviour of these systems and demonstrate the eects of the probabilistic parameters, we focus on the classical problem of shear striping in a stretched nematic elastomer for which the solution is given explicitly. We nd that, unlike in the neoclassical case where the inhomogeneous deformation occurs within a universal interval that is independent of the elastic modulus, for the elastic-nematic models, the critical interval depends on the material parameters. For the stochastic extension, the bounds of this interval are probabilistic, and the homogeneous and inhomogeneous states compete in the sense that both have a a given probability to occur. We refer to the inhomogeneous pattern within this interval as `likely striping'.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Publisher: SAGE Publications (UK and US)
ISSN: 1081-2865
Date of First Compliant Deposit: 22 February 2020
Date of Acceptance: 22 February 2020
Last Modified: 03 May 2023 15:59
URI: https://orca.cardiff.ac.uk/id/eprint/129902

Citation Data

Cited 14 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics