Issue 45, 2023

A dimensionally-reduced nonlinear elasticity model for liquid crystal elastomer strips with transverse curvature

Abstract

Liquid crystalline elastomers (LCEs) are active materials that are of interest due to their programmable response to various external stimuli such as light and heat. When exposed to these stimuli, the anisotropy in the response of the material is governed by the nematic director, which is a continuum parameter that is defined as the average local orientation of the mesogens in the liquid crystal phase. This nematic director can be programmed to be heterogeneous in space, creating a vast design space that is useful for applications ranging from artificial ligaments to deployable structures to self-assembling mechanisms. Even when specialized to long and thin strips of LCEs – the focus of this work – the vast design space has required the use of numerical simulations to aid in experimental discovery. To mitigate the computational expense of full 3-d numerical simulations, several dimensionally-reduced rod and ribbon models have been developed for LCE strips, but these have not accounted for the possibility of initial transverse curvature, like carpenter's tape spring. Motivated by recent experiments showing that transversely-curved LCE strips display a rich variety of configurations, this work derives a dimensionally-reduced 1-d model for pre-curved LCE strips. The 1-d model is validated against full 3-d finite element calculations, and it is also shown to capture experimental observations, including tape-spring-like localizations, in activated LCE strips.

Graphical abstract: A dimensionally-reduced nonlinear elasticity model for liquid crystal elastomer strips with transverse curvature

Article information

Article type
Paper
Submitted
23 May 2023
Accepted
20 Oct 2023
First published
23 Oct 2023

Soft Matter, 2023,19, 8764-8778

Author version available

A dimensionally-reduced nonlinear elasticity model for liquid crystal elastomer strips with transverse curvature

K. LoGrande, M. R. Shankar and K. Dayal, Soft Matter, 2023, 19, 8764 DOI: 10.1039/D3SM00664F

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