Abstract. Predicting the mechanical response of biological soft materials requires an understanding of the complex phenomena characterizing their microscale. In this work, we use an existing versatile framework, based on assumptions on the statistical distribution of biolpolymers at the network scale, for extending our previous entropic constitutive model of Worm-Like Chains networks to different deformation classes. Furthermore, we include the effect of molecules topological constraints by introducing an energy term depending on the second invariant of the Green-Cauchy tensor. In this way we are able to qualitatively reproduce, with a limited set of physically meaningful constitutive parameters, a range of observed phenomena such as induced anisotropy, stress softening, hardening, Mullins effect, evolution of permanent stretches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.