4.4 Article

Tensor decomposition for modified quasi-linear viscoelastic models: Towards a fully non-linear theory

Journal

MATHEMATICS AND MECHANICS OF SOLIDS
Volume -, Issue -, Pages -

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/10812865231165232

Keywords

Quasi-linear viscoelasticity; strain-dependent relaxation; isotropy and transverse isotropy; fourth-order tensor decomposition

Ask authors/readers for more resources

This article discusses the decomposition of the tensorial relaxation function for isotropic and transversely isotropic modified quasi-linear viscoelastic models. It demonstrates how to formulate the constitutive equation using a convenient decomposition of the relaxation tensor into scalar components and tensorial bases. It also shows that the bases must be symmetrically additive, ensuring consistency with the elastic limit.
We discuss the decomposition of the tensorial relaxation function for isotropic and transversely isotropic (TI) modified quasi-linear viscoelastic (MQLV) models. We show how to formulate the constitutive equation using a convenient decomposition of the relaxation tensor into scalar components and tensorial bases. We show that the bases must be symmetrically additive, i.e., they must sum up to the symmetric fourth-order identity tensor. This is a fundamental property both for isotropic and anisotropic bases that ensures the constitutive equation is consistent with the elastic limit. We provide two robust methods to obtain such bases. Furthermore, we show that, in the TI case, the bases are naturally deformation-dependent for deformation modes that induce rotation or stretching of the fibres. Therefore, the MQLV framework allows to capture the non-linear phenomenon of strain-dependent relaxation, which has always been a criticised limitation of the original quasi-linear viscoelastic theory. We illustrate this intrinsic non-linear feature, unique to the MQLV model, with two examples (uni-axial extension and perpendicular shear).

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available