Influence of large strain preloads on the viscoelastic response of rubber-like materials under small oscillations

A.-S Lectez,E Verron

doi:10.1016/j.ijnonlinmec.2015.12.003

Abstract

The viscoelastic response predicted by linearized internal variables models in the case of small oscillations superimposed on a large static preload is investigated, comparing simple forms of the Zener and the Poynting–Thomson models. It is shown that both of them predict a preload dependency of the equilibrium linearized stress, but only the latter take into account such a dependency on the out-of-equilibrium part. Yet, the Zener model is much more frequently linearized as the Poynting–Thomson model. The formulation of each model for finite deformations is quickly reminded, before linearizing them around a large static preload. Finally, a comparison of the influence of preload on each model is proposed for uniaxial extension, before discussing which kind of model has to be chosen regarding theoretical and practical aspects.

Highlights

Elastomers are often used in industrial applications for which vibrations must be filtered, as in automotive engine mounts
Some challenging industrial applications require to master modeling the behavior of elastomers under large static preloads on which are superimposed small oscillations
Attention is focused on linearization of internal variables models

Summary

Introduction

Elastomers are often used in industrial applications for which vibrations must be filtered, as in automotive engine mounts. Simulating accurately the mechanical response of engine mounts is an industrial ongoing challenge which necessitates to predict the change in dynamic properties of elastomers due to the preload. Authors interested in this problem mostly choose to linearize viscoelastic models. Characterizing a material subjected to small oscillations enables the calculation of well-known intrinsic mechanical quantities, like storage and loss moduli. These quantities are only rigorously defined for small strain, as highlighted by Govindjee and Simo [1]. Linearization leads to a relatively simple closed-form solution, which ensure a faster identification procedure

Methods

Discussion

Conclusion