Medium Amplitude Parallel Superposition (MAPS) Rheology, Part 1: Mathematical Framework and Theoretical Examples

TL;DR

This paper introduces MAPS rheology, a new mathematical framework for characterizing weakly nonlinear viscoelastic responses using the third order complex modulus, offering comprehensive data without flow instabilities.

Contribution

It presents the MAPS rheology framework, revealing the third order complex modulus and its advantages over existing methods for analyzing nonlinear viscoelasticity.

Findings

MAPS response encompasses previous medium amplitude shear responses.

MAPS response function can be measured in strain or stress control.

The framework allows visualization and reduction of response functions.

Abstract

A new mathematical representation for nonlinear viscoelasticity is presented based on application of the Volterra series expansion to the general nonlinear relationship between shear stress and shear strain history. This theoretical and experimental framework, which we call Medium Amplitude Parallel Superposition (MAPS) Rheology, reveals a new material property, the third order complex modulus, which describes completely the weakly nonlinear response of a viscoelastic material in an arbitrary simple shear flow. In this first part, we discuss several theoretical aspects of this mathematical formulation and new material property. For example, we show how MAPS measurements can be performed in strain- or stress-controlled contexts and provide relationships between the weakly nonlinear response functions measured in each case. We show that the MAPS response function is a super-set of the response functions that have been previously reported in medium amplitude oscillatory shear and parallel superposition rheology experiments. We also show how to exploit inherent symmetries of the MAPS response function to reduce it to a minimal domain for straightforward measurement and visualization. We compute this material property for a few constitutive models to illustrate the potential richness of the data sets generated by MAPS experiments. Finally, we discuss the MAPS framework in the context of some other nonlinear, time-dependent rheological probes and explain how the MAPS methodology has a distinct advantage over these others because it generates data embedded in a very high dimensional space without driving fluid mechanical instabilities, and is agnostic to the flow protocol.