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On the inference of viscoelastic constants from stress relaxation experiments

  • Kumar Vemaganti
  • Sandeep Madireddy
  • Sayali Kedari
Article
  • 36 Downloads

Abstract

Several constitutive theories have been proposed in the literature to model the viscoelastic response of materials, including widely used rheological constitutive models. These models are characterized by certain parameters (“time constants”) that define the time scales over which the material relaxes. These parameters are primarily obtained from stress relaxation experiments using curve-fitting techniques. However, the question of how best to estimate these time constants remains open.

As a step towards answering this question, we propose an optimal experimental design approach based on ideas from information geometry, namely Fisher information and Kullback–Leibler divergence. The material is modeled as a spring element in parallel with multiple Maxwell elements and described using a one- or two-term Prony series. Treating the time constants as unknowns, we develop expressions for the Fisher information and Kullback–Leibler divergence that allow us to maximize information gain from experimental data. Based on the results of this study, we propose that the largest time constant estimated from a stress relaxation experiment for a linear viscoelastic material should be at most one-fifth of the total time of the experiment in order to maximize information gain. Our results also provide confirmation that the equilibrium modulus of the material cannot be reliably determined from curve-fitting to data from a stress relaxation experiment.

Keywords

Viscoelastic Time constant Optimal experimental design Fisher information Kullback–Leibler divergence 

Notes

Acknowledgement

We are grateful to the University of Cincinnati Simulation Center for providing financial support for this work.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Mechanical and Materials EngineeringUniversity of CincinnatiCincinnatiUSA
  2. 2.Mathematics and Computer Science DivisionArgonne National LaboratoryArgonneUSA

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