Stochastic Generalized-Order Constitutive Modeling of Viscoelastic Spectra of Polyurea-Graphene Nanocomposites

TL;DR

This paper develops a stochastic generalized constitutive model for polyurea-graphene nanocomposites, integrating fractional viscoelastic models to predict their spectra and guide design in various applications.

Contribution

It introduces a novel parallel fractional Maxwell model with a new dimensionless number to accurately describe nanocomposite viscoelastic behavior.

Findings

Identified key influential parameters for the model.

Validated the model's ability to predict nanocomposite spectra.

Provided insights for optimizing nanocomposite design.

Abstract

Polyurea (PUa) elastomers are extensively used in a wide range of applications spanning from biomedical to defense fields due to their enabling mechanical properties. These materials can be further reinforced through the incorporation of nanoparticles to form nanocomposites. This study focuses on an IPDI-based PUa matrix with exfoliated graphene nanoplatelet (xGnP) fillers. We propose a generalized constitutive model by integrating one Fractional Maxwell Model (FMM) and one Fractional Maxwell Gel (FMG) branch in a parallel configuration via introducing a new dimensionless number to bridge between these branches physically and mathematically. Through systematic local-to-global sensitivity analyses, we investigate the behavior of these nanocomposites to facilitate simulation, design, and performance prediction. Consistently, the constructed models share the same most/least influential model parameters. $\alpha_1$ and $E_{c_1}$, the power exponent and the characteristic modulus of the first branch, are found to be the most influential model parameters, while $\tau_{c_2}$ and $\tau_{c_1}$, the characteristic time-scales of each branch, are recognized as the least influential model parameters. The proposed PU nanocomposite constitutive laws can now make an impact to the design and optimization of coating and shock-absorbing coatings in a range of applications.