Bridging nanoparticle morphology and viscoelastic behavior in epoxy nanocomposites: A coarse-grained simulation-informed constitutive model
2026-06-08 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors developed a way to predict how polymer nanocomposites behave when they are stretched, heated, or cooled. They combined computer simulations that simplify molecules with real experiments to understand how tiny particles inside the material affect its strength and flexibility. Their model can show how the material changes with different temperatures, stretch speeds, and particle sizes without needing too many physical tests. This helps engineers design better materials more efficiently.
polymer nanocompositescoarse-grained molecular simulationconstitutive modelsnanoparticleshyperelasticityrate-dependent behaviortemperature effectsmaterial softeningstrain rateexperimental validation
Authors
Atiyeh Hentea, Shadab Zakavatib, Behrouz Arash, Maximilian Jux, Raimund Rolfes
Abstract
Accurate prediction of the material behavior of polymer nanocomposites under various thermomechanical loading conditions is increasingly demanded for engineering applications. This study proposes an integrated framework combining coarse-grained (CG) molecular simulations and experimental testing to develop predictive constitutive models for nanoparticle/epoxy nanocomposites. The key contribution of this work lies in characterizing the influence of nanoparticle content and agglomerate size on the rate- and temperature-dependent behavior of nanocomposites, enabled by large-scale CG simulations. The proposed framework successfully captures the material response, including nonlinear hyperelasticity, softening behavior, and rate- and temperature-dependent properties, across a broad range of strain rates, temperatures, and nanoparticle sizes and weight fractions. The predictive capability of the CG simulation-informed constitutive model is validated using additional experimental data that were not included in the parameter identification process. By reducing reliance on extensive experimental testing while maintaining high accuracy, this simulation-driven approach offers an efficient pathway for developing robust, predictive constitutive models for designing and optimizing advanced nanocomposites.