Deformation and failure in nanomaterials via a data driven modelling approach

TL;DR

This paper introduces a data-driven computational model for nanomaterials that captures multiple material states and predicts deformation and failure behaviors, including strain rate effects, with high accuracy.

Contribution

The work presents a novel multi-state data-driven model for nanomaterials, extending previous models limited to two states, and demonstrates its effectiveness on carbon nanotubes and nanocomposites.

Findings

Model accurately predicts strain rate dependent deformation and failure.

Good agreement between simulations and experimental data.

Applicable to complex nanomaterial systems.

Abstract

A data driven computational model that accounts for more than two material states has been presented in this work. Presented model can account for multiple state variables, such as stresses, strains, strain rates and failure stress, as compared to previously reported models with two states. Model is used to perform deformation and failure simulations of carbon nanotubes and carbon nanotube/epoxy nanocomposites. The model capability of capturing the strain rate dependent deformation and failure has been demonstrated through predictions against uniaxial test data taken from literature. The predicted results show a good agreement between data set taken from literature and simulations.