A novel statistical spring-bead based network model for self-sensing smart polymer materials

Jinjun Zhang, Bonsung Koo, Yingtao Liu, Jin Zou, Aditi Chattopadhyay, Lenore Dai

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

This paper presents a multiscale modeling approach to simulating the self-sensing behavior of a load sensitive smart polymer material. A statistical spring-bead based network model is developed to bridge the molecular dynamics simulations at the nanoscale and the finite element model at the macroscale. Parametric studies are conducted on the developed network model to investigate the effects of the thermoset crosslinking degree on the mechanical response of the self-sensing material. A comparison between experimental and simulation results shows that the multiscale framework is able to capture the global mechanical response with adequate accuracy and the network model is also capable of simulating the self-sensing phenomenon of the smart polymer. Finally, the molecular dynamics simulation and network model based simulation are implemented to evaluate damage initiation in the self-sensing material under monotonic loading.

Original languageEnglish (US)
Article number085022
JournalSmart Materials and Structures
Volume24
Issue number8
DOIs
StatePublished - Aug 1 2015

Keywords

  • network model
  • self-sensing
  • smart material
  • spring-bead model
  • statistical

ASJC Scopus subject areas

  • Signal Processing
  • Civil and Structural Engineering
  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Electrical and Electronic Engineering

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