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

Abstract

This paper presents a multiscale modeling approach to simulate 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 mechanical response of the self-sensing material. The 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. Nomenclature I= fluorescence intensity density = original length of the ith spring = variation of the spring length N = number of springs in this subzone = number of actual potential crosslinked covalent bonds = number of all potential crosslinked covalent bonds = loading strain = equivalent strain in the ith spring ̅ = average equivalent strain in the bead-centered subzone = crosslinking degree Keywords: spring-bead model, network model, statistical distribution of crosslinking degree, self-sensing smart material

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