Preprint Article Version 1 This version is not peer-reviewed

A Multi-Scale Modeling Approach for Simulating Crack Sensing in Polymer Fibrous Composites Using Electrically Conductive Carbon Nanotube Networks. Part II: Meso- and Macro-Scale Analyses

Version 1 : Received: 26 August 2018 / Approved: 27 August 2018 / Online: 27 August 2018 (11:25:23 CEST)

A peer-reviewed article of this Preprint also exists.

Tserpes, K.; Kora, C. A Multi-Scale Modeling Approach for Simulating Crack Sensing in Polymer Fibrous Composites Using Electrically Conductive Carbon Nanotube Networks. Part II: Meso- and Macro-Scale Analyses. Aerospace 2018, 5, 106. Tserpes, K.; Kora, C. A Multi-Scale Modeling Approach for Simulating Crack Sensing in Polymer Fibrous Composites Using Electrically Conductive Carbon Nanotube Networks. Part II: Meso- and Macro-Scale Analyses. Aerospace 2018, 5, 106.

Journal reference: Aerospace 2018, 5, 106
DOI: 10.3390/aerospace5040106

Abstract

This is the second of a two-paper series describing a multi-scale modeling approach developed to simulate crack sensing in polymer fibrous composites by exploiting interruption of electrically conductive carbon nanotube (CNT) networks. The approach is based on the finite element (FE) method. FE models at three different scales, namely the micro-scale, the meso-scale and the macro-scale, have been developed using the ANSYS PDL environment. In the present paper, the meso- and macro-scale analyses are described. In the meso-scale, a two-dimensional model of the CNT/polymer matrix reinforced by carbon fibers is used to develop a crack sensing methodology from a parametric study which relates the crack position and length with the reduction of current flow. In the meso-model, the effective electrical conductivity of the CNT/polymer computed from the micro-scale is used as input. In the macro-scale, the final implementation of the crack sensing methodology is performed on a CNT/polymer/carbon fiber composite volume using as input the electrical response of the cracked CNT/polymer derived at the micro-scale and the crack sensing methodology. Analyses have been performed for cracks of two different lengths. In both cases, the numerical model predicts with good accuracy both the length and position of the crack. These results highlight the prospect of conductive CNT networks to be used as a localized structural health monitoring technique.

Subject Areas

Carbon nanotubes; Polymer nanocomposites; Electrical conductivity; Crack sensing; Multi-scale modeling

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