Simulating the effective electric conductivity of polymer composites with high aspect ratio fillers

Nilsson F, Schubert DW, Chen F (2016)


Publication Type: Journal article, Review article

Publication year: 2016

Journal

Pages Range: 16 - 23

Journal Issue: 132

URI: https://www.sciencedirect.com/science/article/pii/S0266353816305528#!

Abstract

Three simulation models have been developed for predicting the electrical conductivity and the electrical percolation threshold of field-grading polymer composites intended for high voltage applications. The three models are based on finite element modelling (FEM), percolation threshold modelling (PTM) and electrical networks modelling (ENM). A Monte Carlo algorithm was used to construct the geometries, with either soft-core (overlapping) or hard-core/soft-shell (non-overlapping) fibres. Conductivity measurements on carbon-fibre/PMMA composites with well-defined fibre aspect ratios were used for experimental validation. The average fibre orientations were calculated from scanning electron micrographs. The soft-core PTM model with experimental fibre orientations and without adjustable parameters gave accurate (R2 = 0.984) predictions of the electrical percolation threshold as a function of aspect ratio. The corresponding soft-core ENM model, with close-contact conductivity calculated with FEM, resulted in good conductivity predictions for the longest fibres, still without the use of any adjustable parameters. The hard-core/soft-shell versions of the models, using the shell thickness as an adjustable parameter, gave similar but slightly poorer results.

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How to cite

APA:

Nilsson, F., Schubert, D.W., & Chen, F. (2016). Simulating the effective electric conductivity of polymer composites with high aspect ratio fillers. Composites Science and Technology, 132, 16 - 23.

MLA:

Nilsson, Fritjof, Dirk W. Schubert, and Fei Chen. "Simulating the effective electric conductivity of polymer composites with high aspect ratio fillers." Composites Science and Technology 132 (2016): 16 - 23.

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