Investigate the Buckling Behavior of Beams Made of Carbon Nanotube (CNT)-Reinforced Polymer Matrix Nanocomposites by Finite Element Method

Authors

https://doi.org/10.48314/ijrceai.v3i2.56

Abstract

In this study, buckling behavior of beams made of Carbon Nanotube (CNT)-reinforced polymer matrix nanocomposites is investigated. To this end, the mechanical properties including elastic modulus and Poisson’s ratio of nanocomposites are extracted based on an analytical micromechanical model. The distribution type of CNTs into the nanocomposite is considered to be random and aligned, and micromechanical formulations of Mori-Tanaka (M-T) method are extracted for these two conditions. The interphase created due to non-bonded van der Waals interactions between the CNT and polymer matrix is incorporated in the simulation of mechanical properties. after calculating the elastic properties, the buckling behavior of nanocomposite beams is studied using the finite element method. The effects of CNT volume fraction, CNT diameter, temperature variation, thickness and adhesion exponent of interphase at micro-scale on the buckling behavior are examined. Also, the dimensions and cross-section type of beams as well as number of elements at macro-scale on the buckling behavior are investigated. The results show that the increase of CNT volume fraction leads to an increase in buckling loads of nanocomposite beams. So, as an important outcome, adding the CNTs into the nanocomposite beams can postpone the induced damage due to buckling. The comparison between the results of the present micromechanical method and experimental data indicates that the model has a reasonable accuracy.   

Keywords:

Nanocomposite beam, Carbon nanotube, Buckling, Micromechanics

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Published

2026-06-09

How to Cite

Jamali, A., & Rabieifar, H. R. (2026). Investigate the Buckling Behavior of Beams Made of Carbon Nanotube (CNT)-Reinforced Polymer Matrix Nanocomposites by Finite Element Method. International Journal of Researches on Civil Engineering With Artificial Intelligence , 3(2), 89-106. https://doi.org/10.48314/ijrceai.v3i2.56

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