Elsevier

Composite Structures

Volume 308, 15 March 2023, 116709
Composite Structures

Thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite arches via machine learning-based models

https://doi.org/10.1016/j.compstruct.2023.116709Get rights and content
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Abstract

This paper studies the thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite (FG-GRC) arches, where graphene sheets with atom vacancy defects are distributed along the arch thickness according to a power law distribution. The temperature-dependent mechanical properties of the graphene reinforced composites are determined by a genetic programming (GP) assisted micromechanical model. The governing equations for the thermomechanical in-plane dynamic instability are derived by Hamilton’s principle and solved by differential quadrature method (DQM) in conjunction with Bolotin method. Comprehensive numerical studies are performed to examine the effects of vacancy defect and graded distribution of graphene, temperature variation, load position, as well as boundary conditions on the free vibration, elastic buckling, and dynamic instability behaviors of the FG-GRC arch. Numerical results show that the structural performance of the FG-GRC arch is weakened by graphene defect and temperature rise and is significantly influenced by both graphene distribution and boundary conditions.

Keywords

Defective graphene
Functionally graded arch
Asymmetric elastic constraint
Dynamic instability
Thermomechanical action
Genetic programming assisted micromechanical model

Data availability

Data will be made available on request.

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