Journal Article

Data-driven modeling for thermo-elastic properties of vacancy-defective graphene reinforced nanocomposites with its application to functionally graded beams

Yang, Jie, Kitipornchai, Sritawat, Zhang, Yihe, Zhang, Wei, Zhao, Shaoyu, Zhang, Yingyan

2023

Overview

The presence of unavoidable defects in the form of atom vacancies in graphene sheets considerably deteriorates the thermo-elastic properties of graphene-reinforced nanocomposites. Since none of the existing micromechanics models is capable of capturing the effect of vacancy defect, accurate prediction of the mechanical properties of these nanocomposites poses a great challenge. Based on molecular dynamics (MD) databases and genetic programming (GP) algorithm, this paper addresses this key issue by developing a data-driven modeling approach which is then used to modify the existing Halpin–Tsai model and rule of mixtures by taking vacancy defects into account. The data-driven micromechanics models can provide accurate and efficient predictions of thermo-elastic properties of defective graphene-reinforced Cu nanocomposites at various temperatures with high coefficients of determination (R2 > 0.9). Furthermore, these well-trained data-driven micromechanics models are employed in the thermal buckling, elastic buckling, free vibration, and static bending analyses of functionally graded defective graphene reinforced composite beams, followed by a detailed parametric study with a particular focus on the effects of defect percentage, content, and distribution pattern of graphene as well as temperature on the structural behaviors.

• Publisher: Springer Nature B.V
• Subject: Beams (structural) / Composite beams / Defects / Elastic buckling / Elastic properties / Free vibration / Functionally gradient materials / Genetic algorithms / Graphene / Mechanical properties / Micromechanics / Modelling / Molecular dynamics / Nanocomposites / Thermal buckling / Thermoelastic properties