Numerical investigation of graphene-aluminum nanocomposite
Creators: name: Liu, Jun
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 401707
subject: Solid mechanics
classificationCode: 401708
subject: Tribology

description: Developing novel lightweight and yet stiff alternative composite materials substituting conventional metal and alloys is one of the solutions to achieve low fuel consumption, especially in aeronautical applications. The investigation of the mechanical property of the metal matrix composite (MMCs) and the application is still at the infant stage, while the weak interfacial interaction between the matrix and graphene is a significant bottleneck that considerably hinders its reinforcing effectiveness and efficiency. This study is dedicated to investigating graphene-aluminum nanocomposite (Gr/Al NC) material at the atomistic scale and explores the potential applications in engineering structures. By intensive molecular dynamics (MD) simulations, it is found that the volume fraction of graphene has a positive correlation with the Young’s modulus and ultimate tensile strength of Gr/Al NCs regardless of alignments, while increasing orientation angles, temperature and the density of vacancy defects in graphene have adverse effects on the Young’s modulus and ultimate tensile strength. With the same volume fraction, graphene platelets (GPL) with a high aspect ratio are favorable for improved mechanical properties. It is also found that modifying the Al substrate is an effective strategy to achieve significantly improved interfacial shear strength and overall mechanical properties of Gr/Al composites. Among the three cases considered, i.e., substrate with Al2O3 (with or without covalent bonds formed between Al2O3 and graphene) or Al4C3, modifying Al substrate by Al2O3 without covalent bonds formed at the interface between Al2O3 and graphene produces the strongest interfacial interaction and the best mechanical properties. Furthermore, the material laws are generalized by combining molecular dynamic (MD) simulation and machine learning (ML) techniques. After training and optimization based on MD data, ML models are developed to predict Young’s modulus and ultimate tensile strength of Gr/Al NC, with the intricate effects of graphene’s volume fraction, alignment angle, chirality and environment temperature being taken into account. Based on the prediction, a modified micromechanics-based Halpin-Tsai model is proposed as a handy form to determine the Young’s modulus with a significantly improved accuracy from an explicit relationship. Finally, the application of functionally graded graphene nanoplatelet reinforced aluminum composite (FG- GPL/Al NC) in engineering structures is investigated numerically, with a particular focus on rectangular plates and wing structures under aerodynamic loads. Two-way/one-way loosely coupled fluid-structure interaction (FSI) investigation is implemented by coupling finite element analysis (FEA) and computational fluid dynamics (CFD). It is shown that in conjunction with the aeroelastic tailoring technique, the composite plate can achieve optimized structural and aerodynamic performance, while the strength and dynamic behaviors of the wing can be efficiently improved, by exploiting specific GPL distribution patterns.