Created by W.Langdon from gp-bibliography.bib Revision:1.7047
However, there are still many challenges in graphene reinforced nanocomposites. First of all, the mechanical performance of such composites is considerably hindered by weak interfacial interaction between graphene and metal matrix. This challenging issue can be effectively addressed by the use of 3D wrinkled graphene fillers with chemical functionalization. Our extensive molecular dynamics (MD) simulations manifest that the presence of 3D wrinkles and chemical modification of graphene using functional groups can significantly increase its interfacial shear strength between graphene and Cu matrix. Furthermore, it remains a great challenge to achieve high toughness and high strength simultaneously for the composites. We then report a 3D folded graphene (FGr) reinforced Cu nanocomposite that overcomes the long-standing conflicts between toughness and strength. Atomistic simulation results show that the pre-strain-induced 3D FGr reinforced Cu nanocomposite exhibits simultaneous enhancement in toughness, ductility, and strength compared to its counterpart reinforced by pristine graphene (PGr). Meanwhile, achieving negative Poisson's ratio (NPR)and negative thermal expansion (NTE) characteristics in MMCs is very challenging as well. Herein, we propose a class of 3D graphene origami (GOri)-enabled metallic metamaterials with a highly tunable NPR, negative coefficient of thermal expansion (CTE) as well as improved mechanical properties using MD method. Results reveal that the NPR and negative CTE of the nanocomposite with 3.35 wt percent Miura-patterned GOri can reach -0.2796 and -95.42e-06 per degree K at room temperature, respectively.
Predicting material properties of the proposed 3D graphene reinforced nanocomposites via existing micromechanical models, however, is a great challenge. In the thesis, a highly efficient micromechanical modeling approach based on MD simulation and genetic programming (GP) algorithm is developed to address this key issue. The GP-based micromechanical models are extensively trained from MD simulation data to accurately predict the Young's modulus, CTE, Poisson's ratio, and mass density of 3D GOri/functionalized/defective graphene reinforced Cu nanocomposites with various GOri folding degrees/functionalization coverages/defect percentages, ii graphene contents and temperatures with high coefficients of determination (R-squared). The excellent agreement between our estimated results and experimental data shows that the models developed herein are highly effective and accurate in predicting mechanical properties that are essential for the analysis and design of functionally graded (FG) 3D graphene reinforced composite structures.
Regarding the continuum modelling, this thesis presents a detailed linear and nonlinear structural analysis concerning the static bending, buckling, thermal buckling, free vibration, as well as forced vibration behaviors of FG beams made of 3D GOri/functionalized/defective graphene reinforced nanocomposites under different loading conditions within the theoretical framework of the classical Euler-Bernoulli beam/first-order shear deformation beam theory and von Karman type nonlinearity. The material properties of the beam are effectively controlled by 3D graphene content and GOri folding degree/functionalized graphene coverage that are graded across the thickness direction of the beams in a layer-wise manner such that Poisson's ratio and other material properties are position-dependent and are estimated by the GP-assisted micromechanical models. The governing equations are derived by using the minimum total potential energy principle/Lagrange equation approach/Hamilton's principle for different problems, then numerically solved employing Ritz/differential quadrature (DQ) / Newmark-beta methods. A comprehensive parametric study is performed to examine the effects of 3D graphene content, GOri folding degree/functionalized graphene coverage/defective graphene percentage and distribution patterns as well as temperature on the bending deflections and stresses, critical buckling loads, post-buckling equilibrium paths, natural frequencies,and dynamic responses of the beams, offering important insight into the engineering design and application of FG-3D graphene reinforced composite beams for significantly improved structural performance.
The thesis makes key contributions to the material-structure-performance integrated design of 3D graphene reinforced metal matrix nanocomposites based on atomic-scale simulation, machine learning technique, and continuum modeling. This study sheds important insights into the (i) material design to achieve improved interfacial property, superior strength and toughness, as well as negative Poisson's ratio and negative thermal expansion characteristics of the 3D graphene reinforced nanocomposites; (ii) property evaluation based on proposed machine learning-assisted micromechanical models of the 3D graphene reinforced nanocomposites; and (iii) linear and nonlinear structural behaviors of the FG3D graphene reinforced nanocomposite beam structures considering the effects of NPR and NTE, promoting the applications of this new material and structure form in various engineering areas.",
supervisor: Sritawat Kitipornchai",
Genetic Programming entries for Shaoyu Zhao