Material Modelling and Design of Multi-Functional Hierarchical Graphene Nanocomposites

Abstract

Multi-functional nanocomposites represent an advanced category of materials capable of performing multiple functions to meet specific requirements through tailored properties. Using graphene as a nanofiller has led to substantial property enhancements of polymer resins and attractive weight reduction capability. Graphene-based polymer nanocomposites (GPNCs) and multi-functional hierarchical graphene-reinforced polymer nanocomposites (HGPNCs) as an advanced type of composite materials hold a colossal market growth potential due to considerable improvement in properties. Today, the automotive sector is focused on integrating and developing graphene with existing applications and exploring new futuristic or potential usages. One of which is lightweight graphene nanocomposites for crash structural components thus GPNCs and HGPNCs for automotive structures must be designed to absorb a fair amount of the kinetic energy in the event of a collision to maintain the passengers' safety.
Modelling and simulation techniques are now considered essential and non-expensive practices for the manufacturing and industrial sectors to design and develop new materials. However, several technological challenges still face the simple modelling of graphene nanocomposites for structural applications without the need for expensive and time-consuming experiments. The limited literature on computational-based design and numerical modelling of HGPNCs under severe loading conditions (e.g. crashing and fragmentation) and the non-existence of efficient models for graphene-based nanocomposite materials on commercial explicit finite element (FE) software has formed a huge application obstacle.
For all of the reasons mentioned above and the lack of research in the area of modelling graphene-based polymer nanocomposite, this work was intended to understand the splendid behaviour of the GPNCs and to investigate the effect of hierarchical reinforcements in advanced HGPNC materials and their automotive structural applications. The current study contributes to modelling GPNCs and HGPNCs and discloses an integrated and computationally inexpensive approach to help discover a new realistic model. Within the context of continuum mechanics, multi-scale techniques were used to create simple novel FE-UMAT user cards and the associated algorithms as the essential steps toward developing new models.
The novelty of the current study is to create innovative, simple, and robust material models capable of capturing the real-world behaviour and crashworthiness assessment of GPNC and HGPNC materials during a collision. Two material cards were created with two behaviour approaches: elasto-plastic and elasto-visco-plastic. The first one took into account inclusion-matrix interface weakness and damaged microstructure, while the other was for comparison purposes and is able to consider interactions between inclusions and their neighbourhood, respectively. Furthermore, composite materials are susceptible to temperature change; with a mismatch of the coefficient of thermal expansions (CTEs) between composite constituent materials, internal stresses on both micro and macro scales would evolve. Therefore the material models were developed to be able to capture material behaviour under thermal conditions. In addition, the material models allowed methodical variation of material properties correlated to material behaviour and were implemented in end-user FE software (LS-DYNA). Numerical characterisation was executed under different loading conditions, e.g. tensile and compression, to enable the determination of failure thresholds. As a part of the automotive bumper system, a symmetric crush tube of crash box was modelled as it absorbs the frontal impact energy generated during a car's impact and serves as a structural material to alleviate the impact energy imparted to the chassis or passengers.

Date of Award	23 May 2023
Original language	English
Awarding Institution	Northumbria University
Supervisor	Ahmed Elmarakbi (Supervisor) & Wiyao Azoti (Supervisor)