Open Access
Subscription Access
Study on Multiscale Modelling and Simulation of Layered Fibre Reinforced Nanocomposites
Abstract
Layered fibre reinforced nanocomposites (LFRNCs) represent a class of advanced materials with superior mechanical, thermal, and electrical properties compared to conventional composites. Understanding and optimizing their performance necessitates detailed investigations at various length and time scales. This paper explores the application of multiscale modelling and simulation techniques in predicting and analyzing the behavior of LFRNCs. We discuss common computational methods employed across different scales, from the molecular level (molecular dynamics, Monte Carlo) to the macroscale (finite element method, micromechanics). The paper highlights key functionalities of these methods, including the ability to model interactions between fibres, nanoparticles, and matrix, predict mechanical properties, and analyze failure mechanisms. Furthermore, we present various applications of multiscale modelling, such as optimizing fibre/nanoparticle distribution, understanding interfacial interactions, and designing LFRNCs for specific functionalities. Finally, we discuss the challenges and opportunities associated with this approach, paving the way for future research directions.
References
"Multiscale Modeling of Polymer Nanocomposites: Techniques and Applications" by Michel and Tandon (2012): This book provides a comprehensive overview of various multiscale modelling techniques used for polymer nanocomposites, including LFRNCs. It covers methods from molecular dynamics to finite element analysis and discusses their applications in predicting mechanical properties, interfacial interactions, and failure mechanisms.
"A Review of Multiscale Modeling Approaches for Polymer Nanocomposites" by Wang et al. (2006): This review article delves deeper into specific multiscale modelling techniques like molecular dynamics, Monte Carlo simulations, and micromechanics. It discusses their advantages, limitations, and potential applications in understanding the behavior of LFRNCs.
"Multiscale Modeling of Layered SiC Nanoclay/Epoxy Nanocomposites" by Park et al. (2008): This paper presents a case study on applying multiscale modelling to a specific LFRNC system – SiC nanoclay/epoxy. It demonstrates how molecular dynamics and finite element simulations can be combined to predict the mechanical properties and interfacial adhesion of the composite.
"Computational Design of Layered Nanotube-Polymer Multifunctional Composites" by Yakobson (2012): This article focuses on the application of multiscale modelling in designing LFRNCs with specific functionalities, such as enhanced electrical conductivity or thermal stability. It explores the use of molecular simulations to tailor the properties of individual components and optimize their interactions within the composite.
"Challenges and Opportunities for Multiscale Modeling of Nanocomposites" by Plimpton (2011): This paper discusses the challenges and opportunities associated with multiscale modelling of nanocomposites, including LFRNCs. It addresses issues like multiscale coupling, computational cost, and experimental validation, and highlights future research directions to overcome these limitations.
"Micromechanics Modeling of Layered Composite Materials" by Pindera et al. (2014): This book focuses on micromechanics as a multiscale modelling tool for composite materials. It covers various micromechanics models applicable to LFRNCs, including homogenization methods and analytical solutions, and demonstrates their application in predicting effective properties and analyzing stress distributions.
"A Review of Recent Progress in Multiscale Modeling and Simulation of Carbon Nanotube-Reinforced Polymer Composites" by Li et al. (2019): This review provides an update on the latest advancements in multiscale modelling of carbon nanotube-reinforced polymer composites, a specific type of LFRNC. It discusses recent developments in modelling techniques, applications, and challenges, offering insights into future research directions.
"Machine Learning for Multiscale Modeling of Materials" by Chen et al. (2020): This article explores the integration of machine learning with multiscale modelling for LFRNCs. It highlights how machine learning can be used to accelerate simulations, improve model accuracy, and extract valuable insights from simulation data.
"Multiscale Modeling Framework for Design and Performance Prediction of Nanocomposites" by Zhu et al. (2021): This paper presents a comprehensive multiscale modelling framework for designing and predicting the performance of LFRNCs. It integrates various modelling techniques across different scales and demonstrates its application in optimizing the design of a specific LFRNC system.
"A Critical Review of Molecular Simulation Methods for Polymer Nanocomposites" by Zhao et al. (2022): This review focuses on the application of molecular simulation methods, such as molecular dynamics and Monte Carlo, in understanding the behavior of polymer nanocomposites, including LFRNCs. It discusses the strengths and limitations of these methods and their contribution to advancing our knowledge of LFRNCs at the molecular level.
Refbacks
- There are currently no refbacks.