A comparative review of multiscale models for effective properties of nano- and micro-composites

Modelling and simulation techniques are now considered an essential practice for the materials industry. In order to gain insight into factors that can affect the final properties of a particular composite system, several design criteria need to be considered to create a more realistic composite behaviour. Such criteria would include the geometrical shape of reinforcements, interfacial bonding strength, types of reinforcement materials, and analytical formula utilised. In addition, multiscale modelling and simulation of composite materials require the consideration of various length and time scales, especially nano-composites. In terms of applicability, not all approaches can be used or would be effective in all situations to deliver reasonable results. A fair degree of accuracy and efficiency needs to be achieved when conducting multiscale modelling of any composite type. The selection of a competent model depends on its ability to provide rational results and precise estimations while requiring a short or moderate console execution time at a low computational cost. In this paper, various multiscale techniques of heterogeneous materials are critically reviewed in the context of modelling effective properties of nano- and conventional composites. The models are developed considering the frameworks of hierarchical type approaches and concurrent methods. Multiscale techniques are classified into three categories, namely sequential, parallel and synergistic methods. Subdivisions under those three major categories will be further explained. Interfacial models representing the interphase zone between inclusions and the matrix material are also discussed and categorised into interphase and interface models. Examples of modelling techniques are implemented for further illustrations. Comprehensive, detailed schematic charts are created to summarise and compare the different modelling approaches. The archival value of this paper is to provide fast and straightforward decision-making guidance based on the required criteria for which each model would be applied.