Predicting Young’s modulus of agglomerated graphene/polymer using multi-scale modeling

Abstract

A novel combination of concurrent and hierarchical multi-scale modeling approach is developed to predict Young’s modulus of graphene/polymer composites. In this novel approach, laminate analogy and generalized method of cells are integrated into the same framework. The former takes into account orientation, while the latter is utilized to address the effect of graphene dispersions on the results. A phenomenological technique is employed for capturing various graphene dispersion inside the matrix avoiding heavy stochastic modeling. Although stochastic modeling is preferred, due to the randomness of agglomeration and orientation of embedded graphene nanosheets in the matrix, deterministic modeling is developed in this research to prevent huge computational efforts. The results are compared with previously developed stochastic modeling and more precision is observed. The developed modeling strategy is validated using published experimental observations taken from the literature by highlighting the accuracy of the proposed technique.