Estimating Young's modulus of graphene/polymer composites using stochastic multi-scale modeling

Abstract

A stochastic multi-scale approach is developed for predicting elastic properties of graphene/polymer nanocomposites. All scales of nano, micro, meso and macro are travelled through a bottom-up approach using appropriate method at each scale. For each scale, separate representative volume element (RVE) is defined capturing effective parameters of that scale. Categorized under the hierarchical multi-scale modeling, the outputs of each scale are fed into the next upper scale as input data. Semi-continuum modeling is performed at the nano and micro scales, while micromechanical model is adapted for the upper scales of meso and macro. The developed modeling is implemented stochastically to address the randomness in graphene size, volume fraction, orientation, wrinkle and also formation of agglomerated particles. Therefore, stochastic multi-scale modeling is conducted accounting for process-induced uncertainties. Results show a very good agreement with experimental data available on open literature. The novelty of this research is twofold: (1) Developing of a full-range multi-scale modeling for graphene reinforced polymers starting from nano-scale and lasting to the uppermost scale of macro, (2) Full stochastic implementation of developed modeling accounting for non-deterministic parameters induced during processing graphene reinforced polymer.