Predicting mechanical properties of nanoclay/polymer composites using stochastic approach

A full stochastic multi-scale modeling technique is developed to predict Young's modulus of nanoclay reinforced polymers. Performing a top-down scanning, effective parameters of each scale are identified and categorized. The developed modeling procedure covers all scales of nano, micro, meso and macro as a bottom-up modeling approach. The modeling is performed sequentially at each scale and the outputs of analysis are transferred to the next scale as its input data. Proper modeling technique is developed/employed at each scale to efficiently encounter the identified parameters. The developed modeling is executed stochastically capturing inherently process-induced uncertainties. Volume fraction of each possible morphologies of nanoclay in polymer, number of clay platelets in those particles accommodating more than one layered silicates, size and location of inclusions, spatial orientations of particles and also non-uniform dispersion of nanoclay in matrix resulting in agglomeration phenomenon are all considered as random parameters in this research. Estimated results shows a good agreement with experimental data published in the literature.