Rheological modeling of suspensions of fibrous nanoparticles in polymeric viscoelastic media

نویسندگانغلامحسین صدیفیان-احمد رمضانی سعادت آبادی-رقیه رنجبری
نشریهJ NON-NEWTON FLUID
تاریخ انتشار۲۰۱۵-۹-۰۱
نمایه نشریهISI ,SCOPUS

چکیده مقاله

A new rheological model is developed to predict viscous and elastic behavior of concentrated suspensions of short fiber and one dimensional nanoparticles in steady and transient shear flows, simultaneously. The previously presented models cannot predict such rheological behavior with a set of parameters. The reduced strain closure (RSC) model in spite of its ability to successfully reproduce the slow kinetics of orientation growth observed in concentrated short-fiber suspensions is not able to predict both the shear viscosity and the normal stress difference data together, which could be possibly due to the fact that an orientation model with a scalar interaction coefficient cannot predict all components of the orientation tensor correctly. In ad- dition the RSC model cannot predict the rheological behavior of fiber suspensions in viscoelastic media. So, a new rheological model is developed to predict the contribution of both viscoelastic polymeric matrix and fibrous particles in the shear viscosity and the normal stress difference in transient and steady shear flows, simultaneously. In this model, the fiber suspension is represented by two internal state variables namely a second order orientation tensor, a, for the fibrous particles and a second order conformation tensor, c, for the components of suspending fluid. To improve predictions of the model for behavior of nanofibers and nanorod in viscoelastic media, an interaction parameter which have been proposed by Ramazani et al. [36] adopted to represent fiber–matrix and vice versa interactions. Comparison of model predictions and experimental data for suspension of carbon nanofibers in polymeric matrix confirms that model could acceptably predict rheo- logical behavior of such suspensions in steady and transient regimes with only one set of model parameters.