Rheological behavior of Graphene oxide/water nanofluid: experimental and modeling using hybrid RSM/NN

Usage of nanofluids (NFs) due to its outstanding thermal and electronic properties has been introduced for decades. Among nanoparticles (NPs) added to a base fluid, graphene oxide (GO) has found vast applications because of its superb mechanical, thermal, and electronic attributes. However, adding NPs to a fluid causes pressure drop problem. Therefore, the effects of temperature and concentration on viscosity of GO dispersed in water have been experimentally investigated in this article. A ۰.۴% weight fraction solution of GO dispersed in water was purchased and diluted by adding distillated water to make different concentration. The percentages in produced solutions were ۰.۰۵%, ۰.۱%, ۰.۲% and ۰.۴% while the temperatures were ۲۰, ۴۰, and ۶۰ degrees of Celsius. The purchased NF has been characterized using XRD, UV–Visible, and Raman spectroscopy to ensure its morphology and texture. The rheological results show that increase in nanoparticle concentration, rises NF viscosity, and increase in temperature decreases viscosity, which is in line with expectations. Moreover, Newtonian and non-Newtonian behavior observed in different shear stresses and concentrations. Therefore, a scenario using combined response surface methodology (RSM) and neural network (NN) was used to obtain the correlation in which three parameters are included: i. e. concentration, temperature, and shear rate.

کلیدواژه ها:

Graphene oxide/water nanofluid ، shear rate ، viscosity ، response surface methodology ، neural network