Experimental Study and ANN Analysis of Rheological Behavior of Mineral Oil-Based SiO2 Nanofluids

This work reports an experimental and theoretical analysis of the rheological properties of mineral oil-based SiO2 nanofluid for their potential applications in transformer insulation. The flow electrification mechanism on the nanofluids with different surfactants such as cetyl trimethyl ammonium bromide (CTAB), oleic acid, and Span 80 is studied using a spinning disk technique. The results show a higher streaming current for the nanofluids with CTAB as a surfactant compared to oleic acid and Span 80. The rheological behavior of nanofluids is explored with the double gap concentric cylinder geometry. The variation of shear stress with shear rate follows a power law relationship along with a yield stress observed for all the nanofluids. A transition is seen from storage modulus to dominant loss modulus for the nanofluids during the frequency sweep analysis, whereas no transition is observed in the case of mineral oil. In addition, regression analysis using artificial neural network (ANN) algorithms are performed on the experimentally measured viscosity of the nanofluids in order to estimate theoretical parameters and provide insights into the streaming current formation. The desirable rheological characteristics of nanofluids are identified for achieving enhanced insulation performance in transformers.

Automated summary

This work investigates the rheological properties of mineral oil-based SiO2 nanofluid for potential applications in transformer insulation. The study focuses on the flow electrification mechanism and the rheological behavior of the nanofluids with different surfactants. The results show that the addition of cetyl trimethyl ammonium bromide (CTAB) as a surfactant leads to higher streaming currents. The research also employs artificial neural network algorithms for regression analysis to estimate theoretical parameters and gain insights into streaming current formation.