Optimum ultrasonication time will lead to the better performance for heat transfer in addition to preparation methods and thermal properties of the nanofluids. Nano particles are dispersed in base fluids like water (water-based fluids), glycols (glycol base fluids) &oils at different mass or volume fraction by using different preparation techniques. Significant preparation technique can enhance the stability, effects various parameters & thermo-physical properties of fluids. Agglomeration of the dispersed nano particles will lead to declined thermal performance, thermal conductivity, and viscosity. For better dispersion and breaking down the clusters, Ultrasonication method is the highly influential approach. Sonication hour is unique for different nano fluids depending on their response to several considerations. In this review, systematic investigations showing effect on various physical and thermal properties based on ultrasonication/ sonication time are illustrated. In this analysis it is found that increased power or time of ideal sonication increases the dispersion, leading to higher stable fluids, decreased particle size, higher thermal conductivity, and lower viscosity values. Employing the ultrasonic probe is substantially more effective than ultrasonic bath devices. Low ultrasonication power and time provides best outcome. Various sonication time periods by various research are summarized with respect to the different thermophysical properties. This is first review explaining sonication period influence on thermophysical properties of graphene nanofluids.
Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions.