Abstract
The emerging application of nanofluids in heat transfer makes it imperative to study viscous properties. It is essential to know accurate physical properties for practical use. The viscosity of nanofluids is a significant property, and a limited number of studies are found on the modeling of Cu-gear oil-based nanofluid effective viscosity capable of handling accurate predictions. In the present study, the Cu-gear oil-based nanofluid viscosity was optimized using several process parameters such as temperature, nanofluid volume percent (NF vol.%), and shear rate. The optimization study was conducted using a response surface methodology (RSM). The multivariate empirical model is developed and employed for the optimization of process parameters. The significance of process parameters was investigated, and it was found that temperature is the most significant input parameter, followed by NF vol.% and shear rate. The input parameters for minimum viscosity were calculated as temperature (75.67 °C), NF vol.% (0.13), and shear rate (4.62).