Comparative Numerical Study of Thermal Features Analysis between Oldroyd-B Copper and Molybdenum Disulfide Nanoparticles in Engine-Oil-Based Nanofluids Flow

Apart from the Buongiorno model, no effort was ably accomplished in the literature to investigate the effect of nanomaterials on the Oldroyd-B fluid model caused by an extendable sheet. This article introduces an innovative idea regarding the enforcement of the Tiwari and Das fluid model on the Oldroyd-B fluid (OBF) model by considering engine oil as a conventional base fluid. Tiwari and Das's model takes into account the volume fraction of nanoparticles for heat transport enhancement compared to the Buongiorno model that depends significantly on thermophoresis and Brownian diffusion impacts for heat transport analysis. In this paper, the thermal characteristics of an Oldroyd-B nanofluid are reported. Firstly, the transformation technique is applied on partial differential equations from boundary-layer formulas to produce nonlinear ordinary differential equations. Subsequently, the Keller-box numerical system is utilized to obtain final numerical solutions. Copper engine oil (Cu-EO) and molybdenum disulfide engine oil (MoS2-EO) nanofluids are considered. From the whole numerical findings and under the same condition, the thermodynamic performance of MoS2-EO nanofluid is higher than that of Cu-EO nanofluid. The thermal efficiency of Cu-EO over MoS2-EO is observed between 1.9% and 43%. In addition, the role of the porous media parameter is to reduce the heat transport rate and to enhance the velocity variation. Finally, the impact of the numbers of Reynolds and Brinkman is to increase the entropy.

Automated summary

This study introduces an innovative approach of applying Tiwari and Das fluid model on the Oldroyd-B fluid model, focusing on the thermal characteristics of engine oil nanofluids. Results show that the thermal performance of MoS2-EO nanofluid is higher than that of Cu-EO nanofluid, and the porous media parameter reduces heat transport rate while enhancing velocity variation.