Intelligent computing infrastructure for nodal/saddle stagnation point slip flow of an aqueous convectional magnesium oxide–gold hybrid nanofluid with viscous dissipation

Abstract

ABSTRACT Using the power of numerical computation through Levenberg Marquardt backpropagation ( LMB ), the fluidic issue in the present study has features of heat transfer by convection in the hybrid nanofluid suspension of magnesium oxide and gold nanoparticles. The influence of slip effects on boundary layer flow with viscous dissipation at the nodal/saddle stagnation point is examined using a mathematical model. The current study considers water to be the base fluid, MgO and Au as nanoparticles, and the circular cylinder to be the medium’s surface. Non-linear partial differential equations are converted into ordinary differential equations via similarity transformations. The dataset for Levenberg Marquardt backpropagation is generated in Mathematica software using an explicit RK-4 approach by varying different parameters such as the slip parameter ( λ ), convective parameter ( Bi ), Eckert number ( E c ∗ ) and volume fraction ( ϕ 2 ), Bi with Ec. Validation, training, and testing are utilized for network modeling through the Levenberg-Marquardt backpropagation approach for various scenarios of the fluidic model. The result shows that the base fluid’s thermal behavior is quickly increased by the MgO − Au /water hybrid nanofluid. The reliability of the findings is evaluated using autocorrelation, regression, mean square error, absolute error, and error histogram analyses.