Darcy Forchhemier Prandtl-Eyring nanofluid flow with variable heat transfer and entropy generation using Cattaneo-Christov heat flux model: Statistical approach

In the current investigation, we studied the Prandtl-Eyring nanofluid with base fluid engine oil is flowing through a heated stretching surface with entropy production of and variable transfer analysis. Titanium dioxide (TiO2) nanoparticles (NPs) are mixes with engine oil to produce the nanofluid. By exposing the flow of the nanofluid over the heated, slippery surface, we are able to analyze the thermal transmission and flow mechanism. The effects of Darcy-Forchhemier, heat absorption/generation, Cattaneo-Cristov heat flux model and radiative flux are also taken into account in this study. Highly nonlinear partial differential equations that describe the nanofluids flow are converted into a set of ordinary differential equations via similarity transformation. The numerical technique named shooting technique is applied to obtain solution of the resulting system of ODEs. Error estimation, regression, and correlation statistical analysis techniques are used. Statistical approach is also applied on the model. In order to evaluate the constancy of the association features, the probable error is determined. Tables and figures are used to present the results. It has been noted that including nanoparticles into engine oil decreases skin friction while accelerating the transfer of energy. Deceleration is caused by the velocity field being greatly impacted by elements including porosity, the Darcy effect, and the nanoparticles volume fraction. Additionally, as the Brinkman number rises, entropy generation rises and the Bejan number falls.

Automated summary

This study investigates the thermal transmission and flow mechanisms of a Prandtl-Eyring nanofluid mixed with engine oil flowing over a heated stretching surface. The effects of various factors such as heat generation/absorption, porosity, and nanoparticles volume fraction are considered. Nonlinear partial differential equations are transformed into ordinary differential equations for analysis, and numerical techniques and statistical methods are applied for solution and evaluation.