Assorted Graphene-Based Nanofluid Flows Near a Reversed Stagnation Point over an Inclined Permeable Cylinder

Heat flux enhancement resulting from utilization of variant graphene-based nanoparticles; graphenes, graphene nanoplatelets, graphene oxides (GOs), carbon nanotubes (CNTs which include single and multiple walled CNTs) in a water-base fluid is focussed in the present study. A steady, laminar, incompressible, mixed convective and reversed stagnation point flow together with the consideration of transverse magnetic field over varying angles of an inclined permeable cylinder is analyzed for the heterogeneous nanofluids. The governing partial differential equations based on Tiwari-Das model are reformulated into nonlinear ordinary differential equations by applying similarity expressions. A shooting procedure is opted to reformulate the equations into boundary value problems which are solved by employing a numerical finite difference code utilizing three-stage Lobatto IIIa formula in MATLAB. The effects of constructive parameters toward the model on non-dimensional velocity and temperature disseminations, reduced skin friction coefficient and reduced Nusselt number are graphically reported and discussed in details. It is observed that GOs-water has the lowest heat flux performance under increasing values of wall permeability parameter, curvature parameter and nanoparticle volume fraction as compared to other nanofluids. On contrary, our results demonstrate that graphenes-water has the highest heat flux performance as compared to SWCNTs-water across many emerging parameters considered in this study.

Automated summary

This study focuses on the enhancement of heat flux caused by the use of different graphene-based nanoparticles in water-based fluids. Through numerical simulations and analysis, it is found that graphenes-water has the highest heat flux performance, while graphene oxides-water has the lowest heat flux performance. This research is of significance for applications involving heat conduction and fluid dynamics.