Heat transfer optimization in magnetohydrodynamic buoyancy-driven convective hybrid nanofluid with carbon nanotubes over a slippery rotating porous surface

Abstract

Hybrid nanofluids containing carbon nanotubes possess the potential to augment thermal conductivity and are also employed in heat management applications. These nanofluids combine two kinds of nanostructures (single-wall and multi-wall) and have better energy conversion, cooling, and heat transmission qualities. Because of their tiny size and strength, carbon nanotubes (CNT) are used to increase machinery and components lubrication and boost system energy storage and charging cycle effectiveness of lithium-ion batteries. Therefore, a mathematical model is formulated to study the hydromagnetic CNT hybrid nanofluid mixed convective flow past an elongating porous surface in the occurrence of external heat source, thermal linear radiation, viscous and Joule dissipation. The nanoparticle diameter and interfacial layer effects are explored by incorporating the Gharesim dynamic viscosity model and Hamilton–Crosser thermal conductivity model. The partial differential equations (PDEs) defining the considered fluid flow are transformed into ordinary differential Equations (ODEs) utilizing predefined similarity transformations. The numerical Runge-Kutta method and shooting procedure are employed to obtain the outcomes. The current study establishes that the liquid momentum is controlled for the slip flow, thus with the slip condition, the amount of retardation is much higher in comparison with the no-slip condition, and the temperature of the hybrid nanofluid has been raised by a greater heat source coefficient and radiation factor. Further, the sensitivity and optimization analysis of the heat transmission rate is carried out using RSM with face-centered central composite design model of experiments. Sensitivity analysis reveals that the highest evaluated value 0.006330 of heat transmission rate is identified at the uncoded values ${\varphi }_{\text{SWCNT}}=0.01,{\varphi }_{\text{MWCNT}}=0.01,N=0.10$ and the least value −0.002590 is identified at the uncoded values of ${\varphi }_{\text{SWCNT}}=0.01,{\varphi }_{\text{MWCNT}}=0.03,N=0.10$