Comprehensive study of Maxwell-Buongiorno models with suction-injection and cubic stratification: Unlocking complex heat and mass transfer mechanisms in nonlinear convection

Heat and mass transmissions are improved by cubic stratification in nonlinear mixed convection, which has important applications in the energy, electronics, and medical technology sectors. The advancement of cooling systems and environmental management depends on nanofluids, which are valued for their exceptional thermal qualities. Nanofluids are essential in modern thermal and engineering applications because of their exceptional heat transmission characteristics. To examine heat and mass transfer in a non-Newtonian Maxwell nanofluid across a vertically extended surface, this study uses the Buongiorno model, which incorporates Brownian diffusion and thermophoresis effects. Such configurations are major in polymer processing, thermal coating, and biomedical applications. Convective boundary conditions, cubic stratification, and the effects of suction and injection velocity are all considered in this inquiry. Through parametric analysis, the effects of heat source or sink are explored, offering insights to optimize thermal management systems in applications involving nanofluid-based porous media. The NDSolve (Built-in) numerical approach is utilized to resolve the nonlinear regulating formulas. A visual representation is provided of the influences of different factors on concentration, temperature outline, and fluid flow. The skin friction, local Nusselt, and Sherwood numbers have been computed and examined numerically. The important findings show that temperature profile and concentration profile are both noticeably lessened by raising the thermal and solutal stratification value. In contrast, increasing the mixed convection parameter raises the temperature and velocity profiles. Additionally, the transport dynamics of nanoparticles are influenced by the substantial temperature boost caused by thermophoresis and Brownian motion effects. The findings exhibit strong agreement with earlier studies when compared to those published in the recent literature.