Analysis of Maxwell nanofluid model with convective variations, variable-density and heat generation in a porous...

This study introduces a novel analysis of Maxwell nanofluid flow over a stretching surface, integrating multiple coupled physical mechanisms previously overlooked in the literature. The comprehensive model incorporates boundary convection, internal heat generation, Ohmic heating, temperature-dependent transport properties (viscosity, density, and diffusivity), and viscous dissipation effects. This holistic approach distinguishes the current work from earlier studies, which have not simultaneously examined the coupled effects of these interacting mechanisms within a unified Maxwell nanofluid model. A mathematical model is developed and solved numerically using similarity transformations, the shooting method, and the fourth-order Runge-Kutta scheme. Validation against established benchmarks confirms the method's reliability. Further, the results demonstrate the coupled influence of key parameters on flow, thermal, and concentration profiles, along with their effects on skin friction, heat transfer (Nusselt number), and mass transfer (Sherwood number) rates. This study addresses a significant research void while providing useful theoretical guidance for the design and optimization of polymer processing, heat exchanger systems, and nanofluid-based thermal management technologies. A central revelation of this research is the dual role of temperature-sensitive density: it simultaneously amplifies thermal distribution while attenuating both nanoparticle concentration and fluid velocity. This parameter further enhances thermal and mass transfer rates while increasing the surface drag, indicating its important role in the coupled transport behavior of Maxwell nanofluids. Additionally, heat generation and convective mechanisms, as anticipated, elevate temperature profiles but inhibit flow momentum, resulting in suppressed velocity and refined control over transport behavior. Quantitatively, increasing the porous parameter enhances the skin-friction coefficient, local Nusselt number, and local Sherwood number by approximately 20.8%, 45.6%, and 3.0%, respectively, while increasing the density parameter raises these quantities by about 75.1%, 79.8%, and 69.2%, respectively. These quantitative improvements highlight the theoretical potential of the proposed model to support the design and optimization of heat exchangers, polymer processing, and nanofluid-based thermal management systems, while accounting for the associated increase in surface drag.