The stagnation point flow behavior of a micropolar nanofluid over an inclined stretching surface was numerically investigated. The formulation accounts for the combined effects of Brownian motion, thermophoresis, thermal radiation, velocity slip, and the presence of internal heat generation or absorption. The governing system of non-linear partial differential equations was transformed into a set of coupled ordinary differential equations through the application of appropriate similarity transformations. These transformed equations were solved numerically to analyze the behavior of the fluid near the stagnation region, where both the stretching velocity of the surface and the external free stream velocity are assumed to vary linearly with distance from the stagnation point. Special attention was paid to the influence of dimensionless parameters on key physical quantities, including skin friction coefficient, energy transfer, and Sherwood number. It was observed that increasing the stagnation point parameter leads to a reduction in skin friction, while the inclination angle demonstrates an opposing effect on heat and mass transfer rates. Data extracted from graphical results was tabulated to provide quantitative insights into the impact of varying parameters. The findings offer significant implications for microscale heat and mass transfer systems, particularly in processes involving inclined geometries and nanoparticle-enhanced fluids under magnetohydrodynamic (MHD) effects.
Understanding thermal transport phenomena in porous structures is of fundamental importance across diverse sectors, including energy systems, construction, electronics, and biomedical engineering. In contrast to conventional dense solids, porous materials exhibit distinct thermal behaviors due to the intrinsic discontinuity between solid phases, pore geometry, and interfacial interactions. In this review, current advances in the understanding of heat transfer mechanisms—namely conduction, convection, and radiation—within porous media were systematically analyzed, with particular emphasis on the influence of porosity, pore morphology, and material composition on effective thermal conductivity. Both open- and closed-cell architectures were examined, and their respective roles in thermal transport were clarified in relation to practical applications. The predictive capability of numerical models was shown to improve significantly through the incorporation of local thermal equilibrium (LTE) and local thermal non-equilibrium (LTNE) models, as well as homogenization techniques. State-of-the-art experimental techniques employed for characterizing thermal transport in porous materials at micro- and nanoscales were also discussed, including steady-state and transient plane source (TPS) methods, along with high-resolution imaging techniques such as X-ray Computed Tomography (XCT) and electron microscopy. Emerging computational strategies, particularly the integration of reinforcement learning and machine learning (ML) algorithms into numerical and analytical models, were identified as promising tools for optimizing the thermal performance of porous structures. Furthermore, recent progress in the development of functional nanostructured and composite porous materials has enabled enhanced performance in applications such as thermal insulation, energy storage, and medical device design. Nonetheless, several critical challenges persist, particularly in experimental reproducibility, accurate model development, and the bridging of multi-scale effects. The strategic integration of artificial intelligence (AI) and data-driven design methodologies is anticipated to play a transformative role in advancing the next generation of porous materials for sustainable thermal management solutions. The findings underscore the necessity of porous structures in accelerating low-carbon technologies and achieving energy-efficient thermal transport systems.
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