This work tackles the issue of enhancing heat transmission and minimizing entropy formation in tiny enclosures pertinent to thermal energy storage. It looks at how magnetohydrodynamic (MHD), non-Darcian porous media, a ternary hybrid nanofluid composition (Fe3O4–hBN–CuO/water), and triangle corrugation work together in a corrugated rectangular cavity. The goal is to figure out how these things affect convection, entropy formation, and the overall efficiency of the thermodynamic system. Utilizing the Galerkin finite element technique (GFEM), we found numerical solutions to the mathematical models for momentum, energy, and entropy generation. The effects of the porosity parameter, ternary nanoparticle concentration, Hartmann number, Darcy number, and Rayleigh number were carefully studied for the cavities' flat and triangular corrugated walls. Artificial Neural Network (ANN) model was developed and trained to predict the average Nusselt number and total entropy generation with high precision, using fewer computational resources compared to conventional CFD approaches. It is observed that the ANN model is used mostly as an ancillary prediction instrument derived from FEM-generated data, rather than as the principal computational framework. The results show that corrugated shapes improve local heat transfer by increasing the surface area and causing flow disruptions. However, too many corrugations lower the average Nusselt numbers because they cause recirculation. Higher Rayleigh numbers make buoyancy-driven convection stronger, whereas larger magnetic fields make circulation weaker, which makes conduction-dominated transport more likely and lowers entropy generation. The porosity and Darcy number have a big effect on convective intensity and entropy formation. On the other hand, the right number of nanoparticles may boost thermal conductivity without making irreversibility too high. The ANN model showed great prediction ability (MSE≈1.12 × 10⁻⁷), which proved that it works well for quickly testing Multiphysics systems. These results show that integrating ternary nanofluids, controlling porous media, and changing the magnetic field may improve thermal performance in advanced applications, including solar collectors, cooling electronics, and thermal energy storage devices. Combining ANN prediction gives us a solid base for designing and improving next-generation heat management solutions in a way that works well.

The current examination explores the magnetohydrodynamic flow and transport behavior of a Casson-based blood-derived hexa-hybrid nanofluid via a vertically oriented, mildly stenotic artery using a fractional-order framework. The hexa-hybrid nanofluid is formulated by dispersing Au, Cu, ZnO, Ag, MgO and TiO2 nanoparticles into blood, and the flow is considered highly pulsatile. Mathematical modelling is developed from the conservation laws of mass, momentum, and energy, followed by nondimensionalization under the mild-stenosis approximation. To extend the classical model to its fractional form, the Caputo–Fabrizio fractional derivative is incorporated, enabling closed-form analytical expressions for velocity and temperature through combined Laplace and Hankel transforms. The graphical results highlight the influence of key physical factors on velocity, temperature, and entropy production. The inclusion of hexa-hybrid nanoparticles notably enhances the thermal characteristics of blood due to the substantial rise in effective thermal conductivity. The velocity increases with higher Casson parameter values, whereas temperature decreases as the fractional-order parameter intensifies. Furthermore, entropy generation is found to rise with increasing thermodynamic parameters, while the Bejan number correspondingly decreases, reflecting dominant irreversibility effects within the system.

Purpose: After being motivated by the diverse applications of blood rheology, nanotechnology, magnetic field, chemical reaction, solar radiation, and non-Darcy porous media in nano-industrial, medical, and chemical engineering domains. The current computational study aims to numerically examine the influences of velocity slip, internal thermal generation or absorption, chemical reactions, and thermal radiation on magneto-hydrodynamic blood-based nanofluid flow with thermo-Brownian motion through an extending interface within a high-permeability medium. Furthermore, the sensitive analysis of flow features with respect to the independent flow parameters is considered. Design/methodology/approach: Suitable similarity equations are employed to convert the partial differential equations into ordinary differential equations together with their boundary constraints. The NDSolve method in Mathematica 11.0 is employed to numerically analyze the flow model, yielding data for the stream function, velocity profile, frictional force coefficient, temperature profile, concentration profile, local Nusselt number, and Sherwood number across several rheological parameters. Main findings: A boundary slip diminishes momentum transmission from the fluid to the surface; when velocity slip escalates, the velocity profile declines. The intensity of the thermal boundary layer escalates with the thermal Grashof number. The temperature distribution is exacerbated by the influence of radiation. As the Brownian parameter grows, the nanofluid temperature intensifies. The chemical reaction parameter substantially affects the enhancement of both skin friction and the Sherwood number. The Nusselt number is enhanced by increasing the thermal Grashof number. The sensitivity analysis indicates that the chemical reaction and concentration Grashof number significantly influence the improvement of rheological properties. Applications: The results of this work are relevant for regulating film thickness, chemical vapour deposition, drug delivery systems, and process optimization.