In this work, an attempt has been made to explore numerically the thermo-fluidic transport process in a novel M-shaped enclosure filled with permeable material along with Al₂O₃-Cu hybrid nanoparticles suspended in water under the influence of a horizontal magnetizing field. To exercise the influence of geometric parameters, a classical trapezoidal cavity is modified with an inverted triangle at the top to construct an M-shaped cavity. The cavity is heated isothermally from the bottom and cooled from the top, whereas the inclined sidewalls are insulated. The role of geometric parameters on the thermal performance is scrutinized thoroughly by changing the sidewall inclination, number, and height of the top inverted triangular undulation under similar boundary conditions. The governing equations transformed into dimensionless form are solved by using a computing code written in the finite volume approach. The analysis is conducted by considering a wide range of parametric influences like sidewall angles (γ), number (n), and height (δ) of the top triangular undulations, modified Rayleigh number (Ra_m), Darcy number (Da), Hartmann number (Ha), and hybrid nanoparticle concentrations (φ). Furthermore, the artificial neural network (ANN) technique is implemented and tested to predict the overall thermal behavior of the novel cavity to predict new cases. The results revealed that the design of sidewall inclination (γ) is an important parameter for modulating the thermo-flow physics. The M-shaped cavity (compared to trapezoidal) reveals either a rise or drop in the fluid circulation strength depending upon the magnitude of δ, but the heat transfer rate always increases due to an increase in the cooling length. The heat transfer increment is ∼61.01% as δ increases. Single undulation with higher depth is the optimum choice for achieving improved heat transfer (which may go up to ∼355.75% for $\delta$ = 0.5 and γ = 45°). A decrease in Da or Ha causes a drop in the flow strength, which consequently leads to a drop in the heat transfer rate. Furthermore, the concepts of ANN will help researchers predict the behavior for such complicated cavity shapes with a multiphysics approach. This will save efforts as well as computing time for exploring the thermal behavior of any range of a dataset.

Nanofluid refers to a liquid containing a dispersion of submicronic solid particles or nanoparticles. Nanofluids display a thermal conductivity enhancement. This phenomenon suggests the possibility of using nanofluids in electronic cooling and advanced nuclear systems. In this article, a boundary layer analysis is presented for the natural convection past a nonisothermal vertical plate in a porous medium saturated with a nanofluid. Numerical results for friction factor, surface heat transfer rate, and mass transfer rate are presented for parametric variations of the buoyancy ratio parameter, N_r; Brownian motion parameter, N_b; thermophoresis parameter, N_t; Lewis number, Le; and the power law exponent, λ. The dependency of the friction factor, surface heat transfer rate (Nusselt number), and mass transfer rate on these parameters is discussed.

A boundary layer analysis is presented for the mixed convection past a vertical wedge in a porous medium saturated with a nano fluid. The governing partial differential equations are transformed into a set of non-similar equations and solved numerically by an efficient, implicit, iterative, finite-difference method. A parametric study illustrating the influence of various physical parameters is performed. Numerical results for the velocity, temperature, and nanoparticles volume fraction profiles, as well as the friction factor, surface heat and mass transfer rates have been presented for parametric variations of the buoyancy ratio parameter Nr, Brownian motion parameter Nb, thermophoresis parameter Nt, and Lewis number Le. The dependency of the friction factor, surface heat transfer rate (Nusselt number), and mass transfer rate (Sherwood number) on these parameters has been discussed.