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.

This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (h_i = 1.85 to 0.1 m), chimney inlet diameter (d_ch = 10.16 to 55.88 m), and chimney outlet diameter (d_o = 10.16 to 30.48 m). Our findings demonstrate that reducing the collector inlet height consistently leads to increased power output. The optimal collector inlet height of h_i = 0.2 m results in a significant power increase from 51 to 117.42 kW (~ 2.3 times) without additional installation costs, accompanied by an efficiency of 0.25%. Conversely, enlarging the chimney diameter decreases the chimney base velocity and suction pressure. However, as turbine-driven power generation rises, the flow becomes stagnant beyond a chimney diameter of 45.72 m. At this point, power generation reaches 209 kW, nearly four times greater than the Manzanares plant, with an efficiency of 0.44%. Nevertheless, the cost of expanding the chimney diameter is substantial. Furthermore, the impact of chimney divergence is evident, with power generation, collector efficiency, overall efficiency, and collector inlet velocity all peaking at an outer chimney diameter of 15.24 m (corresponding to an area ratio of 2.25). At this configuration, power generation increases to 75.91 kW, approximately 1.5 times more than the initial design. Remarkably, at a low collector inlet height of 0.2 m, combining it with a chimney diameter of 4.5 times the chimney inlet diameter (4.5d_ch) results in an impressive power output of 635.02 kW, signifying a substantial 12.45-fold increase. To model the performance under these diverse conditions, an artificial neural network (ANN) is effectively utilized.