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In this study, the (Cu and Al2O3/water) hybrid nanofluid flow is carried out in a porous cavity. The thermophysical structures of solid materials are used from the available literature to improve the thermal performance of the base fluid. The mathematical model as a porous cavity is mainly used in the distillation process and is vital for the storage of thermal energy. The magnetic field is also employed perpendicular to the flow field and the impact of the magnetic parameter examined versus fluid motion. Similarity variables are used to transform governing equations as simplified partial differential equations. The model is solved using the control volume-based finite element method. Boussinesq–Darcy force is employed for the motion of the fluid flow, and the Koo–Kleinstreuer–Li model is used to assess the characteristics of the hybrid nanofluids. The roles of the Hartmann number, Rayleigh number, porosity factor in the porous medium, and drag fin improve traditional fluids’ thermal distribution presentation. Recent results predict that the two different kinds of nanoparticles speed up the heat transfer through the porous cavity. The percentage analysis shows that the hybrid nanofluids (Cu and Al2O3/water) are prominent in improving traditional fluids’ thermal distribution. Finally, the grid sensitivity test is also carried out for hybrid nanoparticles to demonstrate that the results are asymptotically coherent.

In this article, natural transform iterative method has been used to find the approximate solution of fractional order parabolic partial differential equations of multi-dimensions together with initial and boundary conditions. The method is applicable without any discretization or linearization. Three problems have been taken as test examples and the results are summarized through plots and tables to show the efficiency and reliability of the method. By practice of a few iterations, we observe that the approximate solution of the parabolic equations converges to the exact solution. The fractional derivatives are considered in the Caputo’s sense.

This study examines the generation of irreversibility and the behavior of stagnation point hybrid nanofluid flow on a Riga plate. The effects of nonlinear thermally convective and solar radiation are incorporated in the modeled equations. The nanoparticles of (Cu) and (Al2O3) are mixed with Glycol (C3H8O2) to hybridized it. The leading equations have been changed to dimension-free form by using the set of appropriate variables and then have been evaluated by Artificial Neural Network (ANN) approach. It is revealed in this work that, the velocity panels are amplified with expansion in Grashof number and electromagnetic factor while declined with escalation in magnet/electrode factor and nanoparticles concentration. Upsurge, in Eckert number for both the scenarios (Ec < 0) and (Ec > 0), the radiation factor and nanoparticles concentration cause augmentation in thermal characteristics. Radiation factor has positive impacts on Bejan number and generation of Entropy. Moreover, Bejan number is retarded while entropy is augmented with growth in Brinkman number. It is also established in this work that the principle of entropy generation for hybrid nanoparticles supports the efficient delivery of drug in cancer treatment. [ABSTRACT FROM AUTHOR]

Hybrid nanofluids are employed to enhance the antibacterial effect produced by solar radiation. Antimicrobial properties are present in metal nanoparticles (such as silver, copper, or zinc) or metal oxides (such as titanium dioxide or zinc oxide) when exposed to solar radiation. Nanomaterials that have antimicrobial properties are selected from the available literature. A solar sheet with an inclined plane has been chosen and filled with tri‐hybrid nanofluids (THNFs). Hybrid nanofluids including (CuO), Copper oxide, TiO2 (Titanium oxide), and SiO2 (Silicon dioxide), are selected from metal and metal oxide classes including water as a base fluid. The antimicrobial action caused by solar radiation is enhanced by the slip boundaries and variable porous space. The influence of flow and thermal fields on isotherms, velocities, flow lines, and Nusselt numbers are considered. The transformed system of differential equations is solved by the Control Volume Finite Element Method (CVFEM) and RK‐4 technique. The nanoparticulate volume fraction of CuO, TiO2, and SiO2 is largely responsible for the enhancement in the heat transfer rate (HT), as observed. Improved thermal performance is achieved through the flow of THNFs, which in turn acts as an antimicrobial agent. Increasing values of (ϕ=ϕ1+ϕ2+ϕ3,M,Ec,Rd,Q)$(\phi = {\phi }_1 + {\phi }_2 + {\phi }_3,M,Ec,Rd,Q)$ parameters lead to an improvement in the heat transfer rate, which in turn decreases microbial activity. [ABSTRACT FROM AUTHOR]

In this work, computational intelligence methodologies are used to investigate the trihybrid nanofluid, a new theoretical model with remarkable thermal transmission properties to enhance liquid thermal performance. The nanostructure Cu, Al2O3, and TiO2 were immersed in the base liquid (C2H6O2) to produce (Cu + Al2O3 + TiO2/C2H6O2) trihybrid nanofluid. In a Darcy-Forchheimer porous medium over a stretching Riga sheet, this study examines the electromagnetic ternary hybrid nanofluid flow under various slip situations. The study takes into account the complex interactions between a number of variables, including as viscous dissipation, radiation, heat sources, and chemical reactions. Similarity transformations are used to convert complex partial differential equations for flow, energy, and concentration into nonlinear ordinary differential equations. The highly nonlinear equations of the problem are solved numerically with the use of techniques from the bvp4c approach. The results of the bvp4c method produce the reference dataset required for the Levenberg-Marquardt backpropagation of neural networks (LMBNN). The neural network performance is validated using regression analysis, mean square errors, and error histogram data. The model problem's consistency and precision are evaluated using the absolute error, which is given for each model instance at around 10-06 - 10-08, 10-05 - 10-10 and 10-06 05-10-09. In order to reduce mean square error, the nonlinear fluid dynamics system's numerical solutions have been taken into consideration. Using the comparative configurations of MSE, error histograms, state transitions, correlation, and regression, the reliability and competence of the stochastic technique are verified. [ABSTRACT FROM AUTHOR]

This study investigates the flow of Jeffrey nanofluid through the gap between a disk and a cone, incorporating the influences of thermophoresis and Brownian motion within the flow system. Suitable variables have used to convert the modeled equations to dimension-free notations. This set of dimensionless equations has then solved by using Levenberg Marquardt Scheme through Neural Network Algorithm (LMS-NNA). In this study, it has been observed that the absolute error (AE) between the reference and target data consistently falls in the range 10-4 to 10-5 demonstrating the exceptional accuracy performance of LMS-NNA. In all four scenarios it has noticed that transverse velocity distribution has declined with augmentation in magnetic and Jeffery fluid factors by keeping all the other parameters as fixed. It is evident that the optimal validation performance 2.8227×10-9 has been achieved at epoch 1000 for the transverse velocity when cone and disk gyrating in opposite directions. [ABSTRACT FROM AUTHOR]