Computational modeling study demonstrates accurate prediction of MHD nanofluid heat and mass transfer in porous media, highlighting efficient thermal simulation.
Inspired by practical applications, this study investigates MHD heat and mass transfer in nanofluid flow over a two-dimensional boundary layer in a porous medium, considering radiation, viscous dissipation, Brownian motion, and thermophoresis effects. Suitable transformations reduce the governing nonlinear partial differential equations to ordinary differential equations (ODEs). The resulting boundary-layer equations are solved using the Finite Element Method (FEM). A Levenberg-Marquardt neural network strategy (LMNNS) is then employed to approximate the solutions for different parameter values. A dataset is generated from the FEM solutions and used for training, validation, and testing of the neural network. The performance of the LMNNS is evaluated using gradient analysis, regression, mean squared error (MSE), and histogram analysis. Numerical comparisons further demonstrate the effectiveness and accuracy of the proposed approach. The effects of magnetic, thermophoresis, pressure gradient, permeability, Lewis, Eckert, Brownian motion, and Prandtl parameters on the flow characteristics are also analyzed.
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Shah et al. (2026) studied this question.
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