Exploring the Flow Dynamics of MHD Hybrid Nanofluid over a Non-Flat Porous Surface Using Neural Network Approach

Abstract

T he current work examined the heat transfer performance of TiO2 Al2O3/kerosene oil flowing over a permeable non-flat plate. The energy equation is modeled using Fourier’s law, and heating is supported by Joule heating and nonlinear thermal radiation. Moreover, the momentum equation is extended with mixed convection, magnetic field, and porous medium. We applied an artificial neural network (ANN) to the data obtained from the bvp4c solver to show the significance of AI techniques in predicting skin friction coefficient (SFC) and local Nusselt number (LNN) values. The ANN is developed with the Levenberg-Marquardt backpropagation algorithm to predict the values precisely, and its signif icance is assessed in terms of mean square error (MSE) between values obtained by the bvp4c solver and the predicted value neural network. T heoutcomes revealed that the heat transfer rate decayed with the rise in variable thermal conductivity. Moreover, when increasing the magnitude of the mixedconvectionparameter,thefluidvelocity constantly increases.

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