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This is a Experimental Study study (also classified as: Quantitative Study).

September 23, 2025Open Access

Artificial Neural Network Modeling of Darcy–Forchheimer Nanofluid Flow over a Porous Riga Plate: Insights into Brownian Motion, Thermal Radiation, and Activation Energy Effects on Heat Transfer

Key Points

Heat transfer properties of Powell–Eyring nanofluid are significantly affected by Darcy–Forchheimer flow conditions, including porosity and magnetic fields.
The study shows that increasing thermal radiation and the Biot number enhances the energy curves of the nanofluid, impacting its heat transfer capacity.
Using artificial neural networks, predictions for velocity and thermal profiles align well with numerical data, achieving high accuracy with low mean squared errors.
This research provides insights into complex fluid dynamics, suggesting the ANN approach can streamline heat transfer assessments in nanofluid systems.

Abstract

Nanotechnology has become a transformative field in modern science and engineering, offering innovative approaches to enhance conventional thermal and fluid systems. Heat and mass transfer phenomena, particularly fluid motion across various geometries, play a crucial role in industrial and engineering processes. The inclusion of nanoparticles in base fluids significantly improves thermal conductivity and enables advanced phase-change technologies. The current work examines Powell–Eyring nanofluid’s heat transmission properties on a stretched Riga plate, considering the effects of magnetic fields, porosity, Darcy–Forchheimer flow, thermal radiation, and activation energy. Using the proper similarity transformations, the pertinent governing boundary-layer equations are converted into a set of ordinary differential equations (ODEs), which are then solved using the boundary value problem fourth-order collocation (BVP4C) technique in the MATLAB program. Tables and graphs are used to display the outcomes. Due to their significance in the industrial domain, the Nusselt number and skin friction are also evaluated. The velocity of the nanofluid is shown to decline with a boost in the Hartmann number, porosity, and Darcy–Forchheimer parameter values. Moreover, its energy curves are increased by boosting the values of thermal radiation and the Biot number. A stronger Hartmann number M decelerates the flow (thickening the momentum boundary layer), whereas increasing the Riga forcing parameter Q can locally enhance the near-wall velocity due to wall-parallel Lorentz forcing. Visual comparisons and numerical simulations are used to validate the results, confirming the durability and reliability of the suggested approach. By using a systematic design technique that includes training, testing, and validation, the fluid dynamics problem is solved. The model’s performance and generalization across many circumstances are assessed. In this work, an artificial neural network (ANN) architecture comprising two hidden layers is employed. The model is trained with the Levenberg–Marquardt scheme on reliable numerical datasets, enabling enhanced prediction capability and computational efficiency. The ANN demonstrates exceptional accuracy, with regression coefficients R≈1.0 and the best validation mean squared errors of 8.52×10−10, 7.91×10−9, and 1.59×10−8 for the Powell–Eyring, heat radiation, and thermophoresis models, respectively. The ANN-predicted velocity, temperature, and concentration profiles show good agreement with numerical findings, with only minor differences in insignificant areas, establishing the ANN as a credible surrogate for quick parametric assessment and refinement in magnetohydrodynamic (MHD) nanofluid heat transfer systems.

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