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Magnetic Hydrodynamic Flow and Heat Transfer of Williamson Nanofluids in a Porous Medium Impact of Chemical Reactions and Melting Effects | ||
| Journal of Computational Applied Mechanics | ||
| دوره 57، شماره 2، تیر 2026، صفحه 173-194 اصل مقاله (1.93 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jcamech.2025.405184.1675 | ||
| نویسندگان | ||
| Muhammad Saad* 1؛ Muhammad Sulaiman1؛ Muhammad Fawad Khan2؛ Ghaylen Laouini3؛ Rashid Ashraf4؛ Fahad Sameer Alshammari5 | ||
| 1Department of Mathematics, Abdul Wali Khan University, Mardan, 23200, Mardan, Pakistan | ||
| 2School of Information Technology and Systems, University of Canberra, Canberra ACT 2617, Australia | ||
| 3College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait | ||
| 4Scuola Internazionale Superiore di Studi Avanzati Via Bonomea 265, 34136 Trieste, Italy | ||
| 5Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia | ||
| چکیده | ||
| Radiation and chemical reaction effects on the steady magnetohydrodynamic (MHD) boundary layer flow of Williamson nanofluid through a porous medium over a horizontally linearly stretching sheet are numerically investigated, incorporating coupled influences of melting heat transfer and nanoparticle dispersion. The governing partial differential equations are reduced to a system of nonlinear ordinary differential equations using similarity transformations and solved via the fourth-order Runge-Kutta (RK-4) method to generate reference datasets. A novel supervised machine learning framework, Feed-Forward Neural Network optimized with the Backpropagated Levenberg-Marquardt Algorithm (FFNN-BLMA), is proposed, trained on 1001 data points with 70% training, 15% validation, and 15% testing splits. The FFNN-BLMA yields exceptional predictive accuracy with absolute errors ranging from 10⁻⁸ to 10⁻¹⁰ across velocity temperature and concentration profiles, validated through 10-fold cross-validation, error histograms, regression analysis, and curve superposition. Parametric studies reveal that increasing the melting parameter enhances velocity and reduces temperature, while the chemical reaction parameter diminishes concentration trends consistent with prior literature. Skin friction, Nusselt, and Sherwood numbers are computed to quantify engineering performance. The FFNN-BLMA outperforms traditional RK-4 and analytical methods in accuracy, convergence, and computational efficiency, establishing a robust, discretization-free paradigm for solving complex non-Newtonian multi-physics flow problems with potential extension to fractional-order systems. | ||
| کلیدواژهها | ||
| Melting heat transfer؛ Thermal radiation؛ Stretching surface؛ Williamson nanofluid؛ Machine learning؛ Chemical reaction؛ Artificial neural network | ||
| مراجع | ||
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