Thermal investigation of water-based radiative magnetized micropolar hybrid nanofluid flow subject to impacts of the Cattaneo-Christov flux model on a variable porous stretching sheet with a machine learning approach
Showkat Ahmad Lone1, Zehba Raizah2, Rawan Bossly3, Fuad S. Alduais4, Afrah Al-Bossly4, and Arshad Khan5,†
1 Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, (Jeddah-M), Riyadh-11673, Saudi Arabia; 2 Department of Mathematics, College of Science, Abha, King Khalid University, Saudi Arabia; 3 Department of Mathematics, College of Science, Jazan University, Jazan 82817, Saudi Arabia; 4 Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; 5 College of Aeronautical Engineering, National University of Sciences and Technology, Sector H-12, Islamabad 44000, Pakistan
Abstract This work investigates water-based micropolar hybrid nanofluid (MHNF) flow on an elongating variable porous sheet. Nanoparticles of diamond and copper have been used in the water to boost its thermal conductivity. The motion of the fluid is taken as two-dimensional with the impact of a magnetic field in the normal direction. The variable, permeable, and stretching nature of sheet's surface sets the fluid into motion. Thermal and mass diffusions are controlled through the use of the Cattaneo-Christov flux model. A dataset is generated using MATLAB bvp4c package solver and employed to train an artificial neural network (ANN) based on the Levenberg-Marquardt back-propagation (LMBP) algorithm. It has been observed as an outcome of this study that the modeled problem achieves peak performance at epochs 637, 112, 4848, and 344 using ANN-LMBP. The linear velocity of the fluid weakens with progression in variable porous and magnetic factors. With an augmentation in magnetic factor, the micro-rotational velocity profiles are augmented on the domain due to the support of micro-rotations by Lorentz forces close to the sheet's surface, while they are suppressed on the domain due to opposing micro-rotations away from the sheet's surface. Thermal distributions are augmented with an upsurge in thermophoresis, Brownian motion, magnetic, and radiation factors, while they are suppressed with an upsurge in thermal relaxation parameter. Concentration profiles increase with an expansion in thermophoresis factor and are suppressed with an intensification of Brownian motion factor and solute relaxation factor. The absolute errors (AEs) are evaluated for all the four scenarios that fall within the range - and are associated with the corresponding ANN configuration that demonstrates a fine degree of accuracy.
Fund: The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through large Research Group Project (Grant No. RGP2/198/45). Project supported by Prince Sattam bin Abdulaziz University (Grant No. PSAU/2025/R/1446).
Showkat Ahmad Lone, Zehba Raizah, Rawan Bossly, Fuad S. Alduais, Afrah Al-Bossly, and Arshad Khan Thermal investigation of water-based radiative magnetized micropolar hybrid nanofluid flow subject to impacts of the Cattaneo-Christov flux model on a variable porous stretching sheet with a machine learning approach 2025 Chin. Phys. B 34 064401
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