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Neural network approach for thermal analysis of magnetized radiative micropolar hybrid nanofluid flow on a variable porous stretching sheet with Cattaneo-Christov flux model

MetadataDetails
Publication Date2025-07-01
JournalAdvances in Mechanical Engineering
AuthorsEbrahem A. Algehyne, Mashael A. Aljohani, Khadijah M. Abualnaja, Wafa F. Alfwzan, Arshad Khan
InstitutionsTaif University, University of Tabuk
Citations1

This work examines the micropolar hybrid nanofluid flow on a variable porous elongating sheet using inclined magnetic effects. The fluid is set into motion by stretching nature of the sheet. The effects of heat source/sink and thermal radiation have been utilized in this work together with thermophoresis and Brownian motion effects. Additionally, the Cattaneo-Christov flux model is used to accurately describe mass and heat diffusion by accounting for relaxation time effects, providing a more realistic representation of thermal transport phenomena. The leading equations have been solved using artificial neural network (ANN) method. It has noticed in this work that, when the diamond nanoparticles augments from 0.00 to 0.05 the Nusselt number surges from 0.00% to 8.27% while on the same range of nanoparticles volume fraction it augments from 0.00% to 8.46% for diamond + copper nanoparticles that ensures the dominance of hybrid nanoparticles. Thermal distributions have been increased due to the influence of thermophoresis, Brownian motion, magnetic effects, radiation, and heat source factor while they have decreased with a rise in the thermal relaxation parameter. The linear velocity of the fluid has decreased due to the increase in magnetic factor and variable porous factor. The validation of current work has ensured through comparative analysis of current results with established data set, with a fine agreement among all the results.

  1. 1995 - Enhancing thermal conductivity of fluids with nanoparticles (No. ANL/MSD/CP-84938; CONF-951135-29)