Effects of Hall Current and Thermal Radiation on the Time-Dependent Swirling Flow of Hybrid Nanofluids over a Disk Surface: A Bayesian Regularization Artificial Neural Network Approach

For automobile and aerospace engineers, implementing Hall currents and thermal radiation in cooling systems helps increase the performance and durability of an engine. In the case of solar energy systems, the effectiveness of heat exchangers and solar collectors can be enhanced by the best use of hy...

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Veröffentlicht in:AppliedMath Jg. 4; H. 4; S. 1503 - 1521
Hauptverfasser: Nazir, Faisal, Bhowmike, Nirman, Zahid, Muhammad, Shoaib, Sultan, Amin, Yasar, Shahid, Saleem
Format: Journal Article
Sprache:Englisch
Veröffentlicht: MDPI AG 01.12.2024
Schlagworte:
(CoFe2O4-Al2O3) nanoparticles
Bayesian regularization backpropagation algorithm
Hall current
heat source/sink
hybrid nanofluid
thermophoresis and Brownian motion
ISSN:2673-9909, 2673-9909
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Zusammenfassung:For automobile and aerospace engineers, implementing Hall currents and thermal radiation in cooling systems helps increase the performance and durability of an engine. In the case of solar energy systems, the effectiveness of heat exchangers and solar collectors can be enhanced by the best use of hybrid nanofluids and the implementation of a Hall current, thermophoresis, Brownian motion, a heat source/sink, and thermal radiation in a time-dependent hybrid nanofluid flow over a disk for a Bayesian regularization ANN backpropagation algorithm. In the current physical model of Cobalt ferrite CoFe2O4 and aluminum oxide Al2O3 mixed with water, a new category of the nanofluid is called the hybrid nanofluid. The study uses MATLAB bvp4c to unravel such intricate relations, transforming PDEs into ODEs. This analysis enables the numerical solution of several BVPs that govern the system of the given problem. Hall currents resulting from the interaction between magnetic fields and the electrically conducting nanofluid, and thermal radiation as an energy transfer mechanism operating through absorption and emission, are central factors for controlling these fluids for use in various fields. The graphical interpretation assists in demonstrating the character of new parameters. The heat source/sink parameter is advantageous to thermal layering, but using a high Schmidt number limits the mass transfer. Additionally, a backpropagation technique with Bayesian regularization is intended for solving ordinary differential equations. Training state, performance, error histograms, and regression demonstration are used to analyze the output of the neural network. In addition to this, there is a decrease in the fluid velocity as magnetic parameter values decrease and a rise in the fluid temperature while the disk is spinning. Thermal radiation adds another level to the thermal behavior by altering how the hybrid nanofluid receives, emits, and allows heat to pass through it.
ISSN:2673-9909
2673-9909
DOI:10.3390/appliedmath4040080