Journal
ALEXANDRIA ENGINEERING JOURNAL
Volume 66, Issue -, Pages 1031-1050Publisher
ELSEVIER
DOI: 10.1016/j.aej.2022.12.034
Keywords
ANN; LMM; Squeezing flow; MHD; Cattaneo-Christov heat flux model; Intelligent computing; Numerical computing; Regression analysis
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This study investigates the unsteady-two-dimensional squeezing flow of magnetohydrodynamic Jeffrey fluid between two parallel plates and explores the heat transfer characteristics using the Cattaneo-Christov heat flux model and Artificial Neural Network.
The present communication examines the unsteady-two-dimensional (2-D) squeezing flow of magnetohydrodynamic (MHD) Jeffrey fluid between two parallel plates (HT2DUSMHDJF). In its own plane, the bottom channel plate is extended while the upper plate squeezes towards the lower plate. The complete structure is takan is a rotating frame. Cattaneo-Christov heat flux model (CCHFM) is forced to explore the features of heat transfer. Distinct the conventional position, Instead of the Fourier heat conduction law, the heat flux is implemented by the Cattaneo-Christov theory. The resultant systems are computed through Artificial Neural Network (ANN). The behaviors of a number of relevant parameters are analyzed through graphs and numerical data. The velocity profile increases for Deborah number b and squeezing parameter sq and decreases for rotation, magnetic and relaxation time parameter x, M, and k1 respectively. Also the Skin friction coefficient decreases for k1 and Sq and increases for high value of Deborah numbers b, x and M.(c) 2022 THE AUTHORS. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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