Issue 21, 2023

Heat transfer analysis in magnetohydrodynamic nanofluid flow induced by a rotating rough disk with non-Fourier heat flux: aspects of modified Maxwell–Bruggeman and Krieger–Dougherty models

Abstract

Non-Newtonian fluids have unique heat transfer properties compared to Newtonian fluids. The present study examines the flow of a Maxwell nanofluid across a rotating rough disk under the effect of a magnetic field. Furthermore, the Cattaneo–Christov heat flux model is adopted to explore heat transport features. In addition, a comparison of fluid flow without and with aggregation is performed. Using similarity variables, the governing partial differential equations are transformed into a system of ordinary differential equations, and this system is then solved by employing the Runge–Kutta Fehlberg fourth-fifth order method to obtain the numerical solution. Graphical depictions are used to examine the notable effects of various parameters on velocity and thermal profiles. The results reveal that an increase in the value of Deborah number decreases the velocity profile. An increase in the thermal relaxation time parameter decreases the thermal profile. An artificial neural network is employed to calculate the rate of heat transfer and surface drag force. The R values for skin friction and Nusselt number were computed. The results demonstrate that artificial neural networks accurately predicted skin friction and Nusselt number values.

Graphical abstract: Heat transfer analysis in magnetohydrodynamic nanofluid flow induced by a rotating rough disk with non-Fourier heat flux: aspects of modified Maxwell–Bruggeman and Krieger–Dougherty models

Article information

Article type
Paper
Submitted
31 Aug 2023
Accepted
20 Sep 2023
First published
13 Oct 2023
This article is Open Access
Creative Commons BY-NC license

Nanoscale Adv., 2023,5, 5941-5951

Heat transfer analysis in magnetohydrodynamic nanofluid flow induced by a rotating rough disk with non-Fourier heat flux: aspects of modified Maxwell–Bruggeman and Krieger–Dougherty models

P. Srilatha, M. J, U. Khan, R. N. Kumar, R. J. P. Gowda, S. Ben Ahmed and R. Kumar, Nanoscale Adv., 2023, 5, 5941 DOI: 10.1039/D3NA00711A

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