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

被引:15
|
作者
Srilatha, Pudhari [1 ]
Madhu, J. [2 ]
Khan, Umair [3 ]
Kumar, R. Naveen [4 ]
Gowda, R. J. Punith [5 ]
Ben Ahmed, Samia [6 ]
Kumar, Raman [7 ]
机构
[1] Inst Aeronaut Engn, Dept Math, Hyderabad 500043, India
[2] Davangere Univ, Dept Studies Math, Davangere 577007, India
[3] Lebanese Amer Univ, Dept Comp Sci & Math, Byblos, Lebanon
[4] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Math, Bengaluru, India
[5] Bapuji Inst Engn & Technol, Dept Math, Davanagere 577004, India
[6] King Khalid Univ, Coll Sci, Dept Chem, POB 9004, Abha, Saudi Arabia
[7] Chandigarh Univ, Univ Ctr Res & Dev, Dept Mech Engn, Mohali 140413, Punjab, India
来源
NANOSCALE ADVANCES | 2023年 / 5卷 / 21期
关键词
MIXED CONVECTION FLOW; MASS-TRANSFER; NATURAL-CONVECTION; VERTICAL CONE; POROUS-MEDIUM; PLATE; SORET;
D O I
10.1039/d3na00711a
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页码:5941 / 5951
页数:11
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