Numerical study of radiative Maxwell viscoelastic magnetized flow from a stretching permeable sheet with the Cattaneo–Christov heat flux model

Shahid, A; Bhatti, MM; Beg, OA; Kadir, A

Abstract

In this article, the Cattaneo-Christov heat flux model is implemented to study non-Fourier heat and mass transfer in the magnetohydrodynamic (MHD) flow of an upper convected Maxwell (UCM) fluid over a permeable stretching sheet under a transverse constant magnetic field. Thermal radiation and chemical reaction effects are also considered. The nonlinear partial differential conservation equations for mass, momentum, energy and species conservation are transformed with appropriate similarity variables into a system of coupled, highly nonlinear ordinary differential equations with appropriate boundary conditions. Numerical solutions have been presented for the influence of elasticity parameter (), magnetic parameter (M2), suction/injection parameter (λ), Prandtl number (Pr), conduction-radiation parameter (Rd), sheet stretching parameter (A), Schmidt number (Sc), chemical reaction parameter (γ_c), modified Deborah number with respect to relaxation time of heat flux (i.e. non-Fourier Deborah number) on velocity components, temperature and concentration profiles using the successive Taylor series linearization method (STSLM) utilizing Chebyshev interpolating polynomials and Gauss-Lobatto collocation. The effects of selected parameters on skin friction coefficient, Nusselt number and Sherwood number are also presented with the help of tables. Verification of the STSLM solutions is achieved with existing published results demonstrating close agreement. Further validation of skin friction coefficient, Nusselt number and Sherwood number values computed with STSLM is included using Mathematica software shooting quadrature.

Citation

Shahid, A., Bhatti, M., Beg, O., & Kadir, A. (2017). Numerical study of radiative Maxwell viscoelastic magnetized flow from a stretching permeable sheet with the Cattaneo–Christov heat flux model. Neural Computing and Applications, 30(11), 3467-3478. https://doi.org/10.1007/s00521-017-2933-8

• Journal Article Type: Article
• Acceptance Date: Feb 28, 2017
• Online Publication Date: Mar 9, 2017
• Publication Date: Mar 9, 2017
• Deposit Date: Mar 8, 2017
• Publicly Available Date: Mar 9, 2018
• Journal: Neural Computing and Applications
• Print ISSN: 0941-0643
• Electronic ISSN: 1433-3058
• Publisher: Springer Verlag
• Volume: 30
• Issue: 11
• Pages: 3467-3478
• DOI: https://doi.org/10.1007/s00521-017-2933-8
• Publisher URL: http://dx.doi.org/10.1007/s00521-017-2933-8
• Related Public URLs: http://link.springer.com/journal/521