Estimation of magnetohydrodynamic radiative nanofluid flow over a porous non‐linear stretching surface: application in biomedical research. Issue 9 (18th October 2019)

• Record Type: Journal Article
• Authors: Elayarani, Madasamy; Shanmugapriya, Marayanagaraj; Senthil Kumar, Ponnusamy
• Is Part Of: IET nanobiotechnology. Volume 13:Issue 9(2019)
• Page Start: 911
• Page End: 922
• Publication Date: 2019-10-18
• Subjects: silver -- copper -- transforms -- nanofluidics -- friction -- backpropagation -- heat radiation -- water -- external flows -- partial differential equations -- nonlinear differential equations -- boundary layers -- Runge‐Kutta methods -- mass transfer -- flow through porous media -- magnetohydrodynamics; magnetohydrodynamic radiative nanofluid flow -- nonlinear stretching surface -- biomedical research -- thermal radiation -- chemical reaction -- magnetohydrodynamic flow -- nonlinear porous stretching surface -- viscous dissipation -- similarity transformation -- governing boundary layer equations -- nonlinear ordinary differential equations -- shooting method -- Runge–Kutta–Fehlberg fourth–fifth‐order integration scheme -- flow field -- backpropagation neural network -- Cu–water nanofluid -- Ag–water nanofluid -- skin friction coefficient -- Nusselt number -- Sherwood number -- artificial neural network -- Ag‐H2 O -- Cu‐H2 O; Biotechnology -- Periodicals; Nanotechnology -- Periodicals; 660.6
• DOI: 10.1049/iet-nbt.2018.5427
• Languages: English
• ISSNs: 1751-8741

Abstract

Abstract : The present study investigates the effects of thermal radiation and chemical reaction on magnetohydrodynamic flow, heat, and mass transfer characteristics of nanofluids such as Cu–water and Ag–water over a non‐linear porous stretching surface in the presence of viscous dissipation and heat generation. Using similarity transformation, the governing boundary layer equations of the problem are transformed into non‐linear ordinary differential equations and solved numerically by the shooting method along with the Runge–Kutta–Fehlberg fourth–fifth‐order integration scheme. The influences of various parameters on velocity, temperature, and concentration profiles of the flow field are analysed and the results are plotted graphically. A backpropagation neural network is applied to predict the skin friction coefficient, Nusselt number, and Sherwood number and these results are presented through graphs. The present numerical results are compared with the existing results and are found to be in good agreement. The results of artificial neural network and the obtained numerical values agree well with an error <5%.