Exploration of Arrhenius activation energy and thermal radiation on MHD double-diffusive convection of ternary hybrid nanofluid flow over a vertical annulus with discrete heating
Document Type
Article
Publication Date
1-1-2025
Abstract
The primary objective of this article is to examine the effect of discrete heating on MHD doublediffusive convection and thermal radiation of ternary hybrid nanofluid flow heat and mass transfer in a vertical cylindrical annulus along with Arrhenius activation energy and chemical reaction. In this study, the cavity inner wall has two distinct flush-mounted heat sources, while the outer wall is isothermally cooled at a lower temperature. The top and bottom walls are thermally insulated. The ensuing equations that govern the physical framework are solved using the implicit Crank-Nicholson finite difference technique. As the heater advances upward, the flow intensity decreases, leaving a part of the fluid static at the bottom of the cylinder. Because more heat induces high buoyant flow in the annulus, the absolute value of axial velocity and wall temperature rises as the length of the heat source rises. Enhancing the values of activation energy parameter drops the Arrhenius energy function, elevating the pace of the generative chemical process and hence the concentration. Increasing the thermal radiation parameter lowers the surface heat flux while enhancing the nanofluid temperature. The Brownian motion parameter corresponds to the random motion of nanoparticles in a fluid, and this irregular movement augments the collision of nanoparticles with fluid particles, causing the particle's kinetic energy which leads to thermal energy and hence increases temperature. Also, the heat and mass transfer characteristics are forecasted and analyzed by considering the Levenberg-Marquardt backpropagating artificial neural network technique.
Keywords
Double-diffusive convection, Ternary hybrid nanofluid, Thermal radiation, Activation energy, Vertical annulus, Crank, Nicholson finite difference method, Artificial neural network model
Divisions
mechanical
Funders
Deanship of Research and Graduate Studies at King Khalid University (RGP.2/336/45)
Publication Title
Case Studies in Thermal Engineering
Volume
65
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS