Artificial neural network analysis on heat and mass transfer in MHD Carreau ternary hybrid nanofluid flow across a vertical cylinder: A numerical computation

Abstract

The objective of this study is to examine the combined impact of MHD non-Newtonian Carreau ternary nanofluid flow with mass and heat transport through a vertical stretching cylinder associated with a chemical reaction and radiation parameter. The main aim of this study is to increase the thermal efficiency using three different categories of nanoparticles: copper (Cu), aluminium oxide (Al₂O₃), and titanium dioxide (TiO₂), with H₂O serving as the original fluid is calculated. Applying similarity transformation, partial differential equations can be transformed into ordinary differential equations which are nonlinear, and simplified numerically using the bvp4c technique in the MATLAB software. This work provides new information about the behaviour of heat plumes under magnetic fields, thermal radiation and flow, and has potential applications in enhancing cooling systems in industrial applications, modelling of oil reservoirs and nuclear waste storage. Nanoparticles are used for cooling processors, cancer therapy, medicine, metal strips, automobile engines, welding equipment, fusion reactions, chemical reactions, and for cooling heat exchange mechanisms in various engineering devices due to their superior thermo physical properties. The novel characteristics of a variety of physical parameters on velocity, temperature, concentration, skin friction coefficient, and Nusselt number and mass transfer rate are discussed via graphs, charts, and tables. Results of this investigation indicate that the temperature of modified nanofluids decreased through the increasing amounts of radiation (0.5≤ Nr≤ 20.5), prandtl number (6.2≤ Pr≤ 21) and heat source (0.1≤ Q≤ 0.9) while the opposite impression for the volume fraction of Al₂O₃ (0.05≤ ${\varphi }_{A{l}_2{O}_3}\le$ 0.30) nanoparticles. The skin friction rate and Nusselt number rises as the Weissenberg parameter (0.1≤ We≤ 1.3) enhances. The nanoparticles concentration decays when the Schmidt number (0.1≤ Sc≤ 0.9) and chemical reaction (0.1≤ Kr≤ 0.5) are increased. The results in this limited scenario are consistent with previously published studies. Moreover, neural networking model is constructed to enhance the accuracy of predicting kinetic energy values. The comparison of numerical values and ANN predicted values are displayed through graphs, which are in good agreement.