ANALYZING ACTIVATION ENERGY AND BINARY CHEMICAL REACTION EFFECTS WITH ARTIFICIAL INTELLIGENCE APPROACH IN AXISYMMETRIC FLOW OF THIRD GRADE NANOFLUID SUBJECT TO SORET AND DUFOUR EFFECTS
Anum Shafiq, Andaç Batur Çolak, Tabassum Naz Sindhu
Abstract
The use of nanotechnology has led to the design of many modern and more cost-effective implementation, such as solar power generation, the redevelopment of heat exchangers, and the modernization of the medical and pharmaceutical industries. In this study, the combined effects of activation energy with binary chemical reactant in a steady magnetohydrodynamic mixed convective third-grade nanofluid flow by radially radiative stretching plate has been analyzed with an artificial intelligence approach. Heat transfer analysis was conducted with heat generation, Joule heating, and Soret and Dufour effects. By incorporating appropriate transformations, the initial nonlinear coupled partial differential equations expressing the fluid model were formed as a comparable nonlinear ordinary differential equations system. Three different artificial neural network models were proposed in order to predict the skin friction, Nusselt number, and Sherwood number values of the fluid model by the Shooting Runge-Kutta Fehlberg 4, technique using the data set created by taking various values of the relevant parameters. It is worthy of noting that the average deviation values for each output parameter remained less than 5%. Furthermore it is also observed that mean square error values for skin friction coefficient, local Nusselt number, and local Sherwood number values were attained as 3.63 × 10-3, 4.03 × 10-4, and 8.62 × 10-3, respectively. The obtained results show that artificial neural networks are an engineering tool that can be used with high accuracy to estimate the combined effects of activation energy and binary chemical reaction in a fixed magnetohydrodynamic mixed convective third-grade nanofluid flow with a radial radiative stretched plate.