Implementation of numerical strategy and neural networking paradigm to inspect ternary nanofluid flow over a radiated paraboloid surface with Arrhenius activation energy: A comparative study

Remarkable advantages of the newly introduced composition of nanoparticles by combining three distinctively natural nanometric-sized particles have been demonstrated to improve the energy efficiency of the advanced mechanisms. Some diversified and effective utilizations of the currently conducted exploration have been noticed in thermal exchange systems, solar and power plants, material engineering, manufacturing and metallic extrusion procedures, renewable energy resources, and many more. Subsequently, this work involved an innovative domain called the paraboloid surface, on which a very small number of studies have commenced. Therefore, the primary motive for articulating this effort is to envision the hydrothermal and mass attributes of a viscous base liquid characterized by ethylene glycol (C 2 H 6 O 2 ) with a suspension of aluminum oxide (Al 2 O 3 ), cobalt iron oxide (CoFe 2 O 4 ), and titanium oxide (TiO 2 ). The significance of magnetic field improvisation, along with the radiative flux and activation energy, are also examined. The leading mathematical model is based on a partial differential system involving the thermophysical characteristics of dispersed particles. A set of similar variables is chosen to transmuted the fundamental expressions into a dimensionless ordinary differential structure. The attained set of dimensionless ODEs is resolved numerically by executing computations based on shooting and RK procedures using a MATLAB-based code. The results are extracted in a comparative form to inspect the performance of ternary nanoparticle induction over mono and hybrid compositions. The momentum, thermal, and concentration distributions are sketched in a graphical version, and in comparison, the format against the involved parameters. The prediction of outcomes for the associated quantities was evaluated using a backpropagated ANN strategy. The fit of the data was also confirmed by presenting regression plots. From the insight of the calculated outcomes, it is revealed that the heat flux coefficient and skin friction factors increased with the addition of ternary nanoparticles, irrespective of the change in parameters. Furthermore, the skin friction factor escalates up to 49% with the magnetic field effect compared to its absence. Increment in the magnitude of heat flux coefficient up to 5% is perceived in the occurrence of radiation effect, while heat flux coefficient diminishes up to 23% with heat generation/absorption.