Analysis of homogeneous and heterogeneous reactions in catalytically active ternary nanofluids with radiative effects over permeable 3D configuration via neural network computations - Asadullah; S. Bilal; Tomas Kozubek; Muhammad Bilal Riaz; Muhammad Yasir (Results in Chemistry, 2025)
Dynamics of flowing liquids over 3D domain subjected to non-linear stretching has paramount essence in numerous phenomena related to instrumental design industry, aerospace setups, renewable energy optimization through solar and wind, molding and spinning of materials and many more. To exemplify the performance of liquids utilized in aforementioned procedures by raising heat transfer capabilities, insertion of amalgamated particles has been opted. Due to advancement in particle's structural analysis, recently, incorporation of three diverse natured particles to raise efficiency of thermal energy systems has been considered. In current study, a comparison between performance of two different tri-hybrid models (Xue and Yamada) has been executed. To make work more relevant to realistic situation, effectiveness of permeability, magnetic field and variable heat source has been included. Role of energy provided by radiative source to fluid carrying ternary nanoparticles has also been interrogated. Convective boundary constraint governed by Newtonian thermal flux is also accounted at the surface of domain along with involvement of homogenous and heterogenous reactions. Numerical simulations are manifested on developed system in ordinary differential form by using numerical approach. Back propagated neural networking procedure to predict desired outcomes and found good agreement between both numerical and neuro computing techniques. Outcomes indicates that heat flux coefficient elevates up to 15 % by utilizing physical properties of Yamada-Ota model in comparison to Xue model. It is also notified that the best validation performance of ANN Model-I is 2.44e-07 occur at 481 epochs while for Model-II this value is 1.75e-06 that happen at 776 epochs.