Neural Computing Approach to the Flow and Thermal Analysis of Ternary Hybrid NanofluidOver Wedges

The steady state motion of a ternary hybrid nanofluid across a permeable wedge is investigated in the case when the wedge is stretching or contracting. An applied magnetic field and the effect of radiation are included in the analysis. An application of a similarity transformation to the governing equations that describe the flow of ternary hybrid nanofluid results in the conversion of those equations into ordinary differential equations. All of these equations are solved numerically using the bvp4c solver in MATLAB. Additionally, the flow of the ternary hybrid nanofluid is governed by the nonlinear differential equations that are then solved by a hybrid approach combining artificial neural networks with Levenberg– Marquardt backpropagation technique. The performance and hence the comparison of algorithms are carried out using regression analysis, error histograms, function fitting graphs, and mean squared error results. To achieve the desired results, the considered parameters are varied systematically to be the stretching/shrinking parameter (λ), the magnetic parameter (M), the suction/injection parameter (S), the wedge angle (m), and the radiation parameter (Rd). In terms of performance, the velocity and temperature profiles for the proposed model demonstrated strong accuracy, as evidenced by the mean squared error values of [2.251× 10−9, 5.5339 ×10−10, 1.2449 × 10−9, 1.7511 × 10−8, 3.1377 × 10−9]. According to the investigation, solutions are found up to a particular suction and stretching/shrinking strength. An increase in the wedge angle parameter, the critical value associated with the thermal response decreases. Additionally, the heat transfer rate provided by the ternary hybrid nanofluid is greater than those possessed by the conventional nanofluids. Moreover, an increase in the radiation parameter enhances the heat transferring rate.