Quantitative Study of Non-Linear Convection Diffusion Equations for a Rotating-Disc Electrode

Abstract

Rotating-disc electrodes (RDEs) are favored technologies for analyzing electrochemical processes in electrically charged cells and other revolving machines, such as engines, compressors, gearboxes, and generators. The model is based on the concept of the nonlinear entropy convection-diffusion equations, which are constructed using semi-boundaries as an infinite notion. In this model, the surrogate solutions with different parameter values for the mathematical characterization of non-dimensional OH− and H+ ion concentrations at a rotating-disc electrode (RDE) are investigated using an intelligent hybrid technique by utilizing neural networks (NN) and the Levenberg–Marquardt algorithm (LMA). Reference solutions were calculated using the RK-4 numerical method. Through the training, validation, and testing sampling of reference solutions, the NN-BLMA approximations were recorded. Error histograms, absolute error, curve fitting graphs, and regression graphs validated the NN-BLMA’s resilience and accuracy for the problem. Additionally, the comparison graphs between the reference solution and the NN-BLMA procedure established that our paradigm is reliable and accurate.

Stability analysis of GO‐EG and GO‐blood base nanofluids by implementing of thermal radiation phenomena

“…Alhakami et al [31] used hybrid artificial neural network approach and provided dynamical study of vector-borne viral plant diseases. Alarfaj et al [32] investigated computed stiff fractional modeling through a numerical technique for machine learning and a numerical quantitative study was carried out by Alshammari et al [33] in their paper having diffusion equations for nonlinear convection. Rahman and his coinvestigators [34] reported a nonlinear mass transfer in flow through a porous media-occupied channel with the aid of a numerical approach and hybrid heuristic paradigm.…”