Neural network knacks to investigate thermal variability in nanofluidic fin through temperature-dependent analysis and heat generation studies

Fin as a heat sink is becoming increasingly popular in industrial and commercial sectors due to their high efficiency in cooling devices. This has resulted in a significant rise in research interest in these devices. This study is justified by its potential for improved cooling efficiency in industrial and commercial fields through a comprehensive understanding of porous fin thermal dispersion with different profiles. It brings something new by integrating hybrid nanoliquids and porous fin profiles with an advanced ANN-based method to address difficult heat transfer problems. A combination of convection, radiation, and conduction processes is used in the fin to transmit heat. The Darcy model including heat conduction by Fourier law is used in the study to model an equation that directs the heat transfer within the fin. After that, this equation is simplified into a dimensionless form, ignoring particular measurements, and the associated boundary conditions are added. In this research, intelligent stochastic schemes will be implemented by using a neural network with an optimizer to examine numerically the thermal dispersion and heat transfer within the fin. The nonlinear problem associated with fin is numerically solved by using the Levenberg-Marquardt back-propagation (LMB) based on an artificial neural network. Datasets are created by using the shooting method for distinct parameters under the combined impact of conduction, radiation and convection grounds, allowing the artificial neural network (ANN) to examine multiple scenarios. The LMB is subjected to tests, training, and validation procedures, as well as correlation assessments, to ensure that the proposed scheme is effective in estimating solutions. To validate the authenticity and convergence of the proposed scheme, a comprehensive analysis will be presented through regression testing, mean square error analysis, and histogram assessments. The results obtained by the LMB closely matched the numerical results with absolute error up to E-08\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-08$$\end{document}, indicating the effectiveness of the proposed technique. The findings suggest that temperature distribution is significantly affected by the hybrid nanoliquid's saturation level on the fin's surface as well as the porous nature of the fin. Specifically, as these parameters rise, the rate of heat transmission also increases. Furthermore, with the series of different nanofluid particles, the thermal profile ranges from 0.67 to 0.69.