Machine Learning Investigation For Tri-Magnetized Sutterby Nanofluidic Model with Joule Heating In Agrivoltaics Technology

Abstract

This paper aims to establish a panoramic foundation for investigating the impact of sunlight on a recently formulated water-based tri-nano hybrid Sutterby liquid (TNHF) under provided magnetic field, directed through photovoltaic solar panels by exploiting the knacks of machine intelligent computing paradigm. The comparative performance of hybrid and tri-nano fluidic system with water as a base fluid is exhaustively analyzed and discussed. This research is unique, as it has a distinguished figurative comparison analysis between two different types of nanofluidic materials, which helps to choose the best one for use in agrivoltaics systems, and replace the materials already in use to enhance the efficiency and performance coefficients. Furthermore, the description of the composition scheme makes this research more feasible and applicable. A numerical dataset of nonlinear mathematical model is generated by employing finite difference scheme in the recently introduced Python bvp-solver algorithm, then it is embedded into artificial intelligence (AI)-based Levenberg Marquardt neural network algorithm (LMNNA). A significant outcome of the research indicates that the integration TNHF results in a notably faster enhancement of heat transfer rate and temperature framework as compared to traditional hybrid fluid. It is observed that introducing three distinct nanomaterials of specific thermophysical characteristics enhances the thermal exchanging profile and faces an obvious flow rate dissipation in solar plate channels. The standard numerical and AI-generated results are documented to portray the stability, accuracy and efficacy of scheme in terms of iterative learning curves on MSE, error analysis, histograms and regression statistics. Additional perquisites of the methodology include cost effectiveness, time-saving ability, robustness, stability and its extendibility.