Thermo-magnetic radiative flow in porous enclosure with deep-learning parameter estimation

Abstract

This study numerically investigates convective heat transfer, fluid flow, and entropy generation within an inverted T-shaped porous enclosure filled with a hybrid nanofluid. The interplay between thermal radiation and a magnetic field is considered, as these factors significantly influence thermal acquisition processes in renewable and thermal engineering applications, particularly solar power collectors with the selected physical domain. By analyzing various combinations of flow parameters, this work aims to identify favourable conditions for maximizing heat transfer efficiency in such systems. A Darcy-Forchheimer-Brinkman-based mathematical model has been incorporated and numerically simulated using the penalty finite element method (FEM). Moreover, the variation of fluid flow, isotherms, and entropy generation is thoroughly analyzed for the variation of non-dimensional permeability parameter (Darcy number, Da), buoyancy force (Rayleigh number, Ra), magnetic field (Hartmann number, Ha), porosity value (ϵ), and thermal radiation parameter (Rd). The comprehensive result demonstrates that increasing values of Ra, Da, ϵ, and Rd intensify entropy generation, convective heat, and fluid flow phenomena, whereas the increasing value of Ha substantially weakens these phenomena. The thermal efficiency of the model attends at the lower buoyancy forces (Ra≤105) for each flow parameter (except for Ha). However, the increasing magnetic forces (Ha) substantially improve the thermal efficiency of the developed model. Additionally, this study employs a feed-forward neural network (FNN) based deep learning (DL) approach to predict the parametric influence on convective heat transport rate (Num), total entropy generation rate (Stot), and total Bejan number due to heat transport irreversibility (Beθ,tot) based on the minimally supplied experimental data for FNN training process. The trained FNN model predicts the results very quickly with 98.6% accuracy compared to FEM results, demonstrating its efficiency in approximating the parametric influence of complex mathematical model results. Moreover, this approach significantly reduces computational costs and efforts compared to traditional numerical methods, making it a valuable tool for forecasting the influence of an unknown set of flow parameters in complex mathematical models.