Artificial Neural Network Model to Enhance Thermal Prediction in Circular Porous Slider System With Pollutant Ejected by an External Source

Abstract

Porous slider systems are widely relevant to lubrication, thermal management, and transfer processes where coupled fluid flow, heat transfer, and species transfer need to be controlled. Motivated by these applications, the present study examines the flow, thermal, and concentration characteristics of a fluid past a circular porous slider. The mathematical model incorporates nonlinear thermal radiation, heat generation/absorption, pollutant concentration, and magnetic‐field effects. Using appropriate similarity variables, the governing partial differential equations for velocity, temperature, and concentration are converted into nonlinear ordinary differential equations, which are numerically solved by utilizing the Lerch polynomial collocation method. Moreover, an artificial neural network model is employed to investigate the predictions of velocity, temperature, and concentration profiles. The results show that fluid flow is inhibited as the magnetic field strength increases due to the resistive Lorentz force, while the temperature profile increases substantially. The temperature and concentration profiles diminish with increasing Reynolds numbers as the thermal and concentration boundary layer thickness decreases. Additionally, the higher radiation and heat source/sink parameters enhance the temperature profile. The outcomes of the current article are essential for various types of porous lubrication systems, thermal management devices, pollutant control technologies, and industrial cooling processes.