Advanced deep learning approach with physics-informed neural networks for analysing the thermal variation through a radial fin applicable in heat exchangers

The radiation impact on the thermal distribution of the radial fin with the temperature-dependent thermal conductivity is discussed in this paper. The basic governing heat equation of the radial fin is formulated with the assistance of the Fourier law of heat conduction. The dimensional heat equation of the radial fin is non-dimensionalised utilising appropriate dimensionless variables and this ordinary differential equation (ODE) is tackled by employing the physics-informed neural network (PINN) scheme. The thermal attributes of the radial fin are investigated for different parameters like convection–conduction parameter, radiation–conduction parameter and thermal conductivity parameter. The outcomes of the systematic assessments of these parameters are demonstrated with the support of graphs. The rise in the thermal conductivity variable promotes thermal variation in the fin. A decrease in radiative–conductive variable scales augments the temperature dispersal through the fin. Furthermore, PINN incorporates physics equations directly into its architecture, unlike standard numerical approaches, which frequently require extensive mathematical expertise for accuracy. This approach enables PINN to give precise findings even when working with minimal training data, saving substantial time and resources.