Transient Thermal Distribution in a Wavy Fin Using Finite Difference Approximation Based Physics Informed Neural Network

• Published in: Computer modeling in engineering & sciences Vol. 141; no. 3; pp. 2555 - 2574
• Main Authors: Alzaid, Sara Salem, Alkahtani, Badr Saad T., Chandan, Kumar, Kumar, Ravikumar Shashikala Varun
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2024
• Subjects: Aspect ratio; Conduction heating; Convection; Cooling; Electronic equipment; Finite difference method; Gas turbine engines; Heat transmission; Mathematical analysis; Neural networks; Partial differential equations; Thermal analysis; Thermodynamic properties; Turbine blades; Wavy fins
• ISSN: 1526-1506; 1526-1492; 1526-1506
• DOI: 10.32604/cmes.2024.055312

Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engineering domains. Gas turbine blade cooling, refrigeration, and electronic equipment cooling are a few prevalent applications. Thus, the thermal analysis of extended surfaces has been the subject of a significant assessment by researchers. Motivated by this, the present study describes the unsteady thermal dispersal phenomena in a wavy fin with the presence of convection heat transmission. This analysis also emphasizes a novel mathematical model in accordance with transient thermal change in a wavy profiled fin resulting from convection using the finite difference method (FDM) and physics informed neural network (PINN). The time and space-dependent governing partial differential equation (PDE) for the suggested heat problem has been translated into a dimensionless form using the relevant dimensionless terms. The graph depicts the effect of thermal parameters on the fin’s thermal profile. The temperature dispersion in the fin decreases as the dimensionless convection-conduction variable rises. The heat dispersion in the fin is decreased by increasing the aspect ratio, whereas the reverse behavior is seen with the time change. Furthermore, FDM-PINN results are validated against the outcomes of the FDM.