Open Access

ARTICLE

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

Sara Salem Alzaid¹, Badr Saad T. Alkahtani^1,*, Kumar Chandan², Ravikumar Shashikala Varun Kumar³

1 Department of Mathematics, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
2 Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka, 560035, India
3 Department of Pure and Applied Mathematics, School of Mathematical Sciences, Sunway University, Jalan University, Bandar Sunway, Selangor Darul Ehsan, 47500, Malaysia

* Corresponding Author: Badr Saad T. Alkahtani. Email:

(This article belongs to the Special Issue: Machine Learning Based Computational Mechanics)

Computer Modeling in Engineering & Sciences 2024, 141(3), 2555-2574. https://doi.org/10.32604/cmes.2024.055312

Received 23 June 2024; Accepted 27 September 2024; Issue published 31 October 2024

Abstract

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.

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Keywords

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