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

作　　者：Sara Salem Alzaid Badr Saad T.Alkahtani Kumar Chandan Ravikumar Shashikala Varun Kumar 

机构地区：[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

出　　处：《Computer Modeling in Engineering & Sciences》2024年第12期2555-2574,共20页工程与科学中的计算机建模（英文）

基　　金：supported by the Researchers Supporting Project number (RSPD2024R526),King Saud University,Riyadh,Saudi Arabi.

摘　　要：Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engi-neering 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.

关 键 词：Heat transfer CONVECTION FIN machine learning physics informed neural network 

分 类 号：O17[理学—数学]