Radiative heat transfer analysis of a concave porous fin under the local thermal non-equilibrium condition:application of the clique polynomial method and physics-informed neural networks  

作　　者：K.CHANDAN K.KARTHIK K.V.NAGARAJA B.C.PRASANNAKUMARA R.S.VARUN KUMAR T.MUHAMMAD 

机构地区：[1]Amrita School of Artificial Intelligence,Amrita Vishwa Vidyapeetham,Bengaluru 560035,Karnataka,India [2]Department of Studies in Mathematics,Davangere University,Davangere 577002,Karnataka,India [3]Computational Science Lab,Amrita School of Engineering,Amrita Vishwa Vidyapeetham,Bengaluru 560035,Karnataka,India [4]Department of Pure and Applied Mathematics,School of Mathematical Sciences,Sunway University,Petaling Jaya 47500,Malaysia [5]Department of Mathematics,College of Science,King Khalid University,Abha 61413,Saudi Arabia

出　　处：《Applied Mathematics and Mechanics(English Edition)》2024年第9期1613-1632,共20页应用数学和力学（英文版）

基　　金：funding this work through Small Research Project under grant number RGP.1/141/45。

摘　　要：The heat transfer through a concave permeable fin is analyzed by the local thermal non-equilibrium(LTNE)model.The governing dimensional temperature equations for the solid and fluid phases of the porous extended surface are modeled,and then are nondimensionalized by suitable dimensionless terms.Further,the obtained nondimensional equations are solved by the clique polynomial method(CPM).The effects of several dimensionless parameters on the fin's thermal profiles are shown by graphical illustrations.Additionally,the current study implements deep neural structures to solve physics-governed coupled equations,and the best-suited hyperparameters are attained by comparison with various network combinations.The results of the CPM and physicsinformed neural network(PINN)exhibit good agreement,signifying that both methods effectively solve the thermal modeling problem.

关 键 词：heat transfer FIN porous fin local thermal non-equilibrium(LTNE)model physics-informed neural network(PINN) 

分 类 号：O414.14[理学—理论物理] O174.14[理学—物理]