Abstract
Nowadays the increasing demand for highly effective cooling devices involving radiative-convective porous fin heat sink with functionally graded material (FGM) has gained intense research attention due to their extensive use in industrial, commercial and strategic types of equipment. The present study aims to introduce a novel application of stochastic numerical computing by exploitation of Levenberg–Marquardt backpropagation (LMB) competency for performance analysis of heat sink of functionally graded material of porous fin. The dataset for LMB is generated through the shooting method for the system dynamics by Thermo-geometric variation of conduction-radiation, conduction-convection and radiation. The process of training, testing and validation are employed for network modeling with LMB procedure for different scenarios of porous fin model. The accuracy of the results is analyzed by absolute error, mean square error, error histogram and regression measures for exhaustive numerical simulation studies of proposed LMB to study the thermal performance efficiency of the porous fin heat sink involving non-homogeneous index B of FGM.
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