Thermodynamics second-law analysis in an unsteady conduction and radiation heat transfer system with internal heat source

Zhang, Zhongnong; Lou, Chun

doi:10.1016/j.ijheatmasstransfer.2023.123848

Cited by 3 publications

(1 citation statement)

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“…Their results indicate that the heat removal rate is augmented as the velocity near the active wall increases. Zhang and Lou [5] validated an improvised thermodynamic analysis method to evaluate the efficiency and irreversibility of the radiative-conductive heat transfer process. Their findings indicate that, as the heat generation rate increases, total exergy increases, and the amount of heat generation exhibits more prominence on the radiative entropy generation rate.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Heat Transfer Behavior of Porous Wavy Fin with Radiation and Convection by Using a Machine Learning Technique

Kumar,

Nimmy,

Nagaraja

et al. 2023

Symmetry

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The impact of convection and radiation on the thermal distribution of the wavy porous fin is examined in the present study. A hybrid model that combines the differential evolution (DE) algorithm with an artificial neural network (ANN) is proposed for predicting the heat transfer of the wavy porous fin. The equation representing the thermal variation in the wavy porous fin is reduced to its dimensionless arrangement and is numerically solved using Rung, e-Kutta-Fehlberg’s fourth-fifth order method (RKF-45). The study demonstrates the effectiveness of this hybrid model, and the results indicate that the proposed approach outperforms the ANN model with parameters obtained through grid search (GS), showcasing the superiority of the hybrid DE-ANN model in terms of accuracy and performance. This research highlights the potential of utilizing DE with ANN for improved predictive modeling in the heat transfer sector. The originality of this study is that it addresses the heat transfer problem by optimizing the selection of parameters for the ANN model using the DE algorithm.

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Section: Introductionmentioning

confidence: 99%