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Optimized physics-informed neural network for analyzing the radiative-convective thermal performance of an inclined wavy porous fin

Publication Type : Journal Article

Publisher : Case Studies in Thermal Engineering

Source : Case Studies in Thermal Engineering 64 (2024)

Url : https://www.sciencedirect.com/science/article/pii/S2214157X24014540

Campus : Bengaluru

School : School of Engineering

Department : Mathematics

Year : 2024

Abstract : The significance of radiation and inclination on the temperature dispersion of the wavy porous fin has been addressed in the present study. Also, the influence of convection and internal heat generation on the thermal dissipation of the inclined wavy porous fin (IWPF) is examined. The pertinent temperature expression of the fin is represented using basic laws, and this equation is reduced to a dimensionless form via dimensionless variables. Additionally, a mix-encoding Genetic algorithm and Particle swarm optimization technique is shown to optimize the network hyperparameters. This resolves the issue of arbitrarily identifying the Physics informed neural networks (PINN's) ideal network and successfully limits local optimization during the training phase. Further, the equation is also resolved numerically using Runge-Kutta Fehlberg's fourth-fifth (RKF-45) scheme, and the solutions are subsequently used to verify the PINN model's applicability. The temperature results estimated by PINN and their associated RKF-45 values correlate excellently, which indicates the accuracy of the applied PINN model. The obtained findings denote that reduced measures of convective-conductive variables stimulate the IWPF's thermal distribution. An inclination angle of the fin has a significant impact on the thermal variation of the IWPF.

Cite this Research Publication : Chandan, K., PudhariSrilatha, K. Karthik, M. E. Raghunandan, K. V. Nagaraja, E. A. Gopalakrishnan, RS Varun Kumar, and FehmiGamaoun. "Optimized physics-informed neural network for analyzing the radiative-convective thermal performance of an inclined wavy porous fin." Case Studies in Thermal Engineering 64 (2024):Impact Factor: 6.4

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