Stochastic Levenberg–Marquardt Neural Network Implementation for Analyzing the Convective Heat Transfer in a Wavy Fin

R. S. Varun Kumar, M. D. Alsulami, I. E. Sarris (), G. Sowmya and Fehmi Gamaoun
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R. S. Varun Kumar: Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India
M. D. Alsulami: Department of Mathematics, College of Sciences and Arts at Alkamil, University of Jeddah, Jeddah 23218, Saudi Arabia
I. E. Sarris: Department of Mechanical Engineering, University of West Attica, 12244 Athens, Greece
G. Sowmya: Department of Mathematics, M S Ramaiah Institute of Technology, Bangalore 560054, India
Fehmi Gamaoun: Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia

Mathematics, 2023, vol. 11, issue 10, 1-26

Abstract: The present research examines the steady, one-dimensional thermal distribution and heat transfer of a wavy fin. This heat transfer analysis considers convective effects as well as temperature-dependent thermal conductivity. Furthermore, a novel implementation of a neural network with backpropagated Levenberg–Marquardt algorithm (NN-BLMA)-based machine learning intelligent strategies is provided to interpret the heat transfer analysis of a convective wavy fin. The non-linear ordinary differential equation (ODE) of the study problem is converted into its non-dimensional form using the similarity transformation technique. The dimensionless equation obtained is then numerically explored via the Runge–Kutta–Fehlberg scheme. A data set for varying the pertinent parameters is generated, and an artificial neural network model is designed to estimate the heat transfer behavior of the wavy fin. The effectiveness of the proposed NN-BLMA is subsequently endorsed by analyses using a regression model, mean square error, and histograms. The findings of comprehensive computational parametric studies illustrate that the presented technique, NN-BLMA is an effective convergent stochastic numerical solver employed for the heat transfer model of the convective wavy fin. The wavy fin’s temperature dispersion optimizes as the thermal conductivity parameter rises. Heat transfer rate is higher in wavy fin compared to rectangular fin.

Keywords: fin; temperature distribution; thermal conductivity; wavy fin; artificial neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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