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

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...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2023-05, Vol.11 (10), p.2401
Main Authors: Kumar, R. S. Varun, Alsulami, M. D., Sarris, I. E., Sowmya, G., Gamaoun, Fehmi
Format: Article
Language:English
Subjects:
Air conditioning
Algorithms
Analysis
artificial neural network
Artificial neural networks
Convective heat transfer
Dimensionless numbers
Electric properties
fin
Heat conductivity
Heat exchangers
Heat transfer
Influence
Investigations
Machine learning
Mathematics
Neural networks
Nonlinear differential equations
Parameters
Productivity
Radiation
Regression models
Runge-Kutta method
Temperature dependence
temperature distribution
Thermal conductivity
wavy fin
Wavy fins
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11102401