Backpropagated Neural Network Modeling for the Non-Fourier Thermal Analysis of a Moving Plate

• Published in: Mathematics (Basel) Vol. 11; no. 2; p. 438
• Main Authors: Varun Kumar, R. S., Alsulami, M. D., Sarris, I. E., Prasannakumara, B. C., Rana, Saurabh
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Approximation; Artificial intelligence; artificial neural network; Artificial neural networks; Boundary conditions; Continuous casting; Dispersion; Energy; Finite difference method; Flow velocity; Food science; Heat exchange; Heat flux; Heat transfer; Investigations; Mathematical models; moving plate; Neural networks; non-Fourier heat transfer; Parameters; Partial differential equations; Peclet number; Radiation; Temperature; Thermal analysis; thermal radiation; Thermodynamic properties
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11020438

The present article mainly focuses on the transient thermal dispersal within a moving plate using the non-Fourier heat flux model. Furthermore, the innovative, sophisticated artificial neural network strategy with the Levenberg-Marquardt backpropagated scheme (ANNS-LMBS) is proposed for determining the transient temperature in the convective-radiative plate. Using dimensionless terms, the energy model for transient heat exchange is simplified into a non-dimensional form. The arising partial differential equation (PDE) is then numerically tackled using the finite difference method (FDM). A data set for the various scenarios of the thermal parameters influencing the thermal variation through the plate has been generated using the FDM. In addition, the effect of the dimensionless physical variables on the thermal profile of a moving plate has been examined and discussed in detail. Increments in the convection-conduction and radiation-conduction parameters are figured to yield a reduction in the transient thermal dispersion. An upsurge in the Peclet number caused the improvement of thermal dispersal in the plate.