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

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.

Automated summary

This article focuses on the transient thermal dispersal within a moving plate using the non-Fourier heat flux model. An innovative artificial neural network strategy with the Levenberg-Marquardt backpropagation scheme is proposed for determining the transient temperature. The energy model is simplified into a non-dimensional form, and the partial differential equation is numerically solved using the finite difference method.