Artificial neural network (ANN) analysis on thermophysical properties of magnetohydrodynamics flow with radiation in an arc-shaped enclosure with a rotating cylinder

T Bairagi; Md Jahid Hasan; M N Hudha; A K Azad; M M Rahman

doi:10.1016/j.heliyon.2024.e28609

Artificial neural network (ANN) analysis on thermophysical properties of magnetohydrodynamics flow with radiation in an arc-shaped enclosure with a rotating cylinder

Heliyon. 2024 Mar 24;10(7):e28609. doi: 10.1016/j.heliyon.2024.e28609. eCollection 2024 Apr 15.

Authors

T Bairagi¹, Md Jahid Hasan², M N Hudha¹, A K Azad³, M M Rahman¹

Affiliations

¹ Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh.
² Department of Mechanical and Production Engineering, Islamic University of Technology, Board Bazar, Gazipur, 1704, Bangladesh.
³ Department of Natural Sciences, Islamic University of Technology, Board Bazar, Gazipur, 1704, Bangladesh.

Abstract

The objective of this research is to examine the thermophysical features of magnetic parameter (Ha) and time step (τ) in a lid-driven cavity using a water-based Al₂O₃ nanofluid and the efficacy of ANN models in accurately predicting the average heat transfer rate. The Galerkin weighted residual approach is used to solve a set of dimensionless nonlinear governing equations. The Levenberg-Marquardt back propagation technique is used for training ANN using sparse simulated data. The findings of the investigation about the flow and thermal fields are shown. Furthermore, a comparative study and prediction have been conducted on the impact of manipulating factors on the average Nusselt number derived from the numerical heat transfer analysis. The findings of the research indicate that, in the absence of magnetohydrodynamics, a rise in the Hartmann number resulted in a drop in both the fluid velocity profile and magnitude. Conversely, it was observed that the temperature and Nusselt number exhibited an increase under these conditions. The mean temperature of the fluid rises as the Hartmann number drops, reaching a peak value of 0.114 when Ha = 0. The scenario where Ha = 0, representing the lack of magnetohydrodynamics, shows the highest average Nusselt number, whereas the instance with Ha = 45 presents the lowest Nusselt number. The ANN model has a high level of accuracy, as seen by an MSE value of 0.00069 and a MAE value of 0.0175, resulting in a 99% accuracy rate.

Keywords: Artificial neural network; Lid-driven arc-shaped cavity; Rotating cylinder; Two-layer feed-forward model; Water-based Al2O3 nanofluid.