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Artificial neural network (ANN) analysis on thermophysical properties of magnetohydrodynamics flow with radiation in an arc-shaped enclosure with a rotating cylinder
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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.
Description:
Date:
2024
Authors:
Bairagi, T.
Md. Jahid Hasan
Hudha, M.N.
Azad, Abul Kalam
M.M. Rahman
Publisher:
Elsevier Ltd.
