Gradient descent machine learning regression for MHD flow: Metallurgy process

Machine learning techniques have received a lot of interest in the exploration to minimize the computational cost of computational fluid dynamics simulation. The present article investigates application of heat and mass transfer in magnetohydrodynamic flow over a stretching sheet in metallurgy proce...

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Bibliographic Details
Published in:International communications in heat and mass transfer 2022-11, Vol.138, p.106307, Article 106307
Main Authors: Priyadharshini, P., Archana, M. Vanitha, Ahammad, N. Ameer, Raju, C.S.K., Yook, Se-jin, Shah, Nehad Ali
Format: Article
Language:English
Subjects:
Computational cost
Heat and mass transfer
Learning algorithms
Magnetohydrodynamic
Nanofluid
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Summary:Machine learning techniques have received a lot of interest in the exploration to minimize the computational cost of computational fluid dynamics simulation. The present article investigates application of heat and mass transfer in magnetohydrodynamic flow over a stretching sheet in metallurgy process by employing the learning methodology based on gradient descent. It is anticipated that the consequences of the current work will show the benefits of future research to enhance the development in the domains of science and engineering. A tabular and graphical evaluation greatly demonstrates the similarity between current and previous outcomes in the prescribed fluid flow model.
ISSN:0735-1933
1879-0178
DOI:10.1016/j.icheatmasstransfer.2022.106307