https://doi.org/10.1140/epjp/s13360-025-06141-2
Regular Article
Application of machine learning to analyze Ohmic dissipative flow of 
nanofluid between two concentric cylinders
Department of Mathematics, Faculty of Sciences, HITEC University, Taxila Cantt, Pakistan
Received:
14
May
2024
Accepted:
16
February
2025
Published online:
5
March
2025
The present research work aims to investigate a steady laminar flow of a nano-lubricant Zinc Oxide–Society of Automotive Engineers 50 alias between two concentric cylinders under the effects of Ohmic dissipation and thermal radiation. With the help of conservation laws, a theoretical controlling model for the flow and heat transmission has been developed. The model consisting of a system of partial differential equations has been reduced to a system of nonlinear ordinary differential equations by using similarity transformation. Solution approximation to the resulting system is carried out using artificial neural networks along with the Bayesian regularization technique. The reference data to train and test the network has been obtained by employing the Lobatto IIIA algorithm. To show the correctness of the approximation algorithm, different metrics, such as mean squared loss, error histogram, regression analysis, and function fit plots, are observed. Our graphical simulation shows that the Ohmic dissipation directly leads to an increase in temperature by converting electrical energy into heat. Conversely, the local rate of heat transfer falls due to Ohmic dissipation.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
