Application of machine learning to analyze Ohmic dissipative flow of \(\text{ZnO}{-}\text{SAE}50\) nanofluid between two concentric cylinders

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.