Prediction of Cattaneo-Christov heat flux with thermal slip effects over a lubricated surface using artificial neural network

The lubricated systems containing fluid lubricants have the load-carrying ability. Suitable lubrication permits smooth, incessant operation of machine elements. The significant applications in engineering and industry are drag reduction, cooling of electronic devices and cooling of nuclear reactors, and many other hydrodynamic processes. In the industries, lubricants frequently exhibit non-Newtonian properties and conform to various constitutive relations. One prevalent type of lubricant is the power law fluid, which adheres to the Ostwald procedure. The present investigation focuses on the analysis of fluid flow in the purlieu of a lubricated surface, where a thin layer of variable-thickness power law fluid is used for lubrication. The effects of velocity and thermal slip with Cattaneo-Christov heat transfer are taken into account. A conversion from partial to ordinary system of equations is happened utilizing similarities. To acquire a dataset, the shooting method is utilized. An artificial neural network procedure is utilized to envisage the fluid flow by solving the governing system of partial differential equations, and testing, training, and validation procedures are arranged to generate results under different circumstances and cases of Levenberg-Marquardt backpropagation neural network. The precision of the proposed model is established by comparing the outcomes with the reference dataset. The Levenberg-Marquardt backpropagation neural network output is evaluated using mean regression illustrations, analysis of error histograms, mean square error, and dynamics of state transition. The results indicate that developed neural network models can accurately envisage thermal analysis. Furthermore, compared to other numerical performances, the current artificial neural network model can be employed in more complicated scientific models while decreasing the time and processing ability needed to solve the problem.