Deep Learning-driven analysis using artificial neural networks of hydrodynamic and radiative behavior of SAE oil-based hybrid nanofluid over stretching surface

The rotating flow of radiative hybrid nanofluid consist of titanium alloy and aluminum NPs nanoparticles moving over a stretching/shrinking surface is numerically studied. This work has direct application in high performance lubrication systems for aerospace engines and industrial cooling processes, where enhanced thermal conductivity and reduced friction are critical. The hybrid nanofluid (HNF) is made by scattering of titanium alloy (Ti 6 Al 4 V) and aluminum (AA7075) nanoparticles (NPs) in SAE (society of automotive engineers) 50 engine oil. Engine oil with a 50-grade viscosity offers passable lubrication in thrilling conditions, such as Mobil HD 50, SAE 50 API CD and SAE 50. Furthermore, the impact of thermal radiation, surface permeability, aligned magnetic field, heat source and convective condition are also examined. The intentions of the current study are to discourse and deliver momentous valuations of the aforesaid physical characteristics and energy issues using numerical and artificial neural networking approaches. The flow model is initially solved by employing the Matlab package “bvp4c” to prepared the dataset. The data set is further deal by the artificial neural networks (ANNs) based on Levenberg-Marquardt back propagation (LMBP). Using the data sets, various operations such as training, testing and validation are carried out by developing a scheme for a wide range of fluid parameters. The accuracy of LMBP-ANNs is assessed using curve fitting, error histogram and statistical regression. It can be noticed that the intensifying numbers of Ti 6 Al 4 V and AA7075 NPs diminish the fluid velocity and thermal distribution of Hnf. The influence of aligned magnetic field (rising from 0 ∘ to 90 ∘ ) also diminishes the fluid flow. The findings are relevant to optimizing heat exchangers and automotive cooling systems, where nanoparticles-enhanced fluid improve energy efficiency.