An artificial neural network approach to comparative aspects: A predictive analysis of magnetic dipole on the heat transfer of maxwell hybrid nano coolants flow in an inclined cylinder

SAE 10W-30 synthetic blended over-based oil with hybrid nano coolants and magnetic dipole effects provides enhanced thermal characteristics and lubricating properties for sophisticated vehicle systems. This study examines the convective flow of Maxwell hybrid nanofluid in inclined and vertical cylinders, highlighting the influence of magnetic dipoles. New governing equations are formulated using SAE 10W-30 as the base fluid, incorporating ferrous ferric oxide (Fe₃O₄) and graphene oxide (GO) nanoparticles, along with thermal and solutal stratification boundary conditions. The implementation of MATLAB with its built-in bvp4c numerical solver converts the partial differential equations into ordinary differential equations, and a multilayer perception -artificial neural network ( ANN) using the Levenberg-Marquardt algorithm predicts skin friction, Nusselt, and Sherwood numbers. Numerical data consisting of 110 points is obtained via bvp4c and implemented in the ANN model, where the training, validation, and testing are 70 %, 15 %, and 15 % respectively as the input data. The ANN accurately predicts performance metrics for inclined and vertical cylinders validated through mean square error (MSE), regression analysis, and error distribution. Results highlight that higher reciprocal magnetic prandtl numbers decrease flow velocity and induced magnetic profiles while increasing temperature and concentration fields. From the result of ANN, applied performance metrics include the mean square error (MSE) = 0.0081488, coefficient of determination (R) = 0.99858 while MSE = 0.000031011and R = 1 for the inclined and vertical cylinders respectively. Comparing the findings of this study with those of previous studies shows much similarity. This predictive analysis optimizes engine cooling and lubrication under varying thermal and flow conditions. The addition of Magnetic dipole effects enhances heat transfer, reduces drag, and improves lubrication, offering better engine protection than regular engine oil at both low and high temperatures. This research demonstrates the potential of hybrid nanofluids with magnetic dipole effects to improve engine efficiency and extend component lifetime in challenging conditions. • This research shows great enhancements in cooling and lubricating of engines via adding magnetic dipole effects to SAE 10W-30 synthetic blended engine oil, with Fe 3 O₄ and GO. Fluid flow analysis of two different cylinder positions suggests that heat transfer behavior depends on additive-magnetic field interactions. • Provides a new dataset using MATLAB's bvp4c to calculate skin friction, Nusselt number, and Sherwood number, which are verified through MLP-ANN for improved accuracy in fluid dynamics predictions. • Adding magnetic dipole effects enhances heat transfer minimizes drag and offers superior lubrication to give better engine oil protection than the normal engine oils at both low and high temperatures, which in turn enhances the performance of engines at cold start and high temperatures.