Machine learning algorithm of thermo-diffusion effects on 10W40 engine oil hybrid nanomaterial for automotive engine cooling

This machine learning model can improve automotive engine cooling by properly predicting the heat transfer performance of 10W40 engine oil hybrid nanomaterials under thermo-diffusion conditions. It also helps to develop energy-efficient lubricating systems, which reduce friction, improve fuel efficiency, and increase engine durability. This article provides a short analysis of the thermos-diffusion influence on a hybrid nanofluid based on 10W40 motor oil with LTNE (local thermal non-equilibrium effects). The neural network with the Levenberg-Marquardt method (NN-LMA) is the foundation of an integrated numerical computing system. In order to improve people's intellect and abilities in daily work, this field of study is especially eager to investigate and develop innovative (AI) techniques in the fields of technology and engineering. The LTE (local thermal equilibrium) model (one equation) and the LTNE model (two equations) are two fundamental models that can be used to explain convective heat transfer in porous media. The LTNE model takes into account the heat differential between the liquid and solid phases. If there is a heat disparity between the two phases, the LTE assumption may not be valid. It is now necessary to use the LTNE model and provide an extra thermal boundary condition at the one another. The liquid phase's thermal field increases as the inter-phase heat transmission characteristic upsurges, while the solid phase's temperature field declines.Clinical trial: This study is not a clinical trial.

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