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In lubricating and specialty oil industries, blending is routinely used to convert a finite number of distillation cuts produced by a refinery into a large number of final products matching given specifications regarding viscosity, flash point, pour point or other properties of interest. To find the right component ratio for a blend, empirical or semi-empirical equations linking blend characteristics to those of the individual components are used. Mathematically, the problem of finding the right blend composition boils down to solving a system of equations, often non-linear ones, linking the desired properties of the blend with the properties and percentage of the blend components. This approach can easily be extended to crankcase lubricants, in which case major blend constituents are base oils, additive packages, and viscosity index improvers. Artificial intelligence (AI) tools allow accurate predictions of the basic physicochemical properties of such blends.

Despite the influence of folded metal material on the lubrication performance of engine cylinder liners has been largely investigated, its effect has not been isolated yet in terms of other surface parameters as Sa, Sq, Vo, Rpk etc. In the present contribution, the isolated effect of folded metal on the performance of engine cylinder liners was investigated by comparing the hydrodynamic and asperity contact pressures through a deterministic mixed lubrication model. From that, the friction coefficients and the engine friction losses were also estimated. The topography of a production car engine block was characterized employing a Non-Contact Surface Profiler System. Folded metal was quantified using in-house algorithms, and so its occurrences were digitally removed. Afterwards, the surfaces with and without folded metal were studied with the deterministic model.

On urban and emission homologation cycles, engines operate predominantly at low speeds and part loads where engine friction losses represent around 10% of the consumed fuel energy but would account for 25% of the fuel consumption once combustion efficiency is taken into account. Under such mild conditions, engine and engine oil temperatures are also lower than ideal. The influence of oil viscosity on friction losses are significant. By reducing lubricant viscosity, engine friction, fuel consumption and emissions are reduced. Tribological and machine learning models were investigated to predict the effect of oil viscosity on fuel consumption during the FTP75 emission cycle with the use of detailed actual emission test measurements. Oil viscosity was calculated with the measured oil temperature. As the same vehicle transient is followed in the cold and hot phases, the models were evaluated by comparing their prediction of fuel consumption in the hot phase versus the measured value.