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In the present work, a system approach to the tribological optimization of passenger car engines is demonstrated. Experimental data and simulation results are presented to demonstrate the role of surface specifications, ring pack, and lubricant on the piston/bore tribology. The importance of in-design “pairing” of low-viscosity motor oils with the ring pack and the cylinder bore characteristics in order to achieve maximum reduction in GHG emissions and improvement in fuel economy without sacrificing the endurance is elucidated. Earlier motored friction data for two different gasoline engines - Ford Duratec and Mercedes Benz M133 - using motor oils of different viscosity grades are now rationalized using AVL EXCITE® piston/bore tribology simulations. The main difference between the engines was the cylinder bore surface: honed cast iron vs thermally sprayed, and the valve train type: direct-acting mechanical bucket (DAMB) vs roller finger follower (RFF).

Low viscosity engine oils are considered a feasible solution for improving fuel economy in internal combustion engines (ICE). So, the aim of this study was to verify experimentally the performance of low viscosity engine oils regarding their degradation process and possible related engine wear, since the use of low viscosity engine oils could imply higher degradation rates and/or unwanted wear performance. Potential higher wear could result in a reduction in life cycle for the ICE, and higher degradation rates would be translated in a reduction of the oil drain period, both of them non-desired effects. In addition, currently limited data are available regarding “real-world” performance of low viscosity engine oils in a real service fleet.

This paper is structured into two different parts: Firstly, it describes a methodology to evaluate wear conditions in internal combustion engines in order to go beyond the classical evaluation based on specified wear concentration limits provided by engine manufacturers or commercial oil laboratories. The proposed methodology uses spectrometric wear debris measurement data and typical maintenance data to obtain a more representative parameter of wear condition, defined as “compensated wear rate”, that takes into account particular engine operating conditions affecting wear concentration measurements. Later, an evaluation of this compensated wear rate is carried out using statistical criteria and considering individual engine characteristics such as engine age, type of service, engine metallurgy, environmental conditions of work etc.

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.

Due to the increasingly stringent emissions standards in the world and, on the other hand, the foreseen shortage of fossil fuels, the application of low viscosity engine oils (LVO) is considered one of the most interesting options for counter these threats. In parallel to a fuel consumption fleet test, the aim of this study was to assess the performance of commercial low viscosity oils regarding their degradation and engine wear, since the use of LVO could imply an increase in wear rate. Potential higher engine wear could result in a reduction in the expected engine life cycle, obviously is a non-desired effect. In addition, currently limited data are available regarding “real-world” performance of LVO in a real service fleet.