Assessment of Power-Law Viscosity Models for Binary Lubricating Oil–EN 590 B7 Diesel Mixtures as a Tool for Evaluating Diesel Oil Dilution

Diesel oil dilution of lubricating oil is an important phenomenon affecting its viscosity and, consequently, the lubrication conditions and operational reliability of internal combustion engines. The viscosity of a lubricating oil–biodiesel oil mixture can therefore be used as a parameter for assessing the degree of fuel dilution. This study evaluates the applicability of selected power-law viscosity models for describing binary mixtures of lubricating oil and EN 590 diesel oil containing 7% bio-component (fatty acid methyl esters) by mass. Two lubricating oils, SAE 30 and SAE 40 grades, were investigated at diesel oil concentrations of 0, 1, 2, 10, 20, 50 and 100% by mass over a temperature range of 40–100 °C. The experimental results were compared with four power-law models, the linear, Kendall–Monroe, Koval and Bingham models, with the Arrhenius and REFUTAS models included as reference models. The novelty of the study lies in the systematic comparison of the power-law models over a broad range of temperature and dilution levels and their performance assessment against the Arrhenius and REFUTAS models. The relative error between calculated and experimental viscosities was determined for all investigated conditions. The linear model showed the lowest accuracy, followed by the Kendall–Monroe and Bingham models. The Koval model demonstrated the highest accuracy among the power-law models, with a maximum relative error of less than 9%, outperforming the Arrhenius model and showing accuracy comparable to that of the REFUTAS model. The results also show that model suitability depends on mixture composition and temperature, and that the distribution of prediction errors should be considered alongside their magnitude. The Koval model may therefore provide a simple and sufficiently accurate alternative for describing lubricating oil–diesel oil mixtures and assessing diesel oil dilution.