Adaptive lubricants involve binary mixture of synthetic oil and dissolved carbon dioxide (CO2). Unlike conventional lubricant oils, the lubricant viscosity not only varies with the temperature within the bearing, but also can be directly adjusted through the CO2 concentration in the system. In this study, we investigated the performance of adaptive lubricants in a hybrid journal bearing considering the synthetic oil to be fully saturated by CO2. The adaptive lubricant analyzed for this study was the polyalkylene glycol (PAG) oils with low concentration of CO2 (< 30%). A three-dimensional computational fluid dynamic (CFD) model of the bearing was developed and validated against the experimental data. The mixture composition and the resultant mixture viscosity were calculated as a function of pressure and temperature using empirical equations.
The simulation results revealed that the viscosity distribution within the PAG/CO2-lubricated bearing is determined primarily by the pressure at the low operating speed. When the speed becomes higher, it is the temperature effect that dominates the viscosity distribution within the bearing. Moreover, the PAG/CO2-lubricated bearing can reduce up to 12.8% power loss than the PAG-lubricated bearing due to the low viscosity of PAG/CO2 mixture. Most importantly, we have found the PAG/CO2 can enhance the load capacity up to 19.6% when the bearing is operating at the high speed conditions.