Veröffentlichungen

Lubricant oils are used in many technical applications, e.g., in automotive, refrigeration technology, and many other industries. Reliable knowledge of thermophysical properties of such oils is essential, but modeling all of the important properties, including density, phase behavior, heat capacity, entropy, enthalpy, viscosity, and thermal conductivity, remains a key challenge today. To tackle this challenge, we propose a novel modeling approach based on treating the lubricant oil as a quasi-pure fluid, setting up a simple set of equations for all of the important properties of the oil, and developing a parameter fitting procedure using a minimal set of experimental data. This simple model set includes the Patel–Teja–Valderrama cubic equation of state, a simple expression for the ideal gas isobaric heat capacity as a linear function of temperature and residual entropy scaling for viscosity and thermal conductivity. To fit some of the parameters in this model set, two extra models are required: Raoult’s law of boiling point elevation and the modified Rackett equation. As a result, fewer than 20 (at least 12) experimental points are needed to fit all 15 parameters of a pure or quasi-pure component, and one experimental mixture bubble-point pressure is required to enable a binary system prediction. For pure or quasi-pure components, in the liquid phase and not in the vicinity of the critical point, this modeling approach has an uncertainty over large temperature and pressure ranges of less than 7% for viscosity and less than 3% for all other properties. For binary mixtures, except for viscosity, the modeling approach still yields good predictions for all other properties, typically within 8%.

Schlagwörter: Fluids, Lipids, Mathematical methods, Thermodynamic modeling, Viscosity