A Predictive Viscosity Model for Highly Asymmetric Lubricant Oil + Synthetic Refrigerant Mixtures

Abstract

Lubricant oil is critical in refrigeration and heat pump systems, where precise viscosity models for oil + refrigerant mixtures are essential for reliable analysis. Commercial lubricants are complex mixtures with unknown compositions, and their viscosity typically exceeds that of refrigerants by 3 orders of magnitude, causing asymmetric behavior that hinders physical modeling. We propose a novel framework integrating the PC-SAFT EoS with residual entropy scaling (RES). Treating lubricant as a quasi-pure fluid, PC-SAFT characterizes mixture thermodynamics: density and bubble point pressure show 2% and 8% mean absolute deviations vs experimental data. For viscosity modeling, RES parameters for quasi-pure oils are determined using ambient-pressure data, and no additional adjustable parameters are needed for mixtures. The model achieves a 16% absolute average relative deviation from experimental viscosity, with a MATLAB package provided in Supporting Information.