Dynamic viscosity of low GWP refrigerants in the liquid phase: An empirical equation and an artificial neural network

Abstract

This study presents a simple correlation for describing the temperature and pressure dependence of the liquid dynamic viscosity of low GWP refrigerants, namely HydroFluoroOlefins (HFOs) and HydroChloroFluoroOlefins (HCFOs). The model has 3 input parameters (i.e., reduced temperature, reduced pressure, and acentric factor) and 6 coefficients which were regressed on 794 experimental data collated from the literature for 7 alternative refrigerants (i.e., R1233zd(E), R1234yf, R1234ze(E), R1234ze(Z), R1224yd(Z), R1336mzz(E), and R1336mzz(Z)). Moreover, a multi-layer perceptron neural network for the liquid dynamic viscosity of the studied fluids was developed from the selected dataset. The artificial network has the same 3 input parameters of the correlation and one hidden layer with 19 neurons. The results of the proposed correlation proved that it is an accurate model for calculating the dynamic viscosity of the studied liquid refrigerants, despite its simplicity. It ensured an average absolute relative deviation of the liquid dynamic viscosity (AARD(η)) of 2.88 %, lower than that given by other literature correlations. As expected, the multi-layer perceptron neural network provided the best results for all the selected refrigerants (AARD(η) = 0.86 % for the complete dataset), proving that it can be considered a reference for the development of other models.