Mass flow rate prediction of a direct-expansion ice thermal storage system using R134a based on dimensionless correlation and artificial neural network

Accurately predicting the refrigerant mass flow rate through the electronic expansion valve (EEV) is crucial for improving the system's performance and achieving intelligent control. However, the refrigerant mass flow rate model applicable to the direct-expansion ice thermal storage (DX-ITS) system for a wide range of flow rates is rare in the open literature. In this study, Buckingham-pi theorem and artificial neural network (ANN) are adopted to predict the refrigerant mass flow rate through the EEV of the DX-ITS system using R134a. The dimensionless pi-groups and optimal number of neurons in hidden layers of ANN model are obtained. The obtained ANN model shows good accuracy, and over 95.7 % of the predicted data are within the 15 % error band. Results indicate that the EEV outlet pressure has a significant impact on the refrigerant mass flow rate compared with the inlet pressure, the average increase in refrigerant mass flow rate is 43.76 kg/h with an outlet pressure increase of 0.05 MPa. Moreover, the refrigerant mass flow rate gently rises with the increase of superheat temperature under fixed EEV inlet and outlet pressure. On average, every 2 degrees C increase in superheat temperature leads to an approximately 3.98 kg/h increase in refrigerant mass flow rate.