Efficient Mass Flow Prediction Through Adiabatic Capillary Tubes via Neural Networks Based on the Homogeneous Equilibrium Model

Capillary tubes are widely used as essential expansion devices in small refrigeration and air-conditioning systems. Accurate prediction of mass flow rate through adiabatic capillaries is a critical aspect of system design and optimization. While there are currently numerous models capable of predicting mass flow through capillaries, most rely on experimental data containing uncertainties, resulting in suboptimal generalization performance. Unlike previous ANN models and empirical correlations that rely on experimental data, this study addresses this limitation by introducing neural networks based on the homogeneous equilibrium model (HEM) of adiabatic capillaries. Two neural networks—a traditional multi-layer perceptron (MLP) and a deep residual network (ResNet)—are developed using a dataset generated by the HEM. The models are subsequently validated and compared against established models using experimental data for various refrigerants and operating conditions collected from the open literature. The results demonstrate that both neural networks exhibit exceptional generalization ability. The average deviations on the experimental dataset are 5.2% for the MLP and 4.5% for the ResNet, outperforming existing models. Their performance across different refrigerants is stable, with the ResNet demonstrating superior overall performance. Furthermore, the trained neural networks achieve a computational speed substantially superior to that of the HEM.