A prediction method of flow characteristics of electronic expansion valve based on improved particle swarm optimization neural network algorithm
2022-01-0204
03/29/2022
- Event
- Content
- Electronic expansion valve as a throttle element is widely used in heat pump systems and flow characteristics are its most important parameter. The flow characteristics of the electronic expansion valve (EXV) with a valve port diameter of 3mm are studied, when the refrigerant R134a is used as the working fluid. The main factors affecting the flow characteristics are researched by adopting the orthogonal experiment method, for example, inlet pressure, inlet subcool, outlet pressure and valve opening. In view of the complicated phase change of the refrigerant passing through the electronic expansion valve, it is difficult to model the flow characteristics accurately. Based on the measured experimental data, an artificial neural network method with weights and thresholds optimized by improved particle swarm algorithm is used to predict the flow characteristics of the valve. A comparative analysis is carried out with an artificial neural network method without the improved particle swarm optimization algorithm. The results show that the model with optimized weights and thresholds converges faster, mean square error is smaller and the coefficient of determination is closer to 1. The deviation of calculation results is also studied,when using different transfer functions and different numbers of neurons in the neural network. Compared with the method of using π law and polynomial fitting to predict flow characteristics, it is found that the artificial neural network method has the best predictive effect among the three methods, and most of the predicted data is in agreement with the experimental data to a large extent.
- Citation
- Liang, G., Li, L., and Shangguan, W., "A prediction method of flow characteristics of electronic expansion valve based on improved particle swarm optimization neural network algorithm," SAE Technical Paper 2022-01-0204, 2022, .