由曹祥、李则宇、邵亮亮、张春路（通讯作者）撰写的论文Refrigerant flow through electronic expansion valve: experiment and neural network modeling在Applied Thermal Engineering, 2016年第92卷在线发表。论文研究多种不同的电子膨胀阀的特性（实验数据）和实验关联式，并基于计算精度、计算速度、通用性等方面的要求，提出了一种简洁的神经网络模型，获得了令人满意的效果。

论文链接：http://www.sciencedirect.com/science/article/pii/S1359431115009837

免费下载链接：http://authors.elsevier.com/a/1Rvt44r6KujEyj(有效期至2015年12月11日)

论文摘要：

Electronic expansion valve (EEV) plays a crucial role in controlling refrigerant mass flow rate of refrigeration or heat pump systems for energy savings. However, complexities in two-phase throttling process and geometry make accurate modeling of EEV flow characteristics more difficult. This paper developed an artificial neural network (ANN) model using refrigerant inlet and outlet pressures, inlet subcooling, EEV opening as ANN inputs, refrigerant mass flow rate as ANN output. Both linear and nonlinear transfer functions in hidden layer were used and compared to each other. Experimental data from multiple sources including in-house experiments of one EEV with R410A were used for ANN training and test. In addition, literature correlations were compared with ANN as well. Results showed that the ANN model with nonlinear transfer function worked well in all cases and it is much accurate than the literature correlations. In all cases, nonlinear ANN predicted refrigerant mass flow rates within ±0.4% average relative deviation (A.D.) and 2.7% standard deviation (S.D.), meanwhile it predicted the EEV opening at 0.1% A.D. and 2.1% S.D.