由杨亮、张春路（通讯作者）撰写的论文A generalized dimensionless local power-law correlation for refrigerant flow through adiabatic capillary tubes and short tube orifices在国际制冷学报International Journal of Refrigeration, 2014年第46卷在线发表。论文提出了一种基于均相流积分模型的新的通用关联模型，对毛细管和节流短管的流量预测都可以获得很好的效果，且有效避免了神经网络模型可能过拟合的问题。

论文链接：http://www.sciencedirect.com/science/article/pii/S0140700714001601

Free access:http://authors.elsevier.com/a/1PjclV-Tm4~J1(until November 7, 2014)

论文摘要：This paper presents a new method to obtain generalized dimensionless correlation of refrigerant mass flow rates through adiabatic capillary tubes and short tube orifices. The dimensionless Pi groups were derived from the homogeneous equilibrium model, which is available for different refrigerants entering adiabatic capillary tubes or short tube orifices as the subcooled liquid, two-phase mixture, or supercritical fluid. To mitigate the potential over-fitting risk in neural network, a new “local” power-law correlation reformed from the homogeneous equilibrium model was proposed and compared with the conventional “global” power-law correlation and recently developed neural network model. About 2000 sets of experimental mass flow rate data of R12, R22, R134a, R404A, R407C, R410A, R600a and CO2 (R744) in the open literature covering capillary and short tube geometries, subcritical and supercritical inlet conditions were collected for the model development. The comparison between the recommended six-coefficient correlation and experimental data reports 0.80% average and 8.98% standard deviations, which is comparable with the previously developed neural network and much better than the “global” power-law correlation.