由杨亮、李则宇、邵亮亮（通讯作者）、张春路（通讯作者）撰写的论文Model-based dimensionless neural networks for fin-and-tube condenser performance evaluation在国际制冷学报International Journal of Refrigeration, 2014年第48卷在线发表。论文提出了一种基于机理模型和量纲分析法的翅片管冷凝器无量纲神经网络模型，进一步提高了冷凝器神经网络模型的统一性。

论文链接：http://www.sciencedirect.com/science/article/pii/S0140700714000206

Free access:http://authors.elsevier.com/a/1PtkiV-Tm4zzE(until December 6, 2014)

论文摘要：The paper presents a dimensionless neural network modeling method for the fin-and-tube refrigerant-to-air condensers which are widely used in air-cooled refrigeration and heat pump systems. The model-based dimensional analysis method is applied to develop the dimensionless Pi-groups for the condenser performance. The three-layer perceptron neural network is served as the performance model using the dimensionless Pi-groups as its inputs and outputs. Compared with a well-validated tube-by-tube first-principle model, the standard deviations of trained dimensionless neural networks are 0.66%, 4.83% and 0.11% for the heating capacity, the refrigerant pressure drop and the air pressure drop, respectively. The accuracy is also consistent with the previously developed dimensional neural networks. Furthermore, independent model validation using different refrigerants shows that the dimensionless models have good potential in predicting the condenser performance if the Pi-groups were in the range of training data.