由李则宇、邵亮亮、张春路（通讯作者）撰写的论文Fin-and-tube condenser performance modeling with neural network and response surface methodology在International Journal of Refrigeration, 2015年第59卷在线发表。论文通过将响应面方法与神经网络模型相结合，将翅片管冷凝器神经网络模型训练所需样本下降至30~40组（原先需要数百组训练样本）。这项研究使得相关实验量降低到了可以接受的程度，对于换热器神经网络模型的实用化具有重要意义。

论文链接：http://www.sciencedirect.com/science/article/pii/S0140700715002108

免费下载链接：http://authors.elsevier.com/a/1S0hdV-Tm8oZy(有效期至2015年12月31日)

论文摘要：

This paper presents a new approach of combining response surface methodology and neural network for performance evaluation of fin-and-tube air-cooled condensers which are widely used in refrigeration, air-conditioning and heat pump systems. Box–Behnken design (BBD) and Central Composite design (CCD) are applied to collect a small dataset for neural network training, respectively. It turns out that 41 sets of data are collected for heating capacity and refrigerant pressure drop, and 9 sets of data are collected for air pressure drop. Additional 2000+?sets of data are served as the test data. Compared with the test data, for the heating capacity, the average deviation (A.D.), standard deviation (S.D.) and coefficient of determination (R2) of trained neural network are ?0.43%, 0.98% and 0.9996, respectively; for the refrigerant pressure drop, those are ?2.09%, 4.98% and 0.996, respectively; and for the air pressure drop, those are 0.11%, 1.96% and 0.992, respectively. Classical quadratic polynomial response surface models were also included for reference. By comparison, the developed neural networks gave much better results. Moreover, the proposed method can remarkably downsize the neural network training dataset and mitigate the over-fitting risk.