由李则宇、邵亮亮、张春路（通讯作者）撰写的论文Modeling of finned-tube evaporator using neural network and response surface method methodology在传热领域的国际顶级期刊Journal of Heat Transfer – Transactions of the ASME, 2016年第138卷在线发表。论文通过将响应面方面与人工神经网络结合起来，解决了小样本下的翅片管蒸发器的性能建模问题。

论文链接：http://heattransfer.asmedigitalcollection.asme.org/article.aspx?articleid=2480261

论文摘要：

A new response surface methodology (RSM) based neural network (NN) modeling method is proposed for ?nned-tube evaporator performance evaluation under dry and wet conditions. Two RSM designs, Box–Behnken design (BBD) and central composite design (CCD), are applied to collect a small but well-designed dataset for NN training, respectively. Compared with additional 7000 sets of test data, for all the evaporator performance including total cooling capacity, sensible heat ratio and pressure drops on both refrigerant and air sides, the standard deviation (SD) and coef?cient of determination of trained NNs are less than 2% and higher than 0.998, respectively, under dry conditions while those are less than 4% and greater than 0.974, respectively, under wet conditions. Classic quadratic polynomial response surface models were also included for reference. By comparison, the proposed model achieves higher accuracy. Finally, parametric study based on the trained NNs is conducted. This new method can remarkably downsize the training dataset and mitigate the over-?tting risk of NN. [DOI: 10.1115/1.4032358]