反向传播神经网络对多结构翅片管换热器变工况性能预测适应性研究  

Adaptability of Multi-Structure of Finned Tube Heat Exchanger Under Variable Operation Conditions Based on Back Propagation Neural Network

作　　者：李强林 曾炜杰 田镇[1,2] 谷波 LI Qianglin;ZENG Weijie;TIAN Zhen;GU Bo(Institute of Refrigeration and Cryogenics,Shanghai Jiao Tong University,Shanghai 200240,China;Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海交通大学制冷及低温工程研究所,上海200240 [2]上海海事大学商船学院,上海201306

出　　处：《上海交通大学学报》2020年第7期668-673,共6页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金(51976114);中国博士后科学基金(2019M650084)资助项目。

摘　　要：基于多结构翅片管换热器变工况实验数据,研究神经网络模型在水-空气翅片管换热器性能预测方面的可行性.建立2排管、3排管翅片管换热器在制冷、制热工况下的反向传播神经网络模型,优化并确定单隐含层和双隐含层情况下较优的网络结构,模型预测误差达到1%左右.以指定结构翅片管换热器数据作为测试集,对比单隐含层和双隐含层网络模型在性能预测方面的效果.研究结果表明:对于制冷工况,双隐含层模型不能提高模型精度,反而会因为过拟合导致部分参数的预测精度降低;对于制热工况,双隐含层模型在预测结果精度上有明显的提高.Based on the experimental data of variable structure heat exchangers,the feasibility of neural network model in the performance for the prediction of water-air finned tube heat exchangers is studied.The back propagation(BP)neural network models of 2 rows and 3 rows of finned tubes under refrigeration and heating conditions are established which optimize and determine the optimal network structure under the condition of single hidden layer and double hidden layer.The prediction error of the models is about 1%.The specified structural heat exchanger data is set as a test set,and the performance of the single hidden layer and double hidden layer network model is compared.The research results show that for the refrigeration condition,the double hidden layer model cannot improve the accuracy of the model,but will even reduce the prediction accuracy of some parameters due to over-fitting.For the heating condition,the double hidden layer model has a better accuracy in prediction.

关 键 词：翅片管换热器 反向传播神经网络 变结构 网络结构 

分 类 号：TB69[一般工业技术—制冷工程]