整体式翅片管换热器的精度自校正模型  

ACCURACY SELF-ADAPTIVE MODEL FOR FIN-IN-TUBE HEAT EXCHANGERS

作　　者：丁国良[1] 张春路[1] 刘浩[1] 

机构地区：[1]上海交通大学机械与动力工程学院,上海200030

出　　处：《机械工程学报》2003年第6期53-57,共5页Journal of Mechanical Engineering

基　　金：国家重点基础研究发展规划(973)资助项目(G2000026309)

摘　　要：为提高换热器性能的计算精度和速度,建立了整体式翅片管式换热器的精度自校正模型。该模型中带了两个神经网络,一个用于补偿简化模型与分布参数模型的差异,另一个则用于自适应地学习试验结果,提高模型的精度。用该模型计算整体式翅片管冷凝器和蒸发器性能,并与试验结果相对照。对于冷凝器,换热量误差的平均值和最大值分别为0.63％和1.72％,过冷度误差的平均值和最大值则为0.9℃和3.2℃。对于蒸发器,换热量误差的平均值和最大值分别为1.56％和11.0％,过热度误差的平均值和最大值则为1.5℃和9.8℃。对于冷凝器和蒸发器,计算速度较分布参数模型均提高两个数量级。An accuracy self-adaptive model for fin-in-tube heat exchangers is established, in which two artificial neural networks are combined with a simplified traditional mathematical model. One of the neural networks is used to compensate the difference between the distributed-parameter model and the simplified one, the other is used to improve the model accuracy by adaptively learning from experimental data. The model is used for predicting fin-in-tube heat exchangers and compared with experimental results. For condensers, it is shown that the average and maximum deviations of heat flow rate are 0.63% and 1.72% respectively, while the average and maximum deviations of subcooling are 0.9℃ and 3.2℃respectively. For evaporators, the average and maximum deviations of heat flow rate are 1.56% and 11.0% respectively, while the average and maximum deviations of superheat is 1.5℃and 9.8℃respectively. For condensers and evaporators, the computational speed with the new model is about two orders of magnitude faster than that with the distributed-parameter model.

关 键 词：整体式翅片管式换热器 精度自校正模型 人工神经网络 冷凝器 蒸发器 空调器 

分 类 号：TB657.2[一般工业技术—制冷工程] TK172[动力工程及工程热物理—热能工程]