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基于人工神经网络的滴膜共存冷凝传热模型的研究
引用本文:马学虎,李香琴,周兴东,陈嘉宾.基于人工神经网络的滴膜共存冷凝传热模型的研究[J].工程热物理学报,2004(Z1).
作者姓名:马学虎  李香琴  周兴东  陈嘉宾
作者单位:大连理工大学化学工程研究所 大连理工大学化学工程研究所 辽宁 大连 辽宁 大连
基金项目:国家自然科学基金项目资助(NO.59906002),教育部留学回国人员科研启动基金(教外司留[1999]747号)资助项目
摘    要:为了研究影响滴膜共存冷凝传热特性的因素,如滴膜区间面积比、滴膜相对位置、表面分割方式,表面过冷度等对冷凝传热的特性共同作用,本文应用人工神经网络技术,建立表面分割数、滴膜区面积比、凝液环数、表面过冷度与强化传热比之间的综合评价预测模型。结果表明,基于Matlab语言的人工神经网络模型具有较高的预测准确率及泛化能力,能够很好的评价和预测不同条件下的冷凝传热特性。

关 键 词:滴膜共存冷凝  传热速率  冷凝传热特性  人工神经网络

INVESTIGATION OF DROPWISE AND FILMWISE COEXISTING CONDENSATION HEAT TRANSFER PREDICTION MODEL USING ARTIFICIAL NEURAL NETWORKS
MA Xue-Hu LI Xiang-Qin ZHOU Xing-Dong CHEN Jia-Bin.INVESTIGATION OF DROPWISE AND FILMWISE COEXISTING CONDENSATION HEAT TRANSFER PREDICTION MODEL USING ARTIFICIAL NEURAL NETWORKS[J].Journal of Engineering Thermophysics,2004(Z1).
Authors:MA Xue-Hu LI Xiang-Qin ZHOU Xing-Dong CHEN Jia-Bin
Abstract:The purpose of this paper is to study the effect on heat transfer characteristics in dropwise and filmwise coexisting Condensation, such as the area ratio, different relative positions and the surface division patterns of the dropwise-filmwise region, the surface subcooling and the interaction of these factors. The prediction modei corresponding to the relative factors of heat transfer enhancement in-cluding surface division numbers, area ratio of dropwise and filmwise regions, condensate ring numbers, and the surface subcooling degree was developed using an artificial neural network method. Using the artificial neural network prediction modei based on Matlab language, the result indicates that the prediction modei has high accuracy and general ability. Condensation heat transfer characteristics can be well predicted and evaluated under different conditions by this approach.
Keywords:dropwise and filmwise coexisting condensation  heat transfer rate  condensation char-acteristics  artificial neural networks
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