基于BP神经网络的不凝性气体对脉动热管传热影响的分析

doi:10.16085/j.issn.1000-6613.2019-1619

摘要/Abstract

摘要：

不凝性气体的存在会影响脉动热管的传热性能。本文以去离子水和聚丙烯酰胺（PAM）溶液为工质，搭建了脉动热管半可视化传热性能实验系统，研究了不凝性气体的存在对脉动热管传热特性和运行特性的影响；通过建立BP神经网络模型，在不同不凝性气体压力条件下，对脉动热管传热热阻做出了回归预测，并进行了验证。结果表明：不凝性气体的存在会恶化脉动热管的传热性能。随着加热功率的增加，在不同不凝性气体分压力条件下的热阻呈现下降趋势。而随着不凝性气体分压力的增大，工质的蒸发段平均温度明显增大。通过BP神经网络可以对不同不凝气体分压力下脉动热管热阻作出可靠的回归预测，可为脉动热管运行状态的优劣提供判定思路。

关键词: 脉动热管, 不凝气体, 神经网络, 蒸发温度, 回归预测

Abstract:

The existence of non-condensable gas will affect the heat transfer performance of pulsating heat pipe. In this paper, the thermal resistance and evaporation process of pulsating heat pipe in stable operation was analyzed with deionized water and polyacrylamide solution as working fluid. The influence of the presence of non-condensable gas on the heat transfer performance and operation characteristics of pulsating heat pipe was studied. By building a BP neural network model, the regression prediction of thermal resistance characteristics of pulsating heat pipe under different pressure conditions was made and verified. The experimental results showed that the existence of non-condensable gas will deteriorate the heat transfer characteristics of pulsating heat pipe. With the increase of heating power, the thermal resistance under different partial pressures of non-condensable gas showed a downward trend. With the increase of partial pressure of non-condensable gas, the average temperature of evaporation section of working fluid increases obviously. BP neural network can be used to make reliable regression prediction for the thermal resistance of pulsating heat pipe, and provide a judgment idea for the operation status of pulsating heat pipe.

Key words: pulsating heat pipe, non-condensable gas, BP neural networks, evaporating temperature, regression

中图分类号:

TK172.4

陈静妍, 徐荣吉, 吴青平, 王瑞祥, 许淑惠. 基于BP神经网络的不凝性气体对脉动热管传热影响的分析[J]. 化工进展, 2020, 39(7): 2574-2582.

Jingyan CHEN, Rongji XU, Qingping WU, Ruixiang WANG, Shuhui XU. Heat transfer of non-condensable gas to pulsating heat pipe based on BP neural network model[J]. Chemical Industry and Engineering Progress, 2020, 39(7): 2574-2582.

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