Research on the Heat and Mass Transfer Performance of the Copper Foam Condensation Dehumidifier for Space Stations

Zhou Hantao; Zhang Liang; Li Haoyue; Fang Qi

doi:10.12465/j.issn.0253-4339.2025.05.157

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Research on the Heat and Mass Transfer Performance of the Copper Foam Condensation Dehumidifier for Space Stations

更新时间：2025-09-29

- Research on the Heat and Mass Transfer Performance of the Copper Foam Condensation Dehumidifier for Space Stations
- Journal of Refrigeration Vol. 46, Issue 5, Pages: 157-165(2025)
- 作者机构：
  
  上海理工大学制冷技术研究所　上海　200093
- 作者简介：
  
  Zhang Liang, male, associate professor, School of Energy and Power Engineering, University of Shanghai for Science and Technology, 86-13816670326, E-mail: L_zhanghk@163.com. Research fields: air conditioning, heat transfer performance of refrigeration system equipment, cryogenic refrigeration, performance testing of refrigeration devices, industrial heat pumps, and waste heat utilization technology.
- 基金信息：
- DOI：10.12465/j.issn.0253-4339.2025.05.157
  CLC： TB657.5;TK124
- Received：01 July 2024，
  
  Revised：2024-08-24，
  
  Accepted：02 September 2024，
  
  Published：16 October 2025
- 稿件说明：
移动端阅览
Zhou Hantao, Zhang Liang, Li Haoyue, et al. Research on the Heat and Mass Transfer Performance of the Copper Foam Condensation Dehumidifier for Space Stations[J]. Journal of refrigeration, 2025, 46(5): 157-165.
DOI：

Zhou Hantao, Zhang Liang, Li Haoyue, et al. Research on the Heat and Mass Transfer Performance of the Copper Foam Condensation Dehumidifier for Space Stations[J]. Journal of refrigeration, 2025, 46(5): 157-165. DOI： 10.12465/j.issn.0253-4339.2025.05.157.

摘要

为满足空间站高效换热与紧凑轻量化的要求，研发了一种以斯特林制冷机驱动的泡沫铜高效冷凝除湿系统，实验研究了其热质传递特性，实验条件设定为：空气入口温度20~30 ℃、相对湿度50%~80%、冷板温度8~13 ℃，入口风速0.4~1.4 m/s。结果表明：空气入口温度与传热系数、传质系数的增加呈正相关性，当空气入口温度从20 ℃升至30 ℃时，传热系数提高10.5%，传质系数提高57.1%；空气入口相对湿度的变化对传热与传质系数产生了差异化影响，即传热系数随相对湿度的增加而下降，降低了31.6%，反之，传质系数则呈上升趋势，增加了11.4%。值得注意的是，虽然降低冷板温度能够有效提升换热量，但这同时也会导致冷凝水积累从而降低传热传质的效率，因此选取适宜的冷板温度至关重要。换热量与热质传递效率随着入口风速的提升而显著增强，但不断提高风速会导致系统能耗增加，在追求高效换热的同时，也需要权衡系统能耗。基于丰富实验数据，借助回归分析优化了传热模型，理论值与实验值的标准偏差为8.21%，最大偏差为19.76%，表明模型具有较好的预测精度。

Abstract

A high-efficiency condensation dehumidification system utilizing copper foam driven by a Stirling refrigerator was developed to address the demands for high-efficiency heat transfer and a compact lightweight design in space stations. An experimental study was conducted to investigate its heat and mass transfer characteristics under various conditions. The experimental parameters were set as follows: air inlet temperature ranging from 20 ℃ to 30 ℃

relative humidity between 50% and 80%

cold plate temperature from 8 ℃ to 13 ℃

and inlet wind speed from 0.4 m/s to 1.4 m/s. The results indicated a positive correlation between the increase in the air inlet temperature and the enhancement of both the heat and mass transfer coefficients. Specifically

when the air inlet temperature increased from 20 ℃ to 30 ℃

the heat transfer coefficient increased by 10.5%

whereas the mass transfer coefficient exhibited a more substantial increase of 57.1%. Furthermore

variations in the relative humidity of the air inlet distinctly affected the heat and mass transfer coefficients: the heat transfer coefficient decreased by 31.6% with an increase in the relative humidity

whereas the mass transfer coefficient increased by 11.4%. Although reducing the temperature of the cold plate can effectively improve heat transfer

it leads to the accumulation of condensate water and reduces the efficiency of heat and mass transfer. Therefore

an appropriate cold plate temperature must be selected. Additionally

the efficiency of heat and mass transfer was markedly enhanced with increasing inlet wind speed. However

a continuous increase in wind speed resulted in higher system energy consumption. Thus

a balance between efficient heat transfer and high system energy consumption was essential. Based on extensive experimental data

the heat transfer model was refined using regression analysis. The standard deviation between the theoretical and experimental values was 8.21%

and the maximum deviation was 19.76%

demonstrating the strong predictive accuracy of the model.

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references

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