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4 K高效大冷量G-M制冷机二级回热器性能研究

Performance analysis of second regenerator for a high efficiency and large cooling power 4K G-M cryocooler
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摘要 为了满足低温超导、核磁共振成像等领域对于液氦温区大冷量G-M制冷机的需求,对4 K双级GM制冷机进行了优化设计。通过数值模拟分析了不同回热器填充方式对4 K回热器损失以及性能的影响,并进行了实验验证。结果表明,二级回热器填充比例铅:钬铜:硫氧化钆为5∶2∶3时,制冷性能最优。从实际应用视角出发,考察了二级回热器不同热端温度下的损失及制冷量的变化情况,输入功6.5 kW,二级回热器热端温度60 K时,制冷量最大,达到2.37 W@4.2 K。同时对比了二级回热器填充钬铜颗粒和硫氧化钆颗粒制冷性能,当制冷温度为7 K及以下时,填充硫氧化钆的制冷量高于填充钬铜,且制冷温度越低,制冷量提升越明显。 In order to meet the high cooling capacity at liquid helium temperature for lowtemperature superconductivity,magnetic resonance imaging and other fields,the optimal design of 4 K two-stage GM cryocooler was carried out.The influence of regenerator filling method on the internal losses and performance was studied by numerical simulation and experimental validation.The result shows that the optimal performance is achieved when the regenerator filling ratio of lead:HoCu2:GOS is 5∶2∶3.From the perspective of actual application,the losses and refrigerating capacity of the second regenerator at different hot-end temperatures are investigated.When the electrical power is 6.5 kW and the temperature of the hot end of the second regenerator is 60 K,the biggest cooling power reaches 2.37 W@4.2 K.And by comparing the cooling performance of the second regenerators filled with HoCu2 particles and GOS particles,the cooling power of the second regenerator filled with GOS is higher when the cold temperature is 7 K or below.It is found that the lower the cold temperature,the more significant the improvement of the cooling power is.
作者 周志坡 李奥 何韩军 陶杰 屈方杰 段罗斌 董文庆 Zhou Zhipo;Li Ao;He Hanjun;Tao Jie;Qu Fangjie;Duan Luobin;Dong Wenqing(CSSC Pride(NanJing)Cryogenic Technology Co.,Ltd,Nanjing 211106,China;Key Laboratory of Ultra-low Temperature Technology and Application in Jiangsu,Nanjing 211106,China)
出处 《低温工程》 北大核心 2025年第2期18-25,共8页 Cryogenics
基金 国家重点研发计划(2023YFF0721300) 国家重点研发计划(2023YFF0713502)。
关键词 G-M 制冷机 液氦温区 大冷量 数值模拟 优化设计 实验验证 G-M cryocooler liquid helium temperature region large cooling capacity numerical simulation optimal design experimental validation
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