含油纳米制冷剂沸腾中气相与液相之间球形纳米颗粒的迁移特性

上海交通大学学报（自然版） ›› 2012, Vol. 46 ›› Issue (05): 671-676.

含油纳米制冷剂沸腾中气相与液相之间球形纳米颗粒的迁移特性

丁国良1, 彭浩2, 胡海涛1, 庄大伟1

(1.上海交通大学制冷与低温工程研究所，上海 200240；2.中国科学院力学研究所微重力重点实验室（国家微重力实验室），北京 100190)

收稿日期:2012-06-10 出版日期:2012-05-28 发布日期:2012-05-28

Migration Characteristic of Spherical Nanoparticles from Liquid to Vapor Phase during Refrigerant/Nanolubricant Mixture Boiling

DING Guo-Liang-1, PENG Hao-2, HU Hai-Tao-1, ZHUANG Da-Wei-1

(1. Institute of Refrigeration and Cryogenics Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. Key Laboratory of Microgravity (National Microgravity Laboratory）, Institute of Mechanics of Chinese Academy of Sciences, Beijing 100190, China)

Received:2012-06-10 Online:2012-05-28 Published:2012-05-28

摘要/Abstract

摘要： 为了评估纳米制冷剂的沸腾传热效果以及球形纳米颗粒在制冷系统中的循环能力，采用称重法实验研究了纳米制冷剂沸腾中气/液相间球形纳米颗粒的迁移特性，重点考察球形纳米颗粒种类和粒径、制冷剂种类、润滑油浓度、热流密度和初始液位高度对球形纳米颗粒迁移特性的影响.结果表明：球形纳米颗粒迁移率随球形纳米颗粒密度或粒径的减小而增大；制冷剂的动力学黏度越小、密度越大，其在完全蒸发时的球形纳米颗粒的迁移率越大；球形纳米颗粒的迁移率随润滑油浓度的增大而减小，随热流密度的增大而减小，随初始液位高度的增加而增大.

关键词: 制冷剂, 纳米颗粒, 迁移, 粒径, 热流密度

Abstract: In order to evaluate the heat transfer characteristics of nanorefrigerant and the cycle behavior of nanoparticles in the refrigeration system, the migration of nanoparticles during pool boiling was investigated experimentally. Weigh method is used in the present study. The research focuses on the influence of nanoparticle type and size, refrigerant type, mass fraction of lubricating oil, heat flux and initial liquidlevel height on the migration of nanoparticles during pool boiling. The experimental results show that the migration ratio of nanoparticles during the pool boiling of refrigerantbased nanofluid increases with the decrease of nanoparticle density, nanoparticle size, dynamic viscosity of refrigerant, mass fraction of lubricating oil or heat flux; while increases with the increase of liquidphase density of refrigerant or initial liquidlevel height.

Key words: refrigerant, nanoparticle, migration, particle size, heat flux

中图分类号:

TK 124

丁国良1, 彭浩2, 胡海涛1, 庄大伟1. 含油纳米制冷剂沸腾中气相与液相之间球形纳米颗粒的迁移特性[J]. 上海交通大学学报（自然版）, 2012, 46(05): 671-676.

DING Guo-Liang-1, PENG Hao-2, HU Hai-Tao-1, ZHUANG Da-Wei-1. Migration Characteristic of Spherical Nanoparticles from Liquid to Vapor Phase during Refrigerant/Nanolubricant Mixture Boiling[J]. Journal of Shanghai Jiaotong University, 2012, 46(05): 671-676.

参考文献

［1］Jiang W T, Ding G L, Peng H, et al. Experimental and model research on nanorefrigerant thermal conductivity ［J］. HVAC and R Research, 2009, 15 (3): 651669.

［2］Kedzierski M A. Effect of diamond nanolubricant on R134a pool boiling heat transfer// Proceedings of MNHMT09 2nd ASME Micro/Nanoscale Heat and Mass Transfer International Conference. Shanghai: ASME, 2009:1821.

［3］Kedzierski M A, Gong M. Effect of CuO nanolubricant on R134a pool boiling heat transfer ［J］. International Journal of Refrigeration, 2009, 32 (5): 791799.

［4］Trisaksri V, Wongwises S. Nucleate pool boiling heat transfer of TiO2R141b nanofluids ［J］. International Journal of Heat and Mass Transfer, 2009, 52 (5/6): 15821588.

［5］Peng H, Ding G L, Hu H T, et al. Nucleate pool boiling heat transfer characteristics of refrigerant/oil mixture with diamond nanoparticles ［J］. International Journal of Refrigeration, 2010, 33 (2): 347358.

［6］Peng H, Ding G L, Jiang W T, et al. Heat transfer characteristics of refrigerantbased nanofluid flow boiling inside a horizontal smooth tube ［J］. International Journal of Refrigeration, 2009, 32 (6): 12591270.

［7］Henderson K, Park Y G, Liu L P, et al. Flowboiling heat transfer of R134abased nanofluids in a horizontal tube［J］. International Journal of Heat and Mass Transfer, 2010, 53 (5/6): 944951.

［8］Ding G L, Peng H, Jiang W T, et al. The migration characteristics of nanoparticles in the pool boiling process of nanorefrigerant and nanorefrigerantoil mixture ［J］. International Journal of Refrigeration, 2009, 32 (1): 114123.

［9］Prasher R, Bhattacharya P, Phelan P E. Brownianmotionbased convectiveconductive model for the effective thermal conductivity of nanofluids ［J］. Journal of Heat Transfer, 2006, 128(6): 588595.

［10］Edzwald J K, Malley J P, Yu C. A conceptual model for dissolved air flotation in water treatment ［J］. Water Supply, 1990, 8: 141150.

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