A correlation for predicting the critical heat flux under an electric field in pool boiling

• Published in: Applied thermal engineering 2024-07, Vol.248, p.123299, Article 123299
• Main Authors: Yu, Ping, Li, Qing, Liu, Bin, Chang, Huaizheng, Tao, Qinghe
• Format: Article
• Language: English
• Subjects: Boiling heat transfer; Critical heat flux; Electric field; Predictive correlation; Visualization
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2024.123299

•CHF is affected by the permittivity and the electric field strength significantly.•Vapor film is invisible near CHF in a high relative dielectric constant fluid.•A correlation was developed to predict pool boiling CHF under an electric field. Enhancing heat dissipation and efficiently monitoring the critical temperature point have become major concerns due to the increasing power consumption of thermal equipment. In this work, the effects of an electric field on the critical heat flux (CHF) of pool boiling in HFE-7100, R123, and ES-500 are studied. The results indicate that the CHF and electric field strength have a roughly linear connection. The CHFs in HFE-7100 on smooth surfaces rise by 31.7 %, 92.7 %, and 196.2 % when the electrode placement height is 7.0 mm, and the electric field strengths are 0.5 × 106 V/m, 1.0 × 106 V/m, and 2.0 × 106 V/m, respectively. For the working fluid with a high relative dielectric permittivity, the CHF is more sensitive to the change in electric field strength, while the electrode placement height (3.0 mm − 7.0 mm) has a slight impact on the CHF. In addition, the visualization results demonstrate that the bubble dynamical behavior near the heating surface at CHF fails to observe an obvious vapor film when the electric field strength reaches a certain value. The existing correlation for pool boiling CHF under an electric field is unable to predict the experimental results. To predict the CHF under an electric field more accurately, a new correlation is developed by reconstructing the existing correlation. The prediction error is within ± 20 %, indicating a good prediction accuracy.