A modified model for bubble growth rate and bubble departure diameter in nucleate pool boiling covering a wide range of pressures. (25th December 2018)

• Record Type: Journal Article
• Authors: Du, Jingyu; Zhao, Chenru; Bo, Hanliang
• Is Part Of: Applied thermal engineering. Volume 145(2018)
• Page Start: 407
• Page End: 415
• Publication Date: 2018-12-25
• Subjects: Bubble growth rate -- Superheated layer -- Bubble departure diameter -- Pool boiling; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2018.09.063
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A modified model is proposed to predict bubble growth rate in a wide range. The force-balance model is improved by present bubble growth rate model. The results of present model perform better than several other existed models. Abstract: Based on the assumption of superheated layer and experimental data from literature, a modified model for bubble growth rate in saturated pool boiling is developed to cover a wide range of boiling conditions at pressures from 0.00204 MPa to 9.57 MPa and Jakob numbers from 0.0904 to 2689. In this paper, the bubble growth rate is non-dimensionalized to follow a power curve and the expression is modified as a function of growth exponent and growth coefficient. According to the analysis of the available experimental data, the growth exponent of bubble appears to change with the system pressure while the growth coefficient vary with the Jakob number. By analyzing the relationships between bubble growth rate and system pressures and Jakob numbers, a modified bubble growth rate model is developed in this paper. The predictions by present model agree better with the experimental data than those by several other models. Based on the developed bubble growth rate model, the force-balance model to predict the bubble departure diameter in nucleate pool boiling is accordingly modified in this paper. The modified bubble departure diameter model predicts more than 90% of the experimental data within ±50%.