Dropwise condensation of low surface tension fluids on lubricant-infused surfaces: droplet size distribution and heat transfer

• Main Authors: Ho, Jin Yao, Rabbi, Kazi Fazle, Sett, Soumyadip, Wong, Teck Neng, Miljkovic, Nenad
• 其他作者: School of Mechanical and Aerospace Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Mechanical engineering; Condensation; Dropwise
• 在線閱讀: https://hdl.handle.net/10356/159478
• 機構: Nanyang Technological University

The use of lubricant-infused surfaces and slippery liquid-infused porous surfaces to promote dropwise condensation of low surface tension fluids has received increasing attention in recent years due to the high condensate droplet mobility and low droplet contact angle hysteresis enabled by these coatings. Though multiple studies have focused on developing models to predict heat transfer during water vapor condensation on hydrophobic lubricant-infused surfaces, a lack of understanding exists for condensation of low surface tension fluids, where the surface becomes wettable to the condensate. In this study, we develop a theoretical model to predict the heat transfer during dropwise condensation of low surface tension fluids (ethanol and hexane) on lubricant-infused surfaces. Using numerical analysis, we develop a relationship between droplet Nusselt number and Biot number to accurately predict individual droplet heat transfer. The droplet size distribution of ethanol and hexane were experimentally determined, with result showing that the size distribution density depends on both the working fluid properties and droplet contact angle behavior. To validate our model, experiments were conducted by condensing pure ethanol and hexane vapor on different diameter tubes coated with lubricant-infused surfaces. From the experimental measurements, two correlations which characterized the distribution density of ethanol and hexane were developed. Using the droplet distribution correlations as input parameters, our developed model predicts the heat transfer coefficients for low surface tension fluid condensation more accurately when compared to the existing models, where the maximum deviation between the prediction and experimental results is less than 15%.