UNDERSTANDING BUBBLE GROWTH BEHAVIOR DURING NUCLEATE BOILING

Boiling heat transfer associated with bubble growth is perhaps one of the most efficient cooling methodologies due to its sizeable latent heat during phase change. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms, a primary source of enhanced heat dissipation. This thesis provides a comprehensive examination of the mechanisms involved in the growth of bubbles during nucleate boiling. By conducting a combination of experiments and numerical analyses, the goal is to enhance our understanding of bubble growth phenomena and their impact on heat transfer. Initially, the experimental work focuses on comparing the heat transfer performance and parameters related to bubble dynamics between regular and modified fin structures. The findings demonstrate that the modified fin structure, which featured artificial nucleation sites, exhibits superior heat transfer characteristics. This improvement is attributed to changes in the bubble departure diameter, bubble departure frequency, and growth time. Subsequently, an artificial neural network is developed to accurately predict the bubble departure diameter based on the wall superheat and subcooling level. This predictive model provides valuable insights into bubble behavior originating from artificial nucleation sites.