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UNDERSTANDING BUBBLE GROWTH BEHAVIOR DURING NUCLEATE BOILING
- Date Issued:
- 2023
- Abstract/Description:
- 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.
Title: | UNDERSTANDING BUBBLE GROWTH BEHAVIOR DURING NUCLEATE BOILING. |
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Name(s): |
Ghazvini, Mahyar , author Kim, Myeongsub , Thesis advisor Florida Atlantic University, Degree grantor Department of Ocean and Mechanical Engineering College of Engineering and Computer Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Date Created: | 2023 | |
Date Issued: | 2023 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 158 p. | |
Language(s): | English | |
Abstract/Description: | 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. | |
Identifier: | FA00014295 (IID) | |
Degree granted: | Dissertation (PhD)--Florida Atlantic University, 2023. | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): | Includes bibliography. | |
Subject(s): |
Nucleate boiling Ebullition Heat--Transmission |
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Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00014295 | |
Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Host Institution: | FAU |