Document Type
Article
Publication Date
7-19-2024
Abstract
Flow boiling is a highly efficient configuration for meeting the high heat dissipation demands of thermal management systems. However, the complex physics of two-phase flow has hindered its broader application, especially in terms of quantifying visual information. Recent advancements in machine learning vision tools have revolutionized the analysis of phase change phenomena by enabling the digitalization of physically meaningful features such as void fraction, vapor-liquid interfacial behaviors, and liquid-solid wall wetting front areas en masse. In this study, we systematically investigate two-phase models that compute void fractions, heat transfer coefficients, and critical heat flux using live bubble data streams under microgravity. The collected empirical bubble data is used to supplement and validate traditional control-volume-based theoretical modeling approaches. Void fraction data is first validated with analytical frameworks. This is followed by void fractions and wetting front areas being used to improve correlations predicting heat transfer coefficients. This work showcases the potential of using a new machine learning-based strategy to accelerate scientific formula discovery through the extraction of multi-level and physically meaningful features.
Keywords
critical heat flux, flow boiling, heat transfer coefficient, machine learning, void fraction
Language
English
Publication Title
International Journal of Multiphase Flow
Grant
N00014-24-1-2039
Rights
© 2024 The Author(s). This is an open access work distributed under the terms of the Creative Commons Attribution-Non-Commercial (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Huang, C. N., Chang, S., Suh, Y., Mudawar, I., Won, Y., & Kharangate, C. R. (2024). Machine learning boiling prediction: From autonomous vision of flow visualization data to performance parameter theoretical modeling. International Journal of Multiphase Flow, 179, 104928.
Manuscript Version
Final Publisher Version