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Bubble behavior parameters extraction and analysis during pool boiling based on deep-learning method

delete2024-11-01
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PRE
AI
Y
Yanwei Zhao
Z
Zhibo Wang
Q
Qi Liu
吴
吴玉欣 (Yuxin Wu) *
J
Junfu Lyu
DOI:10.1016/j.ijmultiphaseflow.2024.104979delete
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Abstract

Abstract

En 中文
The nucleate pool boiling plays an important role in thermal and chemical engineering applications. Analyzing bubble dynamics at nucleation site is crucial to improve the understanding of boiling heat transfer mechanism. Quantitative extraction of bubble parameters from high-speed visualized images is a labor-intensitive and timeconsuming task making it necessary for automatically detect single bubble growth and measure boiling characteristic parameters. In the present work, we proposed a deep learning based self-adaptive statistical algorithm for extraction of bubble behavior parameters quickly and automatically from numerous high-speed visualization images looking from the side view of a boiling chamber. A dataset was constructed for training and performance evaluation based on experimental data of saline solution pool boiling. The StarDist and U-Net convolutional neural network were combined in the algorithm so that more exact segmentation of the bubbles can be identified. Based on the segmentation results, a post-processing program was developed to extract the sequential variation of bubbles during consecutive cycles at nucleation sites. The dynamic characteristic parameters that affect heat transfer, such as nucleation density, bubble departure diameter, departure frequency, and wait time under different heat flux were obtained quantitatively. The comparison of automatic extraction algorithm and manual processing proves the reliability and superiority of our method. This work indicates that the proposed method has great potential to be widely applied as an efficient and universal tool for processing different types of bubble shadowgraph images.
Keywords:
Deep learning
Bubble instance segmentation
Bubble behaviors extraction
Nucleate pool boiling
Continuous growth cycle

Journal

International Journal of Multiphase Flow cover
International Journal of Multiphase Flow
IF:
3.8
Papers:
4.8K
Citations:
1.5W

Organization

B
Brown University
Scholars:
2.4W
Papers: 2.2W
Citations: 3.2W
T
tsinghua university
Scholars:
11.9W
Papers: 10.0W
Citations: 137
Cited Papers

Cited Papers

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Bubble identification from images with machine learning methods
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errOAAI
errHessenkemper, H.; Starke, S.; Atassi, Y.; Ziegenhein, T.; Lucas, D.
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Overlapping bubble detection and tracking method based on convolutional Neural network and Kalman Filter
err2022-12-01
err11
PREAI
errWen, Daizhou; Chen, Wuguang; Yin, Junlian; Song, Yuchen; Ren, Mingjun; Wang, Dezhong
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