2021 Fiscal Year Research-status Report
Data-driven Visualization of Bubble Contact-line Dynamics on Heterogeneous Surfaces
| Project/Area Number |
20K04312
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| Research Institution | University of Tsukuba |
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Principal Investigator |
Shen Biao 筑波大学, システム情報系, 助教 (80730811)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | Boiling heat transfer / Contact line / Surface wettability / Machine learning |
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| Outline of Annual Research Achievements |
During 2021-2022, the present research project has yielded the following results:
1. Development of AI-assisted analysis of boiling visualization data. A convolutional-neural-network-based analytical tool for IR thermographic measurements of boiling was developed, which can automatically track the nucleation, growth, and departure of bubbles.
2. Enhancement of boiling efficiency under low-pressure condition through a hybrid engineering approach. Boiling at low pressures suffers from heat transfer deterioration. A new design of dimpled mixed-wettability surface that improves bubble trapping by strengthening contact-line pinning was proposed. The subatmospheric boiling experiment showed that efficient boiling could be sustained on such a surface at lower pressures than is possible otherwise.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The research targets are twofold: (i) to use Artificial Intelligence (AI) in analyzing boiling visualization data; and (ii) to derive enhanced boiling design for low-pressure boiling.
For (i), a convolutional-neural-network (CNN) architecture was developed, which was trained on the infrared thermographic measurement results of boiling experiments. The code is capable of detecting bubble nucleation, growth and departure from the surface automatically.
For (ii), a hybrid surface design that employs precise control of surface topography and wettability was proposed. The surface was shown to be effective in moderating heat transfer deterioration at low pressures by inducing additional contact-line pining and stronger trapping of vapor.
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| Strategy for Future Research Activity |
The research plan for 2022-2023 includes the following two aspects:
1. The current machine-learning-based analytical tool for boiling visualization data will be improved by adding new features such as identifying the existence of microlayer underneath expanding bubbles and tracking its evolution throughout bubble growth cycles. The improvement can lead to a more nuanced mechanistic understanding of boiling heat transfer.
2. Modifications of the current surface design for low-pressure boiling will be attempted. Instead of open-cavity structure, reentrant cavity-type surface design will be tested against low-pressure conditions. Also, its role in further enhancing subatmospheric boiling when combined with direct manipulation of surface wettability will be explored.
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| Causes of Carryover |
Due to COVID-19, nearly all academic conferences and gatherings have been moved online. The resulting decline of business travel led to a surplus amount of \47, which will be used in the fiscal year 2022 on travel expenses.
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