Data-driven Visualization of Bubble Contact-line Dynamics on Heterogeneous Surfaces

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K04312
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 19020:Thermal engineering-related
• Research Institution: University of Tsukuba
• Principal Investigator: Shen Biao 筑波大学, システム情報系, 助教 (80730811)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: Nucleate boiling / Subatmospheric / Wettability / Surface cavity / Vapor trapping / Artificial intelligence / Infrared thermography

Outline of Final Research Achievements

The present study focused on enhancement of boiling heat transfer under low pressures. The role of complex contact-line dynamics during bubble release from the boiling surface was elucidated, which led to development of hybrid surface combining wettability and structural engineering. The hydrophobic-coated cavities introduced stronger contact-line pinning, which was responsible for more vapor trapping compared with flat surface. Consequently, the enhanced surface led to significantly delayed transition to deteriorating intermittent boiling at very low pressures.
Additionally, a Convolutional Neural Network-based model was built to achieve fast processing visualization data for analysis of boiling characteristics. Trained on time series of high-speed infrared thermographic images of the boiling surface, the model was able to identify the existence of microlayer at an accuracy of 90%.

Free Research Field

Thermal engineering

Academic Significance and Societal Importance of the Research Achievements

本研究は、効率的な沸騰伝熱を持続させるための蒸気トラップの重要性について新たな見方を提供した。ハイブリッド表面設計は、気泡離脱時の接触線のピンニングを利用し、減圧下の沸騰を著しく向上させ、コンピュータなどの次世代冷却ソリューションの開発にポジティブな影響を与えることができる。一方、機械学習ベースの解析フレームワークは、可視化データの処理を加速するために使用でき、沸騰現象に対する強力な研究ツールを加える。