Machine learning for identifying characteristics of isolated, clustered, and pulsed vapor bubbles on a heated surface under non-stationary boiling conditions

Authors

  • P.V. Khan Novosibirsk State University, Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences https://orcid.org/0000-0001-6912-8481
  • A.A. Levin Novosibirsk State University, Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences https://orcid.org/0000-0002-3268-5302
  • I.I. Chupin Novosibirsk State University, Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences
  • A.S. Safarov Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences https://orcid.org/0000-0002-3604-3649

DOI:

https://doi.org/10.25729/esr.2025.04.0006

Keywords:

nucleate boiling, machine learning, image segmentation, time-averaging, nucleation site density, maximum bubble diameter, nucleation frequency

Abstract

This paper presents an automated system for analyzing high-speed video of non-stationary nucleate boiling on an opaque steel surface. The method leverages the DenoiSeg deep learning network for robust bubble segmentation under challenging conditions (reflected light, optical distortions) and introduces an algorithm for tracking bubbles and calculating time-dependent characteristics.

The system identifies and classifies bubbles into three types (isolated, clustered, and pulsating) to extract the essential boiling parameters, including nucleation site density, surface area fraction, maximum diameter, and nucleation frequency. Validation against manual key frame analysis confirms the system's accuracy. The results not only verify the significant prevalence of clustered and pulsating bubbles but also, thanks to extensive data processing, reveal trends hidden by stochastic noise, such as the growth of the maximum diameter of clusters with increasing surface temperature. The developed tool provides a reliable foundation for building predictive heat transfer models for non-stationary boiling regimes

Author Biographies

P.V. Khan, Novosibirsk State University, Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences

Polina V. Khan is a senior researcher at the Laboratory for Dynamic of Steam-generating systems at Melentiev Energy Systems Institute, Russia. She received her Ph.D. from Yeungnam University, School of Mechanical Engineering, in 2006. Her research interests include nucleate boiling, unsteady heat transfer, and multiphase flow.

A.A. Levin, Novosibirsk State University, Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences

Anatoliy A. Levin is a head of the Laboratory for Dynamic of Steam-generating systems at Melentiev Energy Systems Institute, Russia. He received his Ph.D. from Melentiev Energy Systems Institute in 2008 and D.Sc. in Engineering from Melentiev Energy Systems Institute in 2024. His research interests include nucleate boiling, unsteady heat transfer, and mathematical modeling of thermal plants equipment. He is an elected member of the Scientific Council of the International Centre for Heat and Mass Transfer (ICHMT).

I.I. Chupin, Novosibirsk State University, Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences

Ilya I. Chupin is a researcher assistant at the Laboratory for Dynamic of Steam-generating systems at Melentiev Energy Systems Institute, Russia.

A.S. Safarov, Melentiev Energy Systems Institute of Siberian Branch of Russian Academy of Sciences

Alexey S. Safarov is a junior researcher at the Laboratory for Dynamic of Steam-generating systems at Melentiev Energy Systems Institute, Russia.

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Published

2025-12-29

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Vol. 8 No. 4 (2025): Energy System Research

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