Bubble Detection in Multiphase Flows Through Computer Vision and Deep Learning for Applied Modeling

Irina Nizovtseva (), Pavel Mikushin, Ilya Starodumov, Ksenia Makhaeva, Simon Kraev and Dmitrii Chernushkin
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Irina Nizovtseva: Laboratory of Multiphase Physical and Biological Media Modeling, Ural Federal University, Yekaterinburg 620000, Russia
Pavel Mikushin: Laboratory of Multiphase Physical and Biological Media Modeling, Ural Federal University, Yekaterinburg 620000, Russia
Ilya Starodumov: Laboratory of Multiphase Physical and Biological Media Modeling, Ural Federal University, Yekaterinburg 620000, Russia
Ksenia Makhaeva: Laboratory of Multiphase Physical and Biological Media Modeling, Ural Federal University, Yekaterinburg 620000, Russia
Simon Kraev: Faculty of Artificial Intelligence Technologies, ITMO University, Saint Petersburg 197101, Russia
Dmitrii Chernushkin: NPO Biosintez Ltd., Moscow 109390, Russia

Mathematics, 2024, vol. 12, issue 23, 1-23

Abstract: An innovative method for bubble detection and characterization in multiphase flows using advanced computer vision and neural network algorithms is introduced. Building on the research group’s previous findings, this study combines high-speed video capture with advanced deep learning techniques to enhance bubble detection accuracy and dynamic analysis. In order to further develop a robust framework for detecting and analyzing bubble properties in multiphase flows, enabling accurate estimation of essential mass transfer parameters, a YOLOv9-based neural network was implemented for bubble segmentation and trajectory analysis, achieving high accuracy. Key contributions include the development of an averaged mass transfer model integrating experimental data, neural network outputs, and scaling algorithms, as well as validation of the proposed methodology through experimental studies, including high-speed video imaging and comparisons with mass transfer coefficients obtained via the sulfite method. By precisely characterizing critical parameters, the algorithm enables accurate gas transfer rate calculations, ensuring optimal conditions in various industrial applications. The neural network-based algorithm serves as a non-invasive platform for detailed characterization of bubble media, demonstrating high accuracy in experimental validation and significantly outperforming traditional techniques. This approach provides a robust tool for real-time monitoring and modeling of bubble flows, laying the foundation for novel, non-invasive methods to measure multiphase media properties.

Keywords: mathematical modeling; bubble flow; two-phase medium; object detection; multiphase systems; fluid dynamics; deep learning algorithms; computer vision; biotechnological applications (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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