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Description of flow regime transitions with deep‐learning‐based polydisperse image method in a gas–liquid stirred tank.

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Title: Description of flow regime transitions with deep‐learning‐based polydisperse image method in a gas–liquid stirred tank.
Authors: Chen, Shilong1,2 (AUTHOR), Wang, Haoliang1 (AUTHOR) hlwang17@ipe.ac.cn, Wang, Chongqing1,3 (AUTHOR), Yang, Li1 (AUTHOR), Li, Zheng1 (AUTHOR), Cheng, Jingcai1 (AUTHOR) jccheng@ipe.ac.cn, Yang, Chao1,2 (AUTHOR)
Source: AIChE Journal. Feb2026, Vol. 72 Issue 2, p1-13. 13p.
Subjects: Transition flow, Bubble dynamics, Particle detectors, Deep learning, Flow separation, Chemical reactors
Abstract: The characterization of flow regime transitions in gas–liquid stirred tanks is important for process optimization. Conventional studies, however, have primarily characterized steady‐state flow regimes, overlooking the evolution of gas‐phase polydispersity that significantly influences the transition dynamics. This work addresses this gap by introducing a deep‐learning‐based polydisperse particle detection system for efficient size‐resolved bubble characterization. The results reveal that regime transitions are mechanistically controlled by a hydrodynamic decoupling between bubble classes, with large‐bubble dynamics playing a key role. The flooding‐to‐loading transition is characterized by the radial dispersion of large bubbles, marked by a sharp surge in their local occurrence frequency, with its growth rate increasing more than fivefold. The transition to full recirculation coincides with axial entrainment of large bubbles below the impeller, causing a more than threefold increase in local gas holdup. This study establishes a mechanistically grounded framework linking microscopic bubble phenomena to macroscopic flow pattern transitions. [ABSTRACT FROM AUTHOR]
Copyright of AIChE Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 190911153
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Description of flow regime transitions with deep‐learning‐based polydisperse image method in a gas–liquid stirred tank.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Shilong%22">Chen, Shilong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Haoliang%22">Wang, Haoliang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hlwang17@ipe.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Chongqing%22">Wang, Chongqing</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Li%22">Yang, Li</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zheng%22">Li, Zheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cheng%2C+Jingcai%22">Cheng, Jingcai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jccheng@ipe.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Chao%22">Yang, Chao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22AIChE+Journal%22">AIChE Journal</searchLink>. Feb2026, Vol. 72 Issue 2, p1-13. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Transition+flow%22">Transition flow</searchLink><br /><searchLink fieldCode="DE" term="%22Bubble+dynamics%22">Bubble dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+detectors%22">Particle detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+separation%22">Flow separation</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+reactors%22">Chemical reactors</searchLink>
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  Data: The characterization of flow regime transitions in gas–liquid stirred tanks is important for process optimization. Conventional studies, however, have primarily characterized steady‐state flow regimes, overlooking the evolution of gas‐phase polydispersity that significantly influences the transition dynamics. This work addresses this gap by introducing a deep‐learning‐based polydisperse particle detection system for efficient size‐resolved bubble characterization. The results reveal that regime transitions are mechanistically controlled by a hydrodynamic decoupling between bubble classes, with large‐bubble dynamics playing a key role. The flooding‐to‐loading transition is characterized by the radial dispersion of large bubbles, marked by a sharp surge in their local occurrence frequency, with its growth rate increasing more than fivefold. The transition to full recirculation coincides with axial entrainment of large bubbles below the impeller, causing a more than threefold increase in local gas holdup. This study establishes a mechanistically grounded framework linking microscopic bubble phenomena to macroscopic flow pattern transitions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of AIChE Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1002/aic.70117
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        Text: English
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        PageCount: 13
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    Subjects:
      – SubjectFull: Transition flow
        Type: general
      – SubjectFull: Bubble dynamics
        Type: general
      – SubjectFull: Particle detectors
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Flow separation
        Type: general
      – SubjectFull: Chemical reactors
        Type: general
    Titles:
      – TitleFull: Description of flow regime transitions with deep‐learning‐based polydisperse image method in a gas–liquid stirred tank.
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          Name:
            NameFull: Chen, Shilong
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          Name:
            NameFull: Wang, Haoliang
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            NameFull: Wang, Chongqing
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            NameFull: Yang, Li
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            NameFull: Li, Zheng
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            NameFull: Cheng, Jingcai
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            NameFull: Yang, Chao
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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