Bubble identification from images with machine learning methods

Hessenkemper, Hendrik; Starke, Sebastian; Atassi, Y.; Ziegenhein, Thomas; Lucas, Dirk

doi:10.1016/j.ijmultiphaseflow.2022.104169

Cited by 26 publications

(17 citation statements)

References 20 publications

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“…Here, the experimental setup and the main single-point statistics are briefly summarized. The reader is referred to [18][19][20][21][22] for more technical details and the applied measurement methods. Tap water was used in the experiment as the base liquid, and 1-pentanol was added as an additional surfactant with varying bulk concentration C ¥ of 0, 333, and 1000 ppm.…”

Section: Main Results From Experiments Based On Single-point Statisticsmentioning

confidence: 99%

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A Note on Modeling the Effects of Surfactants on Bubble‐Induced Turbulence

Liao

Hessenkemper

et al. 2023

Chem Eng & Technol

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Recent experimental results reveal that with rising concentration of surfactants, the generated bubble‐induced turbulence (BIT) increases as well, although the bubbles rise slower. Herein, it is shown that this phenomenon has not be considered so far in the standard Reynolds‐averaged Navier‐Stokes (RANS) modeling for BIT, using the Ansatz with source terms added to the transport equation of turbulent kinetic energy and dissipation, respectively. Some problematic issues related to the common concepts for closure of the liquid‐phase turbulence kinetic energy equation are also discussed. Furthermore, this issue is demonstrated by simulating the experiments using two‐fluid approach.

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Section: Main Results From Experiments Based On Single-point Statisticsmentioning

confidence: 99%

“…Here, the experimental setup and the main single‐point statistics are briefly summarized. The reader is referred to 18–22 for more technical details and the applied measurement methods.…”

Section: Main Results From Experiments Based On Single‐point Statisticsmentioning

confidence: 99%

A Note on Modeling the Effects of Surfactants on Bubble‐Induced Turbulence

Liao

Hessenkemper

et al. 2023

Chem Eng & Technol

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“…We used various algorithms for processing binary masks for automatic pixel search, for which we were giving significant penalties. We trained a model on the crops [17] with different augmentations. But for inference, we used the whole image as an input tensor.…”

Section: Froth Bubbles Semantic Segmentationmentioning

confidence: 99%

Flotation Froth Monitoring Using Unsupervised Multiple Object Tracking Methods

Abrarov¹

2023

J Miner Sci Materials

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The popularity of computer vision algorithms applied to the metals and mining industry has grown drastically in recent years. This article will cover the application of computer vision models, video processing techniques, and methods of tracking many objects without data labeling (so-called unsupervised multiple object tracking) using the flotation froth, by example. In more detail, you will find in this article description of this kind of data domain as well as some words about the flotation process, the segmentation approach for many similar objects and an approach to its simultaneous tracking, an overview of existing tracking methods without data labeling and quality metrics comparison of these methods.

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“…For the segmentation of bubbles in the shadowgraphy images, the detection method StarDist [20] was applied to detect individual bubbles in a bubbly flow with a gas holdup up to 5 % [21]. The details of this approach, the training of the applied models, and its performance evaluation are described in the Supporting Information.…”

Section: Data Analysis Of the Gas Dispersion Properties By Shadowgraphymentioning

confidence: 99%

Hydrodynamics in a Bubble Column – Part 1: Two‐Phase Flow

Sommer,

Draw,

Wang

et al. 2023

Chem Eng & Technol

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Multiphase computational fluid dynamics (CFD) simulation is a useful tool to study the hydrodynamics in a bubble column if appropriate closure models are known. Systematic assessment of different models is an ongoing venture that benefits from improved validation data. The present study accumulates a database on two-phase flow experiments in a bubble column. This is achieved by using a combination of particle image velocimetry and shadowgraphy to measure the liquid velocity field and gas dispersion properties simultaneously. This methodology is applied for different needle diameters and gas flow rates. The experimental data are compared with CFD simulations which show good predictions. A systematic investigation of the three-phase flow in the bubble column will appear as a sequel.

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Bubble identification from images with machine learning methods

Cited by 26 publications

References 20 publications

A Note on Modeling the Effects of Surfactants on Bubble‐Induced Turbulence

A Note on Modeling the Effects of Surfactants on Bubble‐Induced Turbulence

Flotation Froth Monitoring Using Unsupervised Multiple Object Tracking Methods

Hydrodynamics in a Bubble Column – Part 1: Two‐Phase Flow

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