Development and evaluation of data-driven modeling for bubble size in turbulent air-water bubbly flows using artificial multi-layer neural networks

Jung, Hokyo; Yoon, Sukhan; Kim, Young-Jae; Lee, Jun Ho; Park, Hyungmin; Kim, Dongjoo; Kim, Jung-Woo; Kang, Seongwon

doi:10.1016/j.ces.2019.115357

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…Jung et al performed an efficient model that can accurately predict the size of cavitation bubbles on test data and in real systems. 110 The approach was to create a pipeline with a multilayer perceptron (MLP) model capable of simulating the size of cavitation bubbles for various parameters (Fig. 10b).…”

Section: Machine Learning For Bubblesmentioning

confidence: 99%

Machine learning for soft and liquid molecular materials

et al. 2023

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Section: Machine Learning For Bubblesmentioning

confidence: 99%

Machine learning for soft and liquid molecular materials

et al. 2023

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“…57 Most of the studies on bubble characterization were focused on gas−liquid flows using the machine learning method. For example, Jung et al 58 applied ANN to successfully predict mean bubble diameter in turbulent air−water bubbly flows. Theßeling et al 59 adopted the least absolute shrinkage and selection operator regression algorithm as well as the regression treebased algorithm to estimate the diameter of a single bubble in a bubble column.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted Characterization of Local Bubble Properties and Its Coupling with the EMMS Bubbling Drag

Jin,

Hu,

Liu

et al. 2024

Ind. Eng. Chem. Res.

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Empirical correlations for bubble diameter and velocity are incapable of predicting the local bubble behaviors fairly because the impact of local hydrodynamics on bubbles in fluidized beds. Based on image processing, a novel bubble identification method with an adaptive threshold was proposed to distinguish and characterize bubbles in fluidized beds. The information regarding bubble properties and local hydrodynamics can thus be extracted using the big data from highly resolved simulations. Accordingly, the deep neural network was trained to accurately predict local bubble properties, where the inputs were determined by performing correlation analysis and a random forest algorithm. We found Reynolds number, voidage, and relative coordinates are the dominant factors, and a four-variable choice was demonstrated to output satisfactory performance for predicting local bubble diameter and velocity. The model was preliminarily validated by coupling with the EMMS drag into CFD codes, which showed that the accuracy of coarse-grid simulations can be significantly improved.

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“…The relative error of CHF is around 20% which is better than that of empirical correlation or mechanistic models. Jung et al (2020) investigated the bubble size distribution in turbulent airwater bubbly flows by using multi-layer ANNs. Compared to the 20% error of traditional theoretical models, the results of the use of ANNs show average relative error of 4.98% for the given experimental datasets.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Data-Driven Model for Bubble Maximum Diameter in Subcooled Boiling Flow Using Artificial Neural Networks

Dong

Chen

et al. 2022

Front. Energy Res.

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In the subcooled boiling flow under low-pressure conditions, bubble characteristic diameter is of great influence on the surface heat transfer coefficient. However, large errors are still found in calculations using traditional mechanistic models or empirical correlations, especially for wide experimental condition. In this paper, we propose a widely applicable data-driven model using artificial neural networks (ANN) to predict the bubble maximum diameter and investigate the effect of experimental conditions. After a series of analyses on structural parameters and input parameters, the ANN model is established and validated based on six available experimental databases. The result shows that the relative error is around 14%. Uncertainty analysis is carried out for the four experimental conditions and two structural conditions. The results show the measuring accuracy of pressure is one of the most sensitive parameters on the prediction of bubble maximum diameter in the subcooled boiling flow under 1.0 MPa, especially for the bubble sizes larger than 0.5 mm. According to the results of uncertainty analysis, a new correlation is proposed for coefficients C and φ, which are used to express the effect of pressure and fluid dynamic. The new correlation works well for all the experimental databases, and the error for bubble datasets of large size is also modified. Furthermore, another independent validation with a low relative error to 14% is provided to prove the accuracy of the new correlation.

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Development and evaluation of data-driven modeling for bubble size in turbulent air-water bubbly flows using artificial multi-layer neural networks

Cited by 12 publications

References 49 publications

Machine learning for soft and liquid molecular materials

Machine learning for soft and liquid molecular materials

Machine Learning Assisted Characterization of Local Bubble Properties and Its Coupling with the EMMS Bubbling Drag

An Evaluation of the Data-Driven Model for Bubble Maximum Diameter in Subcooled Boiling Flow Using Artificial Neural Networks

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