Hydrodynamics and heat transfer in an inclined bubbly flow

Chinak, A. V.; Gorelikova, A. E.; Kashinsky, O. N.; Пахомов, М. А.; Randin, V. V.; Терехов, В. И.

doi:10.1016/j.ijheatmasstransfer.2017.11.022

Cited by 22 publications

(2 citation statements)

References 43 publications

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“…However, the gas-liquid two-phase flow is such a complex system that there are only a few precise analytic models suitable for interpreting the complex dynamics of diverse flow patterns. Hence, plenty of researchers have shifted to experimental methods to investigate the dynamics of gas-liquid two-phase flows in various channels, such as vertical pipes, 6 horizontal pipes, 7 inclined pipes, 8 porous media, 9 fluidized bed, 10 and micro-fluidic chip. 11 To reveal the hidden fluid dynamics from the experimental observations, many data analysis tools, including time-frequency spectrum, 12 recurrence plot, 13 complex network, 14 nonlinear analysis, 15 multi-scale analysis, 16 and wavelet analysis, 17 have been applied, and various two-phase flow characteristics, such as flow pattern complexity, 18 flow pattern irreversibility, 19 flow pattern transition dynamics, 20 fluid multi-scale dynamics, 21 fluid stability, 22 and the flow pattern determinism 23 have been clarified.…”

Section: Introductionmentioning

confidence: 99%

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas–liquid two-phase flow

Wei

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The complex phase interactions of the two-phase flow are a key factor in understanding the flow pattern evolutional mechanisms, yet these complex flow behaviors have not been well understood. In this paper, we employ a series of gas–liquid two-phase flow multivariate fluctuation signals as observations and propose a novel interconnected ordinal pattern network to investigate the spatial coupling behaviors of the gas–liquid two-phase flow patterns. In addition, we use two network indices, which are the global subnetwork mutual information (I) and the global subnetwork clustering coefficient (C), to quantitatively measure the spatial coupling strength of different gas–liquid flow patterns. The gas–liquid two-phase flow pattern evolutionary behaviors are further characterized by calculating the two proposed coupling indices under different flow conditions. The proposed interconnected ordinal pattern network provides a novel tool for a deeper understanding of the evolutional mechanisms of the multi-phase flow system, and it can also be used to investigate the coupling behaviors of other complex systems with multiple observations.

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Section: Introductionmentioning

confidence: 99%

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas–liquid two-phase flow

Wei

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…The applications can be found in the field of pedestrian surveillance [1], vehicle detection in aerial images [2], cell or bacterial colony counting in medical images [3][4][5], oil droplet characterization in petroleum engineering [6] and bubble counting in bubble columns or nuclear reactors. In bubble column or nuclear reactor applications, accurate separation and reconstruction of bubble shape have equal importance to the number counting, since bubble shape contains important geometrical information for the study of mass, momentum and energy transport in these systems [6,7]. The demand for bubble shape acquisition introduces additional challenges for algorithm development and benchmarking in this field.…”

Section: Introductionmentioning

confidence: 99%

BubGAN: Bubble generative adversarial networks for synthesizing realistic bubbly flow images

Liu

2019

Chemical Engineering Science

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Bubble segmentation and size detection algorithms have been developed in recent years for their high efficiency and accuracy in measuring bubbly two-phase flows. In this work, we proposed an architecture called bubble generative adversarial networks (BubGAN) for the generation of realistic synthetic images which could be further used as training or benchmarking data for the development of advanced image processing algorithms. The BubGAN is trained initially on a labeled bubble dataset consisting of ten thousand images. By learning the distribution of these bubbles, the BubGAN can generate more realistic bubbles compared to the conventional models used in the literature. The trained BubGAN is conditioned on bubble feature parameters and has full control of bubble properties in terms of aspect ratio, rotation angle, circularity and edge ratio. A million bubble dataset is pre-generated using the trained BubGAN. One can then assemble realistic bubbly flow images using this dataset and associated image processing tool. These images contain detailed bubble information, therefore do not require additional manual labeling. This is more useful compared with the conventional GAN which generates images without labeling information. The tool could be used to provide benchmarking and training data for existing image processing algorithms and to guide the future development of bubble detecting algorithms.

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Influence of a Single Bubble on Heat Transfer Between Liquid and the Hot Wall Under Complex Shear Action

Zhang

Pang

2021

Microgravity Sci. Technol.

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Hydrodynamics and heat transfer in an inclined bubbly flow

Cited by 22 publications

References 43 publications

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas–liquid two-phase flow

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas–liquid two-phase flow

BubGAN: Bubble generative adversarial networks for synthesizing realistic bubbly flow images

Influence of a Single Bubble on Heat Transfer Between Liquid and the Hot Wall Under Complex Shear Action

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