Cloud structuring of microbubbles ascending along a vertical wall

Watamura, Tomoaki; Kitagawa, Atsuhide; Murai, Yuichi

doi:10.1016/j.ces.2019.07.050

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…Once the clouds are formed, microbubbles cannot be directly simulated under the assumption of point source. Inside such a cloud, volume fraction and interfacial concentration rise sharply to modify local fluid properties such as density, viscosity and viscoelasticity [15,16,17]. The generation is promoted as the slip velocity of microbubbles to liquid phase is rather small.…”

Section: Introductionmentioning

confidence: 99%

Color-coded visualization of microbubble clouds interacting with eddies in a spatially developing turbulent boundary layer

Park

Saito

Tasaka

et al. 2019

Experimental Thermal and Fluid Science

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Section: Introductionmentioning

confidence: 99%

Color-coded visualization of microbubble clouds interacting with eddies in a spatially developing turbulent boundary layer

Park

Saito

Tasaka

et al. 2019

Experimental Thermal and Fluid Science

Self Cite

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“…Despite this limitation, many previous studies have successfully obtained the bubble velocity using the PIV or PTV(or LIF)-PIV hybrid algorithm for a range of flow conditions such as the flow type, bubble shape, diameter, and local void fraction, as summarized in Table 2 . For dense bubbly flows such as the bubble plume 41 – 43 , stirred vessel 44 , pool boiling 45 , bubbly pipe flow 46 , plunging wave 40 , and ascending microbubbles near the vertical wall 47 , it is found that the bubble size and void fraction considered in the previous studies using the PIV or PTV(or LIF)-PIV hybrid algorithm is less than 10 mm and 30%, respectively. Regarding this, Chung et al 44 mentioned that the significant overlapping of the bubble for the higher void fraction (> 30%) greatly reduces the correlation peak of the PIV and ruptures the spatial continuity, which forces the PIV to produce non-physical vectors.…”

Section: Introductionmentioning

confidence: 97%

Bubble velocimetry using the conventional and CNN-based optical flow algorithms

Choi

Park

2022

Sci Rep

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In the present study, we introduce new bubble velocimetry methods based on the optical flow, which were validated (compared) with the conventional particle tracking velocimetry (PTV) for various gas–liquid two-phase flows. For the optical flow algorithms, the convolutional neural network (CNN)-based models as well as the original schemes like the Lucas-Kanade and Farnebäck methods are considered. In particular, the CNN-based method was re-trained (fine-tuned) using the synthetic bubble images produced by varying the density, diameter, and velocity distribution. While all models accurately measured the unsteady velocities of a single bubble rising with a lateral oscillation, the pre-trained CNN-based method showed the discrepancy in the averaged velocities in both directions for the dilute bubble plume. In terms of the fluctuating velocity components, the fine-tuned CNN-based model produced the closest results to that from PTV, while the conventional optical flow methods under- or over-estimated them owing to the intensity assumption. When the void fraction increases much higher (e.g., over 10%) in the bubble plume, the PTV failed to evaluate the bubble velocities because of the overlapped bubble images and significant bubble deformation, which is clearly overcome by the optical flow bubble velocimetry. This is quite encouraging in experimentally investigating the gas–liquid two-phase flows of a high void fraction. Furthermore, the fine-tuned CNN-based model captures the individual motion of overlapped bubbles most faithfully while saving the computing time, compared to the Farnebäck method.

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