Measurements of Large Bubble Volume Based on 2-D Images Processing Applying Convolutional Neural Network

Hu Ningning; Dang Zhuoran; Zhang Muhao; Tang Ke; Mamoru Ishii

doi:10.13832/j.jnpe.2021.06.0038

Volume 42 Issue 6

Dec. 2021

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Hu Ningning, Dang Zhuoran, Zhang Muhao, Tang Ke, Mamoru Ishii. Measurements of Large Bubble Volume Based on 2-D Images Processing Applying Convolutional Neural Network[J]. Nuclear Power Engineering, 2021, 42(6): 38-43. doi: 10.13832/j.jnpe.2021.06.0038

Citation:

Hu Ningning, Dang Zhuoran, Zhang Muhao, Tang Ke, Mamoru Ishii. Measurements of Large Bubble Volume Based on 2-D Images Processing Applying Convolutional Neural Network[J]. Nuclear Power Engineering, 2021, 42(6): 38-43. doi: 10.13832/j.jnpe.2021.06.0038

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Measurements of Large Bubble Volume Based on 2-D Images Processing Applying Convolutional Neural Network

doi: 10.13832/j.jnpe.2021.06.0038

1.
Nuclear Power Institute of China, Chengdu, 610213, China
2.
College of Nuclear Techonology and Autornation Engineering, Chengdu University of Technology, Chengdu, 610059, China
3.
School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907-2017, USA

Received Date: 2020-10-10
Rev Recd Date: 2020-11-16
Publish Date: 2021-12-09

Abstract

Abstract

Large bubbles (500<Re<2000) in static flow field have the irregular geometrical shape due to the effects of surface tension and inertia force, which brings the enormous error to spherical or ellipsoid equivalent method used in 2D image processing to obtain 3D volume. Besides, due to the refraction and reflection on the irregular surface, the bubble boundary in 2D image is blurred and unrecognizable. The present research applied high-speed video camera to obtain 2D gray scale images of large bubbles in static flow field and used the images as the input of the convolutional neural network (CNN). The bubble 2D projected area and volume obtained in experiments were applied to train the CNN. Finally well-trained CNN was applied to predict the bubble volume. In experiment, actual bubble volume was obtained by small bubble superposition and compared with the predicted value of CNN. The results show that comparing with the traditional image processing method, the proposed method needs no assumption for bubble shape and improves the applicability for large bubble.
- Bubble equivalent volume diameter,
- Convolutional neural network（CNN）,
- Image processing

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References(14)

References

[1]	LUNDE K, PERKINS R J. Shape oscillations of rising bubbles[M]. Dordrecht: Springer, 1998: 387-408.
[2]	CLIFT R, GRACE J R, WEBER M E. Bubbles, drops, and particles[M]. Mineola: Dover Publications, 2005: 340.
[3]	ISHII M. One-dimensional drift-flux model and constitutive equations for relative motion between phases in various two-phase flow regimes: ANL-77-47[R]. Illinois: Argonne National Lab., 1977.
[4]	TRIPATHI M K, SAHU K C, GOVINDARAJAN R. Dynamics of an initially spherical bubble rising in quiescent liquid[J]. Nature Communications, 2015(6): 6268. doi: 10.1038/ncomms7268
[5]	LAU Y M, DEEN N G, KUIPERS J A M. Development of an image measurement technique for size distribution in dense bubbly flows[J]. Chemical Engineering Science, 2013(94): 20-29. doi: 10.1016/j.ces.2013.02.043
[6]	FU Y C, LIU Y. Development of a robust image processing technique for bubbly flow measurement in a narrow rectangular channel[J]. International Journal of Multiphase Flow, 2016(84): 217-228. doi: 10.1016/j.ijmultiphaseflow.2016.04.011
[7]	TOMIYAMA A, CELATA G P, HOSOKAWA S, et al. Terminal velocity of single bubbles in surface tension force dominant regime[J]. International Journal of Multiphase Flow, 2002, 28(9): 1497-1519. doi: 10.1016/S0301-9322(02)00032-0
[8]	MAJUMDER S K, KUNDU G, MUKHERJEE D. Bubble size distribution and gas–liquid interfacial area in a modified downflow bubble column[J]. Chemical Engineering Journal, 2006, 122(1-2): 1-10. doi: 10.1016/j.cej.2006.04.007
[9]	LI Z C, SONG X M, JIANG S Y, et al. The lateral migration of relative large bubble in simple shear flow in water[J]. Experimental Thermal and Fluid Science, 2016(77): 144-158. doi: 10.1016/j.expthermflusci.2016.04.013
[10]	LEWANDOWSKI B, ULBRICHT M, KREKEL G. An automated image analysing routine for estimation of equivalent diameter in high-speed image sequences with high accuracy and its validation[J]. Experimental Thermal and Fluid Science, 2018(98): 158-169. doi: 10.1016/j.expthermflusci.2018.05.016
[11]	王红一. 水中气泡图像处理方法及运动特性研究[D]. 天津: 天津大学, 2011.
[12]	CANNY J. A computational approach to edge detection[M]. Los Altos: Morgan Kaufmann, 1987: 203.
[13]	WONG S C, GATT A, STAMATESCU V, et al. Understanding data augmentation for classification: when to warp?[J]. arXiv, 2016, 45(2): 529-534.
[14]	COUVERT A, ROUSTAN M, CHATELLIER P. Two-phase hydrodynamic study of a rectangular air-lift loop reactor with an internal baffle[J]. Chemical Engineering Science, 1999, 54(21): 5245-5252. doi: 10.1016/S0009-2509(99)00246-8