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Bubble Recognition Using Neural Networks: Application to the Analysis of a Two-Phase Bubbly Jet
International Journal of Multiphase Flow ( IF 2.829 ) Pub Date : 2019-12-26 , DOI: 10.1016/j.ijmultiphaseflow.2019.103194
Igor Poletaev; Mikhail P. Tokarev; Konstantin S. Pervunin

Gas-liquid two-phase bubbly flows are found in different areas of science and technology such as nuclear energy, chemical industry, or piping systems. Optical diagnostics of two-phase bubbly flows with modern panoramic techniques are capable of capturing simultaneously instantaneous characteristics of both continuous and dispersed phases with a high spatial resolution. In this paper, we introduce a new approach based on neural networks to recognize bubble patterns in images and identify their geometric parameters. The originality of the proposed method consists of the training of a neural network ensemble using synthetic images that resemble real photographs obtained in the experiment. The use of neural networks in combination with automatically data allowed us to detect overlapping, blurred, and non-spherical bubble in a broader range of volume gas fractions. Experiments on a turbulent bubbly jet proved that the proposed method increases the identification accuracy, reduces errors, and lowers the processing time compared to conventional recognition methods. Furthermore, utilizing the new method of bubble recognition, the primary physical parameters of a dispersed phase, such as bubble size distribution and local gas content, were calculated for a near field of the bubbly jet. The obtained results and integral experimental parameters, especially volume gas fraction, are in good agreement with each other.
更新日期:2019-12-27

 

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