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 makes it possible to capture simultaneously instantaneous characteristics of both continuous and dispersed phases with a high spatial resolution. In this paper, we introduce a novel approach based on neural networks to recognize bubble patterns in images and identify their geometric parameters. The originality of the proposed method consists in training of a neural network ensemble using synthetic images that resemble real photographs gathered in experiment. The use of neural networks in combination with automatically generated data allowed us to detect overlapping, blurred, and non-spherical bubbles in a broad range of volume gas fractions. Experiments on a turbulent bubbly jet proved that the implemented method increases the identification accuracy, reducing errors of various kinds, and lowers the processing time compared to conventional recognition methods. Furthermore, utilizing the new method of bubbles recognition, the primary physical parameters of a dispersed phase, such as bubble size distribution and local gas content, were calculated in a near-to-nozzle region of the bubbly jet. The obtained results and integral experimental parameters, especially volume gas fraction, are in good agreement with each other.
|Журнал||International Journal of Multiphase Flow|
|Состояние||Опубликовано - мая 2020|