View article

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 …