Bubble patterns recognition using neural networks: Application to the analysis of a two-phase bubbly jet. (May 2020)

• Record Type: Journal Article
• Authors: Poletaev, Igor; Tokarev, Mikhail P.; Pervunin, Konstantin S.
• Is Part Of: International journal of multiphase flow. Volume 126(2020)
• Publication Date: 2020-05
• Subjects: Computer vision -- Machine learning -- Neural networks -- Bubbles recognition -- Two-phase bubbly jet -- Planar Fluorescence for Bubbles Imaging (PFBI); Multiphase flow -- Periodicals; Écoulement polyphasique -- Périodiques; Multiphase flow; Periodicals; 620.1064
• DOI: 10.1016/j.ijmultiphaseflow.2019.103194
• Languages: English
• ISSNs: 0301-9322

Abstract

Highlights: Ready-to-use neural networks powered software was developed. Software can precisely detect bubbles in images with a wide range of the gas content. Software is automated and can be used for different experiments with bubbly jets. The obtained results are in good agreement with the given experiment parameters. Overall experiment analysis time decreased by ~6–8 times compared to the old approach. Abstract: 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. Graphical abstract: Image, graphical abstract