A novel hybrid model for flow image segmentation and bubble pattern extraction

• Published in: Measurement : journal of the International Measurement Confederation 2022-03, Vol.192, p.110861, Article 110861
• Main Authors: Ju, Yuanyuan, Wu, Liucang, Li, Meng, Xiao, Qingtai, Wang, Hua
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Aspect ratio; Bubbles; Convolutional networks; Diameters; Direct-contact heat transfer; Empirical analysis; Empirical mode decomposition; Flow characteristics; Flow distribution; Flow pattern; Gas–liquid mixing; Image analysis; Image processing systems; Image quality; Image segmentation; Mathematical models; Object recognition; Parameters; Pattern recognition; Quantile regression; Two phase flow; Work environment
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.110861

[Display omitted] •A novel hybrid image analysis model for bubble characterization is proposed.•The hybrid model has better segmentation performance than other classical method.•Correlations between bubble number, gas holdup and time are investigated.•Gas-liquid mixing time series display higher stability and stronger periodicity.•Six parameters related to bubble patterns can be measured accurately. Direct imaging method has been proved to be quite convenient and importance for measuring bubble shape parameters of gas–liquid two-phase flow due to its advantages of non-contact and visualization. The accuracy of flow pattern segmentation and measurement has an importantly impact on flow characteristic. For bubble recognition (pattern segmentation and extraction) with low-quality image, the aim of this paper is to propose a novel hybrid image analysis model (U-net-QR-EMD) combining the U-net algorithm with the quantile regression method (QR) and empirical mode decomposition (EMD). For a direct-contact gas–liquid mixing system, both the synthetic and experimental images of bubble swarm are respectively captured and used to verify the feasibility of the new model. The main findings of our study are as follows. (1) Computing and experimental results show that it is effective in the recognition of bubbles with irregular shape and have a potential in recognizing other bubbles taken under different working conditions. The number of iterations with the proposed model is 90 in the training process, leading to the prediction accuracy with more than 90%. Specifically, it can accurately and reliably identify the individual small and large bubbles in a direct-contact gas–liquid mixing system, but the classical Otsu and K-means methods cannot. (2) Using QR method, it is found that with the increase of gas–liquid mixing time, the number of the identified bubbles decreases but the gas holdup increases. Meanwhile, 90% of the bubble number are between the 5% and 95% quantile regression curves. Both the time series of bubble number and gas holdup tend to be non-stationary and non-linear, but intrinsic mode function (IMF) components and residual obtained by EMD display a higher stability and stronger periodicity. (3) The six parameters (area, perimeter, deformation coefficient, equivalent diameter, aspect ratio and eccentricity) related to bubble patterns can be measured and agree with each other in the evolution process of the mixing state quality with the help of the new mode