| The measurement of gas-liquid two-phase flow is one of the difficult issues in hydromechanics research field. The dynamic motion and mechanism of bubble rising in water is a basic subject for gas-liquid two-phase flow. The research of bubble parameter and behavior is significant for experimental system design and chemical reaction control and also helpful for the further understand of the gas-liquid two-phase flow mechanism. In the present work, the dynamic characteristics of bubble rising in water were studied based on digital image processing technique. The main conclusions can be draw as follows:(1) In order to get the image sequences with 2D plane and 3D space of bubbles generated at different situation, a suit of visible gas-liquid two-phase flow filed with video systems is established. Digital image processing methods of removing noises, binaryzation, filling bubbles and detecting edges were studied. Especially, the bubble filled method was improved to fill the bubbles with edge opening. And several bubble parameters can be extracted from the images. The scale coefficient, which is the ratio of the real distance and pixel distance, was acquired by images calibration method. Then, several bubble characters were studied based on the digital image processing method.(2)The formed volume of bubble were calculated by digital image processing. An improved model for the formed volume of bubble, with high accuracy and wide adaptive flow rate, was proposed based on the experimental data analysis. For the steady rising bubble, a volume computing formula was derived based on the force balance equation. The parameters (such as rising velocity and acceleration) used in the formula were measured from the bubble image by digital image processing method. And a 3D ellipsoid model was established based on Hough transition, which can be used to calculate the volume of bubbles with weak interaction.(3) In order to get the tracks of multi-bubble rising in the tank, an improved method based on the combination of labeling method, cross correlation method and wavelet multi-scale transform is proposed. This method not only saves the computing time, but also ensures the tracking accuracy. The process of bubble rising begin from linear to zigzag, and then to helical was investigated. And, the correlation between the detachment frequency and the bubble track was analyzed. The rising velocity of the formed bubble was divided into four regions based on the oscillating characteristics: bubble growing region, velocity increasing region, velocity decreasing region and the steady rising region. The first three regions have good repeatability when the bubbles generated at the same working situation. By discussing the impacts of the bubble size, aspect ratio and detachment frequency on the terminal velocity, we found that the terminal velocity is close related to the generating conditions and the distorting extent.(4) According to the periodicity of bubble formation and the appearance of bubble interaction at different gas flow rate, the bubble formed phenomena was classified into 5 regimes: single bubbling, bubbling in groups without coalescence, bubbling with coalescence and break up, chaining and jetting. And the main characteristics of each regime were detailed based on the image sequences. Several textural features based on GLCM and GLGCM were extracted and the best feature combination approach was selected by feature selection method. Then, bubble formed regimes were identified based on SVM multi-classifier. The Experimental results have shown that the feature combination selected by the StARMiner algorithm is most efficient for bubble formed regime recognition, the recognizing rate of which can reach up to 96%.
|
PDF Full Text Request