Gas–liquid two-phase flow directly determines the efficiency and stability of the aeration tank. In this paper, a gas–liquid two-phase testbed is built to explore the aeration performance and internal flow in an aeration tank, including an inverted-umbrella impeller (immersion depth of 0 mm, rotational speed of 250 r/min). Also, the running process is simulated by computational fluid dynamics (CFD) with a population balance model (PBM), and mass transfer coefficient is compared to the experiment. The experimental results show that there is a big difference in bubble diameter, ranging from 0.4 to 1.6 mm. The simulation shows that the impeller intensely draws air above the free surface into the shallow liquid, and the circulation vortex entrains it to the bottom areas faster. Compared with the experiment, the simulated interfacial area and standard oxygen mass transfer coefficient is 12% more and 3% less, respectively. The results reveal that CFD-PBM coupled model can improve the accuracy of calculation, resulting in the simulation of gas–liquid two-phase flow.

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In order to understand the aeration performance of inverted umbrella aerator and bubble characteristics in aeration tank under different conditions, and to reveal the internal relationship between bubble characteristics and aeration performance, an experimental bench of dissolved oxygen concentration and high-speed photography was built. Logarithmic oxygen deficit values were fitted under various conditions. The images captured by high-speed photography were processed, then the bubble characteristics were extracted accurately. It was found that the standard oxygen mass transfer coefficient increased linearly with an increase of rotational speed at a certain immersion depth, and increased firstly then decreased with a decrease of immersion depth when rotational speed was kept constant. The bubble size ranged from 0 mm to 1.59 mm under different working conditions, and the variation of the gas holdup was the same as the standard oxygen mass transfer coefficient when the rotational speed and immersion depth were changing. It was shown that bubbles play a leading role in the process of oxygen mass transfer and have a great influence on oxygen mass transfer rate.

Cavitation detection is particularly essential for operating efficiency and stability of pumps. In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neural network is introduced to detect the cavitation status for centrifugal pumps. The cavitation performance and interior flow-borne noise are measured under three different flow conditions. Then, time-frequency domain analysis is performed on the interior flow-borne noise signal using WPD, and the energy coefficient of each node is calculated to determine the optimal decomposition frequency band. Six-feature parameters are extracted based on frequency-division statistics, including three time-domain features and three wavelet packet features. After that, the PCA is applied for dimensionality reduction. Finally, three cavitation statuses of noncavitation, inception cavitation, and serious cavitation are identified adopting RBF neural network. The results show that the comprehensive identification rate of the proposed method for three cavitation statuses reaches 98.2% with low identification error. The method based on interior flow-borne noise analysis can be well applied for on-line monitoring and diagnosis of pump industry.

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