The strong noise generated during the operation of the centrifugal pump harms the pump group and people. In order to decrease the noise of the centrifugal pump, a specific speed of 117.3 of the centrifugal pump is chosen as a research object. The bionic modification of centrifugal pump blades is carried out to explore the influence of different bionic structures on the noise reduction performance of centrifugal pumps. The internal flow field and internal sound field of bionic blades are studied by numerical calculation and test methods. The test is carried out on a closed pump test platform which includes external characteristics and a flow noise test system. The effects of two different bionic structures on the external characteristics, acoustic amplitude–frequency characteristics and flow field structure of a centrifugal pump, are analyzed. The results show that the pit structure has little influence on the external characteristic parameters, while the sawtooth structure has a relatively great influence. The noise reduction effect of the pit structure is aimed at the wide-band noise, while the sawtooth structure is aimed at the discrete noise of the blade-passing frequency (BPF) and its frequency doubling. The noise reduction ability of the sawtooth structure is not suitable for high-frequency bands.

In order to study the vibration mechanism of a marine centrifugal pump unit and explore the contribution of vibration caused by different vibration excitation sources, a marine centrifugal pump with a specific speed of 66.7 was used for research. A numerical calculation model of the flow field and electromagnetic field of the pump unit was established to analyze the frequency spectrum characteristics and contribution of pump unit vibration caused by different excitation sources. Using the modal superposition method, the vibration characteristics of the pump unit caused by fluid excitation and electromagnetic excitation were analyzed. The results show that the main frequency of pump unit vibration caused by fluid excitation was at the 1× blade passing frequency. The main frequency of pump unit vibration caused by electromagnetic excitation was at the 2× utility frequency. The contribution of different excitation sources to the vibration of marine centrifugal pump unit was in the following order: fluid excitation on the inner surface of the pump $>$ electromagnetic excitation $>$ fluid excitation in the impeller.

This paper presents an experimental methodology that is capable of sensitively detecting the cavitation inception in a centrifugal pump. Firstly, with a centrifugal pump of n_s = 117 as research object, the cavitation performance, the bubble pattern distribution at impeller inlet, and the vibration and noise were synchronously measured at different flow conditions each with several cavitation coefficients. The change laws of total level of vibration and noise signals throughout the cavitation process were emphatically investigated. After comparing the sensitivity and reliability of different detection methods, the method based on overall sound pressure level of liquid‐borne noise is found to present high sensitivity to cavitation. Secondly, by comparing the affected 1/3 octave spectrum by changing flow and cavitation coefficients, the highly sensitive frequency band to cavitation was obtained. Then, a new inception cavitation detection method was proposed based on Pauta principle. Finally, the method was verified through an ultra‐low‐specific speed pump (n_s = 25). The results show that the total pressure level of liquid‐borne noise increases firstly and then decreases with the development of cavitation. The broadband SPL of liquid‐borne noise between 2000 and 3000 Hz can be used to detect the inception cavitation, and the cavitation detection threshold is determined as 1.0%.

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.

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.