This work aimed to study the bubble distribution in a multiphase pump. A Euler-Euler inhomogeneous two-phase flow model coupled with a discrete particle population balance model (PBM) was used to simulate the whole flow channel of a three-stage gas-liquid two-phase centrifugal pump. Comparison of the computational fluid dynamic (CFD) simulation results with experimental data shows that the model can accurately predict the performance of the pump under various operating conditions. In addition, the liquid phase velocity distribution, gas-phase distribution, and pressure distribution of the second stage impeller at a 0.5 span of blade height under three typical working conditions were compared. Results show that the region with high local gas volume fraction (LGVF) mainly appears on the suction surface (SS) of the blade. With the increase in inlet gas volume fraction (IGVF), vortices and low velocity recirculation regions are generated at the impeller outlet and SS of the blade, the area with high LGVF increases, and gas–liquid separation occurs at the SS of the blade. The liquid phase flows out of the impeller at high velocity along the pressure surface of the blade, and the limited pressurization of fluid mainly happens at the impeller outlet. The average bubble size at the impeller outlet is the smallest while that at the impeller inlet is the largest. Under low IGVF conditions, bubbles tend to break into smaller ones, and the broken bubbles mainly concentrate at the blade pressure surface (PS) and the impeller outlet. Bubbles tend to coalesce into larger ones under high IGVF conditions. With the increase in IGVF, the bubble aggregation zone diffuses from the blade SS to the PS.

Cavitation erosion could lead to material loss and structural damage in the flow components of hydraulic machinery, affecting the stable operation and efficiency of the equipment. This study established a cavitation erosion distance model for the gap in the cavitation field. High-speed cameras were used to capture the bubble distribution under different cavitation erosion distances. Additionally, the effects of cavitation erosion distance on the damage of stainless steel 06Cr16Ni5Mo, carbon steel 45#, and Q355B were investigated. The study revealed that when the cavitation erosion distance was short, the low number of cavitation bubbles in the gap volume reduced the impact frequency on the material surface. When the cavitation erosion distance was long, the attenuation of ultrasonic vibration propagation prevented gas nuclei at longer distances from developing into bubbles. Some small-volume bubbles further grew into larger-volume bubbles. The cavitation intensity distribution map obtained through image analysis showed that the maximum cavitation intensity occurring at the cavitation erosion distance of 1 mm. The maximum cumulative weight loss of three materials occurred at a cavitation erosion distance of 1 mm. The weight loss from highest to lowest was Q355B, 45#, and 06Cr16Ni5Mo. The cavitation damage of 06Cr16Ni5Mo was characterized by plastic deformation, with noticeable grain-boundary sliding and exfoliation. In contrast, the cavitation damage of 45# and Q355B was characterized by brittle spalling. The cavitation damage progression and failure mechanisms of the three hydraulic turbine materials were elucidated through comprehensive metallographic analysis. The experimental results provide guidance for enhancing the cavitation resistance of hydraulic turbine materials.

The accurate identification of the gas–liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas–liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow rate and the pump rotational speed on the flow characteristics of the centrifugal pump were investigated experimentally. Four typical flow patterns in the impeller of the centrifugal pump, i.e., the bubble flow, the agglomerated bubble flow, the gas pocket flow and the segregated flow, were obtained, and the corresponding flow pattern maps were drawn. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. By selecting the appropriate network super parameters, including the neuron numbers in the hidden layer, the learning rate and the activation function, the different flow patterns in the centrifugal pump impeller were identified. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. In addition, the Kappa coefficient, the Macro-F1 and the Micro-F1 were 0.93, 0.95 and 0.95, respectively, indicating that the model established in this paper can well identify the flow pattern in the impeller of a centrifugal pump.