A two-phase liquid pumping test ring is built to study the flow induced characteristics of centrifugal pump under the air-water flow working condition. Pump performances are measured under different flow rates and different inlet air void fraction (a). Pressure pulsation signal spectrums and their probability density maps are also recorded. The calculations, using URANS k-epsilon turbulence model combined with the Euler-Euler inhomogeneous two-phase model, are also performed to obtain inner flow structure inside the impeller and volute channels under different air-water conditions in order to understand the pump characteristic evolutions. The results show that the performance of centrifugal pump is more sensitive to air inlet injection at low flow rates. The maximum air void fraction of model pump could reach 10% when the pump operates at the highest efficiency point, and the performance drops sharply when the air void fraction is more than 8%. The dominant frequency of pump outlet pressure pulsation is still at the blade passing frequency even under two-phase condition. Frequency amplitude increases with the increase of a. The greater the a, the more of low frequency appears in broadband characteristics. With the increase of a, the probability density amplitude of pressure pulsation decreases gradually, and its span becomes gradually wider as well. Comparisons between numerical local results and experimental unsteady pressure can explain part of the phenomena that are found in the present paper.
© 2019, Turbomachinery Society of Japan. All rights reserved.

In the petroleum sector, electrical submersible pumps (ESP) face numerous challenges when handling multiphase flow of gas and liquid. The primary challenge is from the build-up of the gas-bubbles within the impellers of ESPs, leading from moderate to severe deterioration in pump efficiency. Consequently, a comprehensive investigation is conducted, involving a combination of experimental and numerical analyses, to thoroughly explore the effects of gas entrainment on the performance and intricate internal flow mechanisms of a five-stage mixed-flow ESP. All these analysis was carried out under both design (Qd) and off-design conditions (0.8Qd, and 1.2Qd). For unsteady numerical calculations, two widely used multiphase-models: The volume of fluid (VOF) and The Euler-Euler model are employed and compared to investigate the versatility/validity of these models in predicting multiphase performance of ESP. The comparison between tests, Eulerian, and VOF models demonstrated that the Eulerian model aligns well with the experimental results. and shows higher-adaptability pattern with the test results. This reveals its best capability/versatility in predicting two-phase performance and internal-flow results for ESP. Moreover, it can more accurately capture the phase slippage inside ESP under two-phase flow. Additionally, this work provides a vision to the broad-applicability of Eulerian-model for complicated machinery such as ESP.

＜正＞0引言声聚焦在声通信、无损检测和超声诊断治疗等领域具有广泛的应用,受到研究学者们的广泛重视。近年来,声子晶体和声超材料的快速发展为研究新型聚焦透镜提供了更多的可能。利用负折射机理,可以设计多种不同类型的聚焦透镜[1]。近年来,基于声超表面[2-3]可以设计具有相位调控功能的平面声学器件。通过合理地设计超表面离散相位分布,可以实现很多新奇的声学效应,如基

The internal flow of the mini pumps for washing machines is complex due to its compact structure and it normally adopts the upper drainage method. Due to decline in liquid level at the end of drainage (empty discharge period), the pump experiences water–air multiphase flow condition and produces unsteady flow. This unstable flow not only results in the deterioration of pump energy performance, but also induces larger vibration and noise, which in turn, reduce the working capability of pumps and disturb the people living environment. Therefore, with the aim of exploring the internal flow features and noise characteristics of drainage pumps during empty discharge period, the energy performance test was processed under pure water condition as well as under different air void fraction and part load conditions. Moreover, the internal flow field is calculated based on (both k−Ε and k−ω) turbulence model and Euler non-uniform stream model under pure water, and different inlet air content for part load conditions, respectively. All the experimental conditions for test and boundary conditions for simulation are provided. The overall and local results of internal flow field and gas phase distribution are obtained and analyzed. In addition, the research results can provide a theoretical recommendation for designing the lower noise mini pumps. © 2021, Islamic Azad University (IAU).

南水北调东线工程江都第四抽水站在运行中发生了叶轮外壳气蚀严重、内嵌不锈钢开裂、叶片调节机构受油器发热抱死、叶轮密封老化导致漏油等故障,在进行维修性处理后,有必要对轴流泵可靠性进行分析验证.采用数值模拟技术从流场速度与静

Electrical submersible pumps (ESPs) dealing with gas-liquid multiphase flow face great challenges in the oil and gas industries. The main problem is caused by the accumulation of air bubbles inside the ESP, which degrade the pump performance ranging from minor to major degradation (surging and gas-locking). Several empirical models have been developed to predict head and surging mechanisms. However, the feasibility and versatility of these models are still questionable. Therefore, this study focuses on experimental analysis and their comparison with the existing empirical models (developed to predict Head and surging characteristics under two-phase flow conditions) to examine their validity and versatility in predicting the multiphase performance of pumps. The comparison showed that some models agree well with the given test analysis. However, some models either underestimate or overestimate the predicted values because most of these models were established for high inlet pressure, no water but oil and CO2, several impeller and diffusers, different rotational speed, and different pump geometries. Moreover, this study also compares test results with CFD simulations (using Euler-Euler two-fluid model) and shows that the experimental results are consistent and reliable. Furthermore, this study provides a detailed and close insight into the different aspects of empirical models to check whether they can be applied for wider applications or are limited to the type they were developed.

The introduction of air is often used in engineering to delay the occurrence of cavitation. A steady simulation on a single-stage and single-suction centrifugal pump was used to study the influence of the air on the internal flow under cavitation condition at 1% inlet void fraction. Steady simulation based on SST k-ω turbulence model and Rayleigh-Plesset cavitation model included in Ansys CFX 17.0 were processed to obtain vapor volume fraction, gas volume fraction and the turbulent energy of the impeller under the condition of natural cavitation and cavitation with entrained air. A homogeneous two-phase flow model was adopted to describe the air-water mixed flow. The experimental cavitation performance curve provides boundary condition support for the numerical simulation. In conclusion, it shows that the head of the pump keeps steady after 1% air entrained like natural cavitation. Then it goes worse when the cavitation number decreases but with lower NPSHr which means cavitation performance is improved by 1% air entrained. It indicated that the entrained air changes the distribution of the vapor in the passage of the impeller. However, the descent of the pressure over a certain value will also do harm to the internal flow of the model pump whether it is ventilati ng or not. The internal flow of the pump will be seriously damaged and even be blocked completely due to more and more vapor generated causing a dramatic decline of the head. © 2022, Turbomachinery Society of Japan. All rights reserved.

Hydraulic piston pump is a vital component of hydraulic transmission system and plays a critical role in some modern industrials. On account of the deficiencies of traditional fault diagnosis in preprocessing of original data and feature extraction, the intelligent methods based on deep learning accomplish the automatic learning of fault information by integrating feature extraction and classification. As a popular deep learning model, convolutional neural network (CNN) has been demonstrated to be potent and effective in image classification. In this research, an improved intelligent method based on CNN with adapting learning rate is constructed for fault diagnosis of a hydraulic piston pump. Firstly, three raw signals are converted into two dimensional time-frequency images by continuous wavelet transform, including vibration signal, pressure signal and sound signal. Secondly, an improved deep CNN model is built with an adaptive learning rate strategy for identifying the different fault types. Moreover, t-distributed stochastic neighbor embedding is employed to visualize the distribution of features learned by the main layers of CNN model. Confusion matrix is used to analyze the classification accuracy of each fault type. Compared with the CNN model without adapting learning rate, the improved model achieves a higher accuracy based on the selected three kinds of signals. Experiments indicate that the improved CNN model can effectively and accurately identify various faults for a hydraulic piston pump.