Cavitation In Centrifugal Pump Research Articles

Cavitation state recognition method of centrifugal pump based on multi-dimensional feature fusion and convolutional gate recurrent unit

The rapid and accurate recognition of cavitation in centrifugal pumps has become essential for improving production efficiency and ensuring machinery longevity. To address the limitations of existing methods in terms of applicability, accuracy, and efficiency, a new method based on multi-dimensional feature fusion and convolutional gate recurrent unit (MCGN) was proposed. Experimental monitoring of cavitation of centrifugal pumps was conducted. Five signals at different water temperatures and operating frequencies were collected. Key modulating features were extracted by time-frequency analysis and principal component analysis. The multi-dimensional features are fused by one and two dimensional convolutional neural networks. The cavitation state label was used to label the sample set by cavitation number, net positive suction head, and cavitation evolution images captured by high-speed cameras. Finally, the neural network based on the convolutional gate recurrent unit was used to classify the cavitation state of the centrifugal pump. The experimental results demonstrate that the proposed method achieves recognition accuracies exceeding 98% for vibration signals, noise signals, outlet pressure pulsation signals, and torque signals. Compared with the short-time Fourier transform-autoencoder model, MCGN model can improve the recognition accuracy by 4.03%, computation efficiency by 20%, and loss by 87%. These advances underscore the potential of the method in monitoring and maintenance practices for centrifugal pumps.

Exploring a new criterion to determine the onset of cavitation in centrifugal pumps from energy-saving standpoint; experimental and numerical investigation

This paper proposes a new criterion to determine the cavitation initiation in centrifugal pumps. Because of the importance of saving energy, and the need for specifying a margin for the required net positive suction head (NPSHr) in all centrifugal pumps, a new criterion based on dropping three percent in efficiency (NPSHr,η) was investigated. Extensive experimental and numerical studies were undertaken to assess the effectiveness of the novel criterion from the energy-saving standpoint and its margin compared to the conventional one (NPSHr,Ht). A test bench was employed to conduct numerous experiments on five single-stage centrifugal pumps, generating comprehensive head-drop and efficiency-drop curves under part-load, designed, and over-load conditions. Additionally, simulations were carried out using CFD, employing an appropriate turbulence model and the ZGB cavitation model. Subsequently, dropping curves of total head, hydraulic efficiency, and the torque-rise curve against available NPSH were extracted. Analysis of these simulations revealed that NPSHr,η is consistently higher than NPSHr,Ht. A three-percent total head-drop diminishes the efficiency by an average of 6.04%, ranging from 4.79% to 8.92% depending on the specific case. Using NPSHr,η provides enough distance from the NPSHr,Ht without any need to specify a margin, and a huge amount of energy can be saved annually.

Cavitation diagnosis method of centrifugal pump based on characteristic frequency and kurtosis

Centrifugal pumps are important equipment in industrial production. At present, vibration signals are often used to diagnose cavitation in centrifugal pumps, but the vibration signals are easy to be disturbed and the fault characteristics are unstable to be detected. In this paper, a single stage centrifugal pump is taken as the study object, and the vibration signals of various parts of the centrifugal pump cavitation state are collected under different flow conditions. The short-time Fourier transform and one-third octave analysis are performed on the filtered signals, and the characteristic frequency of cavitation and the energy near the characteristic frequency with the development of cavitation are obtained. Based on vibration signals, the vibration root mean square (rms) and kurtosis values of different cavitation states are obtained. Flow state, kurtosis, and rms are used as input variables in the double-layer backpropagation neural network model to identify and classify the cavitation states of centrifugal pumps. The results show that the trained neural network model can accurately identify and classify the cavitation state of the centrifugal pump under the conditions of low flow rate, rated flow rate, and large flow rate, and the accuracy is more than 99.5%. This study provides a new technique for diagnosing cavitation in centrifugal pumps.

Application of three cavitation models in centrifugal pump cavitation flow simulation

In order to solve the problems of difficult and inaccurate cavitation simulation in centrifugal pumps, this paper adopts the commercial software CFX and realizes the same-platform comparison of Zwart-Gerber-Belamri model, Kunz model and Schner-Saer model in the simulation of centrifugal pumps’ cavitation by using its own language. The research results show that the Schner-Saer model can predict the external characteristics of the pump more accurately without considering the coefficient correction; By adjusting the empirical coefficients, different cavitation models can achieve the same effect; The Kunz model can predict the pressure distribution in the impeller more accurately, and the error is basically kept within 10% except for the local complex flow area; The change of the pump head is more obviously affected by the volume of the vapour in impeller, but weakly affected by the morphology of the vapour.

Research Progress on Identification and Suppression Methods for Monitoring the Cavitation State of Centrifugal Pumps

Cavitation is a detrimental phenomenon in hydraulic machinery, adversely impacting its performance, inducing vibration and noise, and leading to corrosion damage of overflow components. Centrifugal pump internal cavitation will lead to severe vibration and noise, and not only will the performance of hydraulic machinery be adversely affected but the impact generated by the collapse of the vacuole will also cause damage to the impeller wall structure, seriously affecting the safety of the equipment’s operation. To prevent the generation and development of internal cavitation in centrifugal pumps, to prevent the hydraulic machinery from being in a state of cavitation for a long time, to avoid the failure of the unit, and to realize the predictive maintenance of centrifugal pumps, therefore, it is of great significance to research the methods for monitoring the cavitation of hydraulic machinery and the methods for suppressing the cavitation. This paper comprehensively describes the centrifugal pump cavitation mechanism and associated hazards. It also discusses the current state of centrifugal pump cavitation monitoring methods, including commonly used approaches such as the flow-head method, high-speed photography, pressure pulsation method, acoustic emission method, and vibration method. A comparative analysis of these methods is presented. Additionally, the paper explores signal characterization methods for centrifugal pump cavitation, including time-domain feature extraction, frequency-domain feature extraction, and time–frequency-domain feature extraction. The current research status is elaborated upon. Moreover, the paper presents methods to mitigate cavitation and prevent its occurrence. Finally, it summarizes the ongoing research on identifying and determining the cavitation state in centrifugal pumps and offers insights into future research directions.

Study on Cavitation Bubble Characteristics in Centrifugal Pump Based on Image Recognition

In this paper, a cavitation bubble in the centrifugal pump cavitation phenomenon was observed in the analysis and research to explore the characteristics of a cavitation bubble in a centrifugal pump. Through the construction of a visualization centrifugal pump test platform and the observation of a high-speed camera, an image processing method was used to extract the characteristics of the cavitation bubble in the captured results, and the characteristics of the cavitation bubble in the centrifugal pump were analyzed and studied in a quantitative way. The results show that the shapes of the cavitation bubbles in a centrifugal pump can be simplified approximately into an ellipsoid shape, more than 75% of the bubbles have a length–diameter ratio between 1 and 2, and the distribution is relatively uniform. Different working conditions affect the size of the cavitation bubble but have little effect on the shape. The average size of the cavitation bubble under different working conditions was calculated by data fitting. This method, which combines high-speed imaging technology and image processing technology, is capable of observing the behavioral characteristics of cavitation bubbles in centrifugal pump cavitation flow both in detail and intuitively. The new method is provided to describe quantitatively the shapes and sizes of bubbles. It is of great significance in understanding the movement characteristics and manifestations of bubbles during a centrifugal pump’s operation and in the further study of the micro mechanisms of the negative effects of cavitation on equipment performance.

Unsteady Cavitation Analysis of the Centrifugal Pump Based on Entropy Production and Pressure Fluctuation

A numerical method using combined detached-eddy simulation (DES) and a cavitation model considering the rotation effect is used for unsteady cavitation flow field of the centrifugal pump. A closed-type pump test system was established to obtain the pump performance and pressure pulsation characteristics under different flow rates and cavitation condition, which provide boundary conditions and verification of calculations. Based on the calculation results of the unsteady flow field of the centrifugal pump cavitation, the entropy generation analysis of the flow field and an analysis of the pressure fluctuation characteristics were carried out. Then, we tried to reveal the relationship between cavitation and the deterioration of the centrifugal pump performance and the generation of the unstable operation excitation force. The internal energy loss is mainly concentrated in the impeller, volute, and pump cavity area, which accounts for more than 85% of the total entropy generation. The characteristic frequency of a Strouhal number of about 0.333 appears at the volute tongue due to the cavitation flow spread downstream.

Experimental study of cavitation noise characteristics in a centrifugal pump based on power spectral density and wavelet transform

Cavitation is a harmful flow pattern in hydraulic machinery that disrupts the normal energy exchange and damages the flow channel components. Therefore, studying cavitation in hydraulic machinery and exploring efficient monitoring methods are currently key research topics. This paper examines the characteristics of inlet and outlet noise from centrifugal pumps at different stages of cavitation development under multiple flow rates. The results indicate that: The inlet and outlet noise of a centrifugal pump mainly consists of discrete noise and broadband noise. Discrete noise is shaft passage frequency, blade passage frequency, and their lower harmonic frequencies. The energy is mainly concentrated in discrete noise. Cavitation has a significant impact on the noise at the inlet, where the power spectral density of discrete noise and broadband noise both sharply decrease as cavitation develops, while the noise at the outlet is largely stable. Through relative energy analysis, it has been found that the relative energy of the different layers of noise at the inlet and outlet changes a lot as cavitation happens. The obvious wavelet energy fluctuation layer of inlet noise is relatively wide, while the obvious wavelet energy fluctuation layer of outlet noise is relatively concentrated, especially in the frequency-containing layer. The wavelet time-frequency diagram indicates that the cavitation noise of centrifugal pumps exhibits temporal stability and is devoid of stochastic interference. It can be observed that the sudden change in sound field during centrifugal pump cavitation is a significant characteristic, and earlier detection of cavitation occurrence can be achieved by analyzing the power spectral density of noise at both inlet and outlet of the pump as well as monitoring relative energy changes in wavelet decomposition layers.

Early Detection of Cavitation in Centrifugal Pumps Using Low-Cost Vibration and Sound Sensors

The scope of this study is the evaluation of early detection methods for cavitation phenomena in centrifugal irrigation pumps by analyzing the produced vibration and sound signals from a low-cost sensor and data acquisition system and comparing several computational methods. Vibration data was acquired using the embedded accelerometer sensor of a smartphone device. Sound signals were obtained using the embedded microphone of the same commercial smartphone. The analysis was based on comparing the signals in different operating conditions with reference to the best efficiency operating point of the pump. In the case of vibrations, data was acquired for all three directional axes. The signals were processed by computational methods to extract the relative features in the frequency domain and use them to train an artificial neural network to be able to identify the different pump operating conditions while the cavitation phenomenon evolves. Three different classification algorithms were used to examine the most preferable approach for classifying data, namely the Classification Tree, the K-Nearest Neighbor, and the Support Vector Data algorithms. In addition, a convolutional neural network was utilized to examine the success rate of the classification when the datasets were formed as spectrograms instead. A detailed comparison of the classification algorithms and different axes was conducted. Comparing the results of the different methods for vibration and sound datasets, classification accuracy showed that in the case of vibration, the detection of cavitation in real conditions is possible, while it proves more challenging to identify cavitation conditions using sound data obtained with low-cost commercial sensors.

Effects of incident angle of sealing ring clearance on internal flow and cavitation of centrifugal pump

High-energy fluid at the outlet of the impeller might get access to the impeller inlet via the sealing ring clearance of the centrifugal pump with shrouded impeller. In this case, the great velocity and energy differences between the fluid at the inlet and outlet of the impeller might trigger unsteady flows during the collision with large energy loss produced. As a result, the flow status of the impeller inlet can be exacerbated; causing that the centrifugal pump is susceptible to cavitation. A centrifugal pump with a low specific speed was studied in this paper. To begin with, five incident angles, that is the original angle (90°), 75°, 60°, 45°, and 30° were formed between the incident section of the sealing ring clearance and the inlet circumference of the impeller through changing the front cover of the impeller and the outer contour of the centrifugal pump. Then, the accuracy of the calculation results of the prototype pump (90°) was verified experimentally with analyses on the entropy generation, velocity streamline, backflow at the inlet section, internal entropy generation distribution of the centrifugal pump, and distribution of cavitation in specific conditions at the incidence of clearance of the centrifugal pump with different incident angles of sealing ring clearance. As can be seen from the results, changing the incident angle of the sealing ring clearance exerts a certain influence on the external characteristics of the centrifugal pump. To be specific, the sealing ring clearance with a small incident angle can lower the flow loss in the front cavity flow area and the impeller flow area of the centrifugal pump as well as can restrain the backflow prewhirl at the inlet section of the centrifugal pump and the cavitation inside the centrifugal pump.

CFD analysis and prediction of centrifugal pump cavitation performance based on three-dimensional two-phase flow between impeller and worm gear

Cavitation performance is an important performance indicator of centrifugal pumps. The occurrence of cavitation will trigger a complex gas-liquid two-phase flow, which will reduce the efficiency of a low ratio centrifugal pump and cause machine vibration, as well as damage to the impeller, seriously affecting the working performance and preventing its normal operation. In this paper, CFD numerical simulation is used to analyze the cavitation characteristics of a centrifugal pump, obtain its internal flow law, calculate the cavitation margin of the device and the head generated by the pump under each inlet condition, and thus predict the critical cavitation margin of the pump at this flow point.

Study on external performance and internal flow characteristics in a centrifugal pump under different degrees of cavitation

Cavitation as a form of unsteady flow within centrifugal pumps can cause the reduced performance of pumps, disordered internal flow regimes, and flow loss. The present criterion used for determining the occurrence of cavitation is a 3% head drop. However, in most cases, pump cavitation already occurs with less than a 1%–2% head drop due to significant changes in the internal flow status. To examine changing patterns in internal flow characteristics as the degree of cavitation deepens in the early stage of cavitation in centrifugal pumps when the head curve does not show significant fluctuation, this paper focuses on a low specific speed centrifugal pump to analyze distributions of total internal pressure, speed, bubble volume, vortex structure, and entropy generation across different degrees of cavitation and obtain internal flow characteristics and flow loss patterns of pumps, with an aim of providing preferences for anti-cavitation hydraulic design of centrifugal pumps.

Numerical and Simulation Study of Cavitation in Centrifugal Pump at Low Flow Rate

Cavitation is the compressible and unsteady turbulent flow caused by the mass transfer between gas and liquid phase which has significant influence on the operating stability and service life of a centrifugal pump. Cavitation can form in a fluid because of localised decreases in dynamic pressure. The collapse of vapour cavities created extremely high pressures, which usually damage neighbouring surfaces and lead to reduce performance. This study aims to simulate a computational model of the cavitation flow using ANSYS Fluid Flow (Fluent). Next, to determine the cavity volume fraction in a centrifugal pump at low flow rate. In this study, the design point of the centrifugal pump used a rotational speed of pump motor at 1450 rpm and initial inlet pressure at 2500 kPa. The simulation of the cavity volume fraction was conducted with four different HNPSHa value, which are 2.09, 1.43, 1.32 and 1.21 m at 25°C of water temperature. The result shows that the values of HNPSHa did affect the cavity volume fraction of pump impeller. Its shows that the decreasing in HNPSHa value, the cavity volume fraction is increased. This study came out with the simulation analysis that predicts a cavitation of the centrifugal pump at different HNPSHa values. The outcomes of this study may help the future researcher to understand how cavitation occurs at low flow rate and its relationship between HNPSHa values

Effect of Vibration and Noise Measuring Points Distribution on the Sensitivity of Pump Cavitation Diagnosis

Cavitation is an essential factor in the deterioration of the hydraulic performance of centrifugal pumps. The study of cavitation fault diagnosis can help avoid or reduce the damage it causes. The vibration and noise analysis method can predict the incipient cavitation more accurately. In order to improve the accuracy of cavitation fault diagnosis, this paper studies the distribution of vibration and noise measuring points for centrifugal pump cavitation diagnosis. The research object is a centrifugal pump with an inducer and splitter blades. Vibration acceleration sensors and hydrophones were used to collect structural vibration and liquid-borne noise signals at different positions of the pump unit. Root-mean-square (RMS) and fast Fourier transform (FFT) methods were used to construct spectrums of vibration and noise signals with different NPSH and compare the sensitivity of different measuring points to the inception and development of cavitation. In addition, the SST k-ω turbulence model and Zwart cavitation model were used to study the cavitation volume distribution in the pump under different cavitation stages. By setting monitoring points at the impeller outlet, the frequency domain signal distribution of pressure pulsation was studied. The results show that the vibration acceleration measuring points at the pump inlet flange and pump axial position, and liquid-borne noise measuring point at the inlet position, are more sensitive to the diagnosis of cavitation fault. Motor current is also the basis for judging the inception of cavitation. Moreover, as the cavitation intensifies, the dominant frequency signal of the pressure pulsation in the pump is partially shifted.

Application and Practice of Project-based Teaching in Marine Auxiliary Machinery Course

Project-based teaching mode is an important teaching method in the current college teaching process. It has obvious effect on the cultivation of creative quality of talents. Project-based teaching emphasizes taking students as the main body and learning as the center. Based on a course in marine auxiliary machinery, centrifugal pump cavitation was selected to explore and practice project-based teaching. The application results show that the project-based teaching method can improve the classroom teaching effect to a certain extent.

Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

Detection of cavitation in centrifugal pumps is critical in their condition monitoring. In order to detect cavitation more accurately and confidently, more advanced signal processing techniques are needed. For the classification of a pump conditions based on the outputs of these techniques, advanced machine learning techniques are needed. In this research, an automatic system for cavitation detection is proposed based on machine learning. Bispectral analysis is used for analyzing the vibration signals. The resulting bispectrum images are given to convolutional neural networks (CNNs) as inputs. The CNNs are a pretrained AlexNet and a pretrained GoogleNet, which are used in this application through transfer learning. On the contrary, a laboratory test setup is used for generating controlled cavitation in a centrifugal pump. The suggested algorithm is implemented on the vibration dataset acquired from the laboratory pump test setup. The results show that the cavitation state of the pump can be detected accurately using this system without any need to image processing or feature extraction.

Numerical method to predict vibration characteristics induced by cavitation in centrifugal pumps

Vibration is a common side effect of cavitation in centrifugal pumps, which are also affected by other physical processes. Therefore, the study of cavitation based on raw vibration signals may introduce potential discrepancies under practical operating conditions. With the objective of accurately identifying the cavitation stages in a centrifugal pump, this study employs the wavelet packet transform (WPT) to process the original signal. Subsequently, the different vibration acceleration signals obtained by processing the raw signals using the proposed method under different working conditions are compared. The unequal interval weight grey (UIWG) model is proposed based on the measurements performed to reconstruct the functional relationship between cavitation and vibration, including in cases where the physical parameters of the research object are unavailable. The results reveal that the post-signal energy exhibits monotonic characteristics in certain frequency bands, despite the presence of interference under practical operating conditions. In addition, the UIWG model is verified to be robust as it is capable of self-correcting its monotonicity based on limited discrete data. In conclusion, the UIWG model equipped with WPT is an effective means to predict cavitation-induced vibrations.

Development of Virtual Laboratory for the Study of Centrifugal Pump Cavitation and Performance in a Pipeline Network

The conventional method of conducting laboratory experiments in engineering becomes a serious challenge asa result of the COVID-19 pandemic; the development of a virtual laboratory is considered a suitable substitute to real laboratory. In this work, a virtual laboratory for a family of centrifugal pumps has been developed. Cavitation development within the centrifugal pumps and the pumps performances in pipeline networks were studied. Negative potential head and high fluid temperature increased early cavitation incidence, while low fluid temperature, as well as positive potential head reduced it. The choice of pipe diameter and its roughness factor played significant roles in the pumps’ performance. The study shows that virtual laboratory represents a good training environment that enables precise pipeline and pump flow matching.

Application of Spectral Kurtosis on vibration signals for the detection of cavitation in centrifugal pumps

The detection of cavitation formation in hydraulic turbomachinery has been widely investigated due to its significant impact on their steady and dynamic operation. The aim of this study is the application of Spectral Kurtosis tool in order to effectively detect the impulsive shock waves generated during the implosion of vapour bubbles. The methodology is applied and evaluated on the vibration signals obtained from two different semi-open impellers. The results under initial cavitating conditions show that the high frequency implosions of vapour bubbles interact with the low frequency passing of the rotating blades and compose part of the vibration signal. The application of the band pass filter, with central frequency and bandwidth estimated from the Fast Kurtogram, to the original signal allows to extract this information both in time and in frequency domain, and to correlate the periodic impulsive behaviour with the blade passing frequency of the impeller. The present results support the establishment of a robust detection cavitation criterion in centrifugal pumps and show that Spectral Kurtosis is a useful tool for the prevention of related faults in centrifugal pumps.

Identification of cavitation in centrifugal pump by artificial immune network

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.