Development Status and Prospects of Centrifugal Pump Cavitation: A Bibliometric Analysis Using CiteSpace

Abstract

This study employs CiteSpace 6.3 R1 software to conduct a quantitative analysis of 645 cavitation-related centrifugal pump publications from the Web of Science Core Collection database (2007–2025) using bibliometric methods. The analysis encompasses publication volume statistics, keyword co-occurrence analysis, and keyword clustering. The results indicate that research on centrifugal pump cavitation is currently in a phase of rapid development. The annual number of publications related to centrifugal pump cavitation shows an overall fluctuating upward trend, with Jiangsu University emerging as the leading research institution. The research hotspots include fault diagnosis, impeller design, numerical simulation, and validation, forming four major developmental pathways. Research on cavitation in centrifugal pumps has gradually shifted its focus from numerical simulation to practical engineering issues such as pressure pulsation and cavitation, with hot topics evolving at an accelerated pace. Future efforts must address challenges like cavitation monitoring and high-precision simulation to comprehensively enhance the anti-cavitation performance and operational reliability of centrifugal pumps.

1. Centrifugal Pump Cavitation and Challenges

As one of the most critical fluid transportation devices in modern industrial systems, the performance and reliability of centrifugal pumps are directly associated with production safety and operational efficiency in fields such as chemical engineering, energy, water conservancy, and aerospace [1,2]. Centrifugal pumps are rotating machines that transfer energy through centrifugal force generated by the rotation of the impeller. Owing to their compact structure, uniform flow characteristics, and ease of operation and maintenance, they have attracted sustained and widespread attention from both the academic community and engineering practice. Cavitation, as a typical and detrimental complex physical phenomenon occurring within centrifugal pumps, has long been a fundamental bottleneck constraining their performance and reliability [3]. This process not only leads to a sharp deterioration in hydraulic performance [4], but also causes severe erosive damage to wetted components, significantly shortening the service life of the equipment. Therefore, in-depth research on cavitation in centrifugal pumps is of crucial importance for improving pump design and ensuring system safety.

1.1. Working Principle and Development of Centrifugal Pumps

Centrifugal pumps convey fluids by means of centrifugal force, with the main components including the impeller, pump casing, suction pipe, and discharge pipe. During operation, the motor drives the impeller to rotate at high speed, causing the fluid within the impeller to be thrown toward the casing periphery under centrifugal force. This creates a low-pressure region at the impeller center, allowing external fluid to enter the impeller through the suction pipe under atmospheric pressure. The expelled fluid then decelerates within the flow passages of the casing and is subsequently delivered to the target location through the discharge pipe, thereby completing the cycle of suction, pressurization, and discharge [5].

The development of centrifugal pumps can be traced back to the 17th century (Figure 1). In 1689, the French physicist Denis Papin invented a device featuring a multi-bladed impeller and a volute-shaped pump casing, which laid the foundation for the later development of centrifugal pumps. In 1754, Swiss mathematician and physicist Leonhard Euler proposed the fundamental equation for impeller-type hydraulic machinery, clarifying the working principle of centrifugal pumps and laying the theoretical foundation for modern centrifugal pumps. In 1818, the United States introduced an early practical centrifugal pump known as the “Massachusetts Pump”. In 1851, British engineer John George Appold further improved the pump structure, driving the widespread industrial application of centrifugal pumps. With advances in materials science during the 20th century, traditional metals were gradually replaced by cast iron, stainless steel, and other improved alloys. At the same time, the integration of variable-frequency technology and automated control systems enabled more precise regulation of flow rates. In the 21st century, centrifugal pumps have benefited from numerical simulations to optimize impeller design, significantly improving energy efficiency. Their development has also shifted toward greater corrosion resistance and lower noise levels, allowing them to better meet the demands of emerging fields such as renewable energy and environmental protection. As centrifugal pumps developed, cavitation caused a sharp decline in head and efficiency, as well as severe noise, vibration, and erosion of the flow components. This has driven global research institutions and manufacturers to heavily invest in the in-depth study of cavitation in centrifugal pumps. Recent research has focused on the combined evaluation of leakage control and rotordynamic stability in centrifugal pump components, with an emphasis on quantifying dynamic coefficients under real operating conditions [6,7].

1.2. Cavitation

Cavitation is a common physical phenomenon in fluid mechanics. When the local pressure drops to the saturated vapor pressure of the liquid, vapor bubbles form inside the liquid (Figure 2). These bubbles collapse rapidly when they move to higher-pressure areas within the fluid flow, generating a tremendous shock force [8,9]. Cavitation is a critical issue that must be strictly prevented during the operation of centrifugal pumps [10,11,12].

Cavitation conditions in centrifugal pumps are typically evaluated using quantitative parameters such as the Net Positive Suction Head (NPSH) and the cavitation number (σ). The available Net Positive Suction Head (NPSHa) is defined as the difference between the total pressure head at the pump inlet and the vapor pressure head of the fluid. Cavitation occurs when the NPSHa falls below the required Net Positive Suction Head (NPSHr). The cavitation number σ is a widely used dimensionless parameter that links the pressure differential to the fluid dynamic pressure and reflects the tendency of the flow to cavitate.

$$NPSHa={\displaystyle \frac{p_{in}}{\rho g}}+{\displaystyle \frac{v_{in}^2}{2g}}-{\displaystyle \frac{p_v}{\rho g}}$$

(1)

$$\sigma ={\displaystyle \frac{p_{ref}-p_v}{\frac{1}{2}\rho u_{ref}^2}}$$

(2)

In the equation, p_in is the absolute static pressure of the liquid at the pump inlet; ρ is the density of the conveyed liquid; v_in is the average flow velocity of the liquid at the pump inlet; and p_v is the saturated vapor pressure of the conveyed liquid at the current temperature.

In centrifugal pumps, areas such as the impeller inlet experience a rapid increase in flow velocity, leading to a significant drop in local pressure. Simultaneously, instabilities and turbulence within the flow field cause fluctuations in cavitation pressure [14]. Cavitation occurs at locations where the pressure falls below the saturated vapor pressure of the liquid at the operating temperature. The impact of cavitation on centrifugal pumps is multifaceted. The microjets and shockwaves generated by bubble collapse continuously impact the metal surfaces, leading to pitting and even perforation of flow components such as the impeller and pump casing. This mechanical erosion significantly shortens the pump’s service life. Cavitation also induces severe vibration and noise [15], affecting the stable operation of the entire pump and potentially causing equipment failure [16]. The presence of a large number of vapor bubbles can also hinder the normal flow of liquid between the impeller passages, leading to deterioration of the pump performance curves [3]. To mitigate the adverse effects caused by cavitation, the international standard ISO 9906:2012 [17] stipulates that, within the specified operating range, centrifugal pumps should not experience cavitation phenomena that could negatively affect their performance or mechanical integrity. This requirement has driven the industry-wide pursuit of highly reliable pump products with low cavitation sensitivity [17].

Therefore, effectively preventing and controlling cavitation is not only a technical prerequisite for ensuring the safe, stable, and efficient operation of centrifugal pumps but also a crucial step in enhancing system reliability and reducing lifecycle and maintenance costs.

1.3. Current Research Gaps and Contributions of This Study

Extensive and in-depth research has been conducted on cavitation phenomena in centrifugal pumps. The existing literature comprehensively covers key aspects such as its occurrence mechanisms, flow field characteristics, performance impacts, and vibration-noise effects. However, through a systematic review of the literature, we have identified three major critical limitations in the current study: 1. Complex Cavitation Mechanism: The entire process from inception to the development and collapse of cavitation involves multiphase flow and phase change, and its unsteady characteristics are still not fully understood. This remains a key obstacle in theoretical modeling and numerical simulation, making accurate prediction and capture highly challenging. 2. Monitoring and Diagnosis Challenges: There is a lack of effective monitoring methods for the initiation and development of cavitation. Existing methods are largely dependent on laboratory environments, making it difficult to achieve early warning and precise localization under complex operating conditions. 3. Multiscale Coupling Effects: The transient impact forces generated by microscopic bubble collapse are coupled with the macroscopic hydraulic performance of the impeller and the rotor dynamics, while the underlying cross-scale interaction mechanisms remain insufficiently understood and require further in-depth investigation.

This study, by applying CiteSpace to analyze keyword co-occurrence frequency, burst strength, and clustering in the literature on centrifugal pump cavitation, systematically reveals the knowledge structure and evolutionary trends in the field. The analysis indicates that research on centrifugal pump cavitation is shifting from fundamental mechanism exploration and performance testing to high-fidelity numerical simulation and intelligent fault diagnosis.

2. Data Sources and Research Methods

2.1. Data Sources

The literature data in this study were retrieved from the Web of Science Core Collection. Articles were selected as the sample source. An advanced search was conducted using the topic field with the search terms “centrifugal pump” and “cavitation”. Only English-language publications were included to ensure the interpretability, reliability, and overall quality of the original data [18]. This study focuses on the development and optimization of centrifugal pump cavitation research from 2007 to 2025. A total of 675 articles were retrieved. After excluding irrelevant entries such as conference notices and calls for papers, the data were imported into CiteSpace 6.3 R1 software for de-duplication, resulting in 645 valid articles.

2.2. Research Methods and Tools

CiteSpace uses co-citation analysis and path exploration network scaling algorithms to perform quantitative analysis of domain-specific literature sets. CiteSpace can identify key evolutionary paths and knowledge turning points and visualize potential driving mechanisms and research frontiers through graphical mapping [17,19]. This study uses CiteSpace 6.3 R1 to conduct a statistical analysis of the literature data on centrifugal pump cavitation. By creating a keyword knowledge map, the research analyzes the current status, trends, and development trajectory of centrifugal pump cavitation, extracting the key research themes and directions. The main analysis parameters in CiteSpace include the following: the time span of the time slice function is from 2007 to 2025, with a single time slice extraction value of 1, and the scale factor k value for the g-index in the selection criteria function is set to 10. A one-year time slice was used to capture short-term fluctuations in research trends while avoiding excessive data smoothing. Longer slices might miss rapid shifts in research focus, whereas shorter slices could lead to excessive fragmentation. Additionally, a g-index with k = 10 was applied to balance the identification of highly cited papers with the mitigation of sporadic citation spikes. This approach ensured stable clustering results, with only minimal variations observed in the identified research themes. The clustering of the keyword knowledge map is visualized using the Log-Likelihood Rate (LLR) [19]. This algorithm has been extensively validated in bibliometric research and delivers the most coherent and interpretable results.

3. CiteSpace Statistics and Analysis

3.1. Literature Spatial Distribution

3.1.1. Analysis of Publication Trends

Literature Spatial Distribution. The change in publication volume is a key indicator for evaluating the research progress in a field. The publication volume in the centrifugal pump cavitation field from 2007 to 2025 was analyzed, revealing an overall fluctuating upward trend, indicating an increase in research attention and interest in the field (Figure 3). This development process can be divided into three stages.

(1) Early Development Stage (2007 to 2012): This stage marks the transition of centrifugal pump cavitation research from steady growth to acceleration. During this period, the primary focus of centrifugal pump cavitation research centered on analyzing cavitation mechanisms, developing and validating numerical simulation methods, and investigating the impact of cavitation on pump performance. The research trend shifted from traditional experimental studies to computational fluid dynamics (CFD) simulations, while also emphasizing interdisciplinary approaches, such as combining vibration analysis, acoustic signal processing, and other methods for cavitation state identification and prediction. Li et al. developed a computational model for predicting the hydrodynamic performance of centrifugal pumps in cavitating flows, observing the flow characteristics associated with off-design flow and dual cavities in the blade passages. Their results indicate a rapid decrease in the head coefficient at low cavitation numbers [20]. Zhu et al. designed and experimented with a low-flow, high-head centrifugal vortex pump for gas–liquid two-phase flow, analyzing the effect of gas content on cavitation performance. Their results show that as the gas volume fraction increases, the pump performance gradually deteriorates [21]. Ding et al. presented a next-generation CFD tool for predicting the cavitation performance of industrial pumps, validating its accuracy by evaluating head and efficiency over a load flow range from 70% to 120% [22]. Wu et al. experimentally studied the cavitation transient characteristics of centrifugal pumps during the startup process. Their results indicate that cavitation is delayed during rapid startup. Under conditions of low suction pressure and high rotational speed, cavitation in the pump becomes inevitable [23]. Ni et al. analyzed vibration signals and found that when cavitation occurs, cavities near the leading edge of the blades exhibit periodic oscillations, enabling the detection of cavitation inception [24].

(2) Rapid Development Stage (2013 to 2018): During this period, research on centrifugal pump cavitation experienced significant growth. The cavitation phenomenon in centrifugal pumps originates from the interaction between microscopic bubble dynamics and macroscopic flow field structures. At the micro-scale, cavitation is governed by the nucleation, growth, and collapse of bubbles, typically occurring over length scales ranging from micrometers to millimeters and time scales on the order of microseconds. The macroscopic flow characteristics of centrifugal pumps are manifested in components such as blade channels, impellers, or guide vanes. Recent studies on labyrinth seals in centrifugal pumps have revealed that even millimeter-scale modifications can significantly alter the rotordynamic response at the system level [25]. Bibliometric analyses indicate that existing research primarily focuses on macroscopic flow behaviors or explores microscopic cavitation mechanisms through simplified bubble models. Concurrently, interest remains high in areas such as pressure pulsation analysis and the study of vibration and noise induced by cavitation [26]. Accordingly, studies on centrifugal pump cavitation focused on in-depth elucidation of cavitation mechanisms, optimization of detection and diagnostic methods, and enhancement of anti-cavitation performance, exhibiting an overall trend toward greater precision, intelligence, and engineering applicability. In terms of mechanisms, numerical simulation methods combined with experimental visualization techniques have been employed to investigate the intrinsic relationships between cavitation and pressure pulsations, vibration, and noise (Figure 4 and Figure 5). Lu et al. investigated the internal flow characteristics and pressure pulsations at the pump inlet and outlet during cavitation development. Their results indicate that cavitation performance can be monitored through pressure pulsations at the pump inlet and outlet, with inlet pressure pulsations being more sensitive to cavitation variations [27]. Li et al. proposed an improved and simplified empirical mode decomposition (EMD) algorithm. Their results show that EMD can effectively suppress noise and extract clear cavitation pulses, while also identifying the number of pulses associated with the cavitation intensity [25]. In terms of detection, early warning of cavitation and vibration noise has been achieved by fusing multi-source signals such as vibration and electrical current [28], thereby establishing an online monitoring and identification framework for cavitation states. Xue et al. proposed an intelligent diagnostic method for centrifugal pump systems based on statistical filtering, support vector machines (SVMs), possibility theory, and Dempster–Shafer theory (DST), enabling early fault diagnosis of centrifugal pumps using vibration signals [29]. McKee et al. proposed a novel vibration-based cavitation detection method based on adaptive octave-band analysis, principal component analysis, and statistical metrics [30]. Lu et al. investigated the internal flow characteristics of the impeller and vibration signals measured at four different locations. Their results indicate that cavitation can be detected by a sudden increase in vibration intensity at the measurement points [31]. At the anti-cavitation design level, measures such as employing diverter blades, optimizing inducer structures, and selecting appropriate working fluids have effectively mitigated vibration and noise caused by cavitation. Guo et al. investigated the influence of short-blade positioning on the anti-cavitation performance of inducers and pumps. Their results indicate that while the short-blade position has a relatively minor effect on the internal flow field of the inducer and the external performance of the pump, it exerts a significant influence on cavitation resistance [32]. Bidhandi et al. examined the inhibitory effects of SiO₂ nanoparticle size and concentration on cavitation inception. Their results demonstrate that SiO₂ nanoparticles can effectively delay the onset of cavitation and reduce the cavitation growth rate [33].

(3) Thriving Development Stage (2019 to 2025): During this stage, the publication volume remained high and stable. In recent years, machine learning has shown significant application potential in centrifugal pump cavitation research, such as using neural networks to rapidly predict cavitation performance [36,37] and employing vibration/acoustic signal-based intelligent recognition and fault prediction of cavitation states [38]. In the field of precise detection and early diagnosis of cavitation states, Han et al. combined vibration and current signals with machine learning to achieve off-design cavitation diagnosis. Their results indicate that combining these signals can improve the reliability of centrifugal pump operational diagnostics [39]. Song et al. developed cavitation state recognition models based on feature-level multi-source information fusion (MSIF) technology utilizing backpropagation neural networks (BPNNs) or support vector machines (SVMs). Research findings indicate that MSIF technology significantly enhances the accuracy of cavitation state recognition. Among these approaches, the combined monitoring scheme integrating current signals with axial pump casing measurements based on BPNN demonstrated optimal performance [40]. On the other hand, research into the mechanisms and unique characteristics of cavitation phenomena under extreme and special operating conditions has also become an important research direction. In complex scenarios such as high-speed, low-temperature, and multiphase flows, the focus of centrifugal pump cavitation research revolves around core areas such as mechanism analysis, model optimization, and upgrades in structure and materials. Extreme temperatures cause dramatic changes in the properties of the medium, leading to differences in cavitation mechanisms under extreme conditions compared to conventional pump operations [34]. Traditional theories and technologies are often not directly applicable.

3.1.2. Analysis of Publishing Institutions

Institutional analysis helps researchers identify leading entities, collaboration patterns, and research priorities within a specific field. By examining inter-institutional cooperation networks, it can reduce the spatial costs of industry collaboration and accelerate the translation of scientific research outcomes from the laboratory to the market. Based on the analysis using Space 6.3 R1, the current institutional collaboration network consists of 194 nodes and 152 links, with active institutions predominantly comprising universities and research organizations (Figure 6). The top three institutions in terms of publication output are Jiangsu University, Tsinghua University, and Zhejiang Sci-Tech University. Jiangsu University not only has the highest number of publications but is also one of the earliest institutions to engage in research in this field. Institutions in North America and Europe, supported by well-established research funding systems and collaborative frameworks, have focused on large-scale interdisciplinary cavitation studies. Their efforts are primarily directed toward developing practical engineering solutions to cavitation-related problems. In contrast, many Asian institutions, supported by advanced computational resources, have made significant progress in cavitation numerical simulations and the development of innovative multiphase flow models.

3.2. Research Hotspot Analysis

3.2.1. Analysis of Keyword Co-Occurrence Network

The keyword co-occurrence knowledge network generated by CiteSpace provides insights into the research hotspots and directions of centrifugal pump cavitation (Figure 7). As shown in the collaboration network, the network density is 0.0141 and consists of 232 nodes and 377 links. Based on co-occurrence frequency statistics from the keyword co-occurrence network, the top 10 most important keywords were identified (

Table 1. Top 10 keywords by count and centrality.

Ordinal	Count	Centrality	Keywords	Count	Centrality	Keywords
1	330	0.49	centrifugal pump	40	0.52	centrifugal pumps
2	136	0.02	flow	330	0.49	centrifugal pump
3	105	0.38	cavitation	105	0.38	cavitation
4	105	0.06	performance	50	0.33	fault diagnosis
5	94	0.23	numerical simulation	56	0.31	impeller
6	61	0.05	model	27	0.28	design
7	58	0.02	simulation cavitation	4	0.25	turbulence model
8	56	0.31	impeller	94	0.23	numerical simulation
9	50	0.33	fault diagnosis	6	0.16	validation
10	44	0.08	turbine	11	0.15	turbulence

). Centrality values indicate keyword importance: higher centrality signifies greater significance, stronger influence on other keywords, and a dominant position within the co-occurrence network. Keywords with both high centrality and frequent co-occurrence represent research hotspots. To enhance the value of keyword co-occurrence analysis, we filtered out generic terms and reclassified the remaining keywords into more specific categories. We excluded general terms such as “flow”, “model”, and “performance” to optimize the keyword co-occurrence analysis, assigning them to specific subcategories reflecting the unique focus of this study. This approach allows us to concentrate on more conceptually meaningful terminology; the top five keywords ranked by centrality are fault diagnosis, impeller design, numerical simulation, and validation.

From a technological application perspective, cavitation readily induces equipment failures, making fault diagnosis a key factor in ensuring the stable operation of centrifugal pumps [40,41,42]. The high centrality of this keyword reflects the strong emphasis placed by researchers on cavitation fault detection and early warning. Timely identification of cavitation through diagnostic techniques can effectively reduce economic losses [43]. The impeller is the core component of a centrifugal pump. Cavitation typically occurs at the leading edge of the blades, where the incoming flow impinges and subsequently separates, causing a pressure drop [44]. This region becomes the location of the lowest pressure, and cavitation often initiates there. Consequently, the design and optimization of the impeller directly affect cavitation suppression performance. The impeller is therefore closely associated with design, as effective prevention and mitigation of cavitation rely on optimized impeller design. Parameters such as the number of blades [45], blade geometry [46], blade installation angle [47], and blade surface roughness [48] all influence pump cavitation behavior.

In terms of research methodology, keywords such as numerical simulation occupy a prominent position. Computational fluid dynamics (CFD), as a key numerical simulation approach, has been widely applied to the analysis of cavitating flow fields in turbomachinery such as centrifugal pumps. A variety of simulation algorithms have been developed to provide robust and accurate mathematical support for modeling [49,50,51,52,53,54], thereby facilitating in-depth investigation of cavitation mechanisms. The keyword validation reflects the rigor of the research, emphasizing the verification of numerical simulation results through experimental methods. The integration of high-speed imaging with synchronized pressure and vibration measurement techniques provides strong validation for numerical simulations, making it possible to observe the transient cavitation process (Figure 8). The study validated numerical simulation results by comparing pressure pulse spectra or steam volume fraction distributions with experimentally measured values. However, model simplifications, uncertainties in cavitation model parameters, and difficulties in capturing transient bubble dynamics often lead to discrepancies in the results.

3.2.2. Analysis of Keyword Clustering Map

Using CiteSpace 6.3 R1, keyword clustering analysis was conducted on 645 publications related to centrifugal pump cavitation, generating a clustering map with 232 nodes, 377 links, and a network density of 0.0141 (Figure 9). The modularity (Q value) of the clustering network is 0.7653, and the silhouette (S value) is 0.9091, indicating a well-defined clustering structure with high reliability. These metrics are used to evaluate clustering validity: the Q value ranges from 0 to 1, where Q > 0.3 indicates a significant modular structure; an S value greater than 0.5 suggests reasonable clustering, while S > 0.7 denotes high-confidence clusters. The results confirm the robustness of the constructed knowledge network.

The keyword clustering knowledge map is used to analyze the clustering characteristics and developmental trajectories of research hotspots. A total of 12 keyword clusters were identified. To extract more structured developmental trends from clustering results, this study integrates semantically related and evolutionarily continuous keywords by analyzing thematic similarity, temporal evolution patterns, and logical relationships within the research domain (Figure 9 and Figure 10), from which four main development paths were summarized: Path 1—structural design and component optimization; Path 2—cavitation state monitoring; Path 3—numerical simulation; and Path 4—multiphase flow.

Path 1 contains the largest number of keyword nodes and represents the key development trajectory, primarily focusing on the structural design and component optimization of centrifugal pumps [56]. This path reflects the evolution of centrifugal pump cavitation research from passive avoidance to active regulation. In the early stage, cavitation resistance was mainly improved by increasing the impeller inlet size and related design measures [45,46,47]. With the advancement of centrifugal pump cavitation research, studies have entered a phase of refined design characterized by the optimization of guide vanes and blade loads. This stage focuses on managing cavitation rather than eliminating it entirely. In recent years, computational fluid dynamics has introduced innovations such as asymmetric blades and flow control structures, enabling active intervention in cavitating flows. However, reasonable structural design requires a comprehensive consideration of the compatibility between key components such as the impeller, volute, shaft system, and seals [57,58]. Achieving the best balance among multiple conflicting objectives and ensuring the process feasibility of new structures remains an unresolved challenge. As research into cavitation in centrifugal pumps continues to advance, various design and optimization approaches are being applied to practical centrifugal pumps. For example, Luo et al. studied the impact of the number of valve petals on the flow state. The results showed that as the number of valve petals increased, the flow section became more circular, and the number of bubbles decreased [59]. Zhao et al. proposed an active method using obstacles attached to the blades to control cavitation in centrifugal pumps. The results showed that obstacles at an appropriate height could generate relatively high pressure and optimize the flow structure to suppress cavitation [60]. Xun et al. studied the generation and development of pressure around the balance holes. The results indicated that when the NPSHa is high, a low-pressure region exists around the balance holes, leading to cavitation inside the pump. This can be improved by relocating the balance hole away from the blade suction surface to enhance the pump’s cavitation resistance [61]. Huan et al. studied the overall cavitation state of high-speed centrifugal pumps and described the pump’s pressure drop phenomenon. The study identified the relationship between the steam distribution along the inducer and the static pressure distribution, providing a foundation for designing an inducer with better cavitation resistance [62]. Gu et al. studied the impact of impeller roughness on the transient cavitation performance of centrifugal pumps (Figure 11). The results showed that roughness promotes bubble collapse, expands vortex structures, disrupts the stability of the flow path, and increases energy losses within the impeller. Impeller roughness can slightly suppress cavitation, but it also intensifies pressure fluctuations in the flow field [47].

Path 2 focuses on cavitation state monitoring. The centrifugal pump cavitation state monitoring development is closely intertwined with sensor technology, signal processing technology, and artificial intelligence, and is critical to the safe operation of centrifugal pumps [63,64]. The current monitoring of cavitation in centrifugal pumps is based on the analysis of vibration signals, which serves as an effective means for diagnosing cavitation [38,65]. By monitoring the vibration spectrum at specific locations on the pump, it is possible to capture the unique high-frequency energy characteristics or the changes in blade-frequency harmonics associated with cavitation. However, relying solely on a single parameter often has limitations. To improve the accuracy of centrifugal pump cavitation state recognition and early warning, multi-physical signal fusion, such as vibration, noise, and pressure fluctuation, is used for combined diagnostics [66]. With the development of industrial monitoring technology, the field has now entered a new phase of intelligent monitoring centered on machine learning and deep learning [67]. Dong et al. studied the measurement of vibration and noise under different flow conditions. The results indicated that as cavitation developed, the overall pressure level of the liquid-borne noise initially increased and then decreased [68]. Chen et al. proposed a multi-level active learning strategy based on Kriging to study the impact of inlet pressure on cavitation. The results indicate that optimizing the pressure at the pump inlet reduced the cavitation margin by 9.14%, effectively improving the cavitation resistance of the centrifugal pump [69]. Goel et al. applied infrared thermography to cavitation and inlet fault diagnosis in centrifugal pumps, observing the thermal images of pumps operating under different conditions to assess the severity of faults. The results showed that this approach improved efficiency and reduced downtime [70]. Song et al. proposed an improved RIME-SDAE network based on the RIME optimization algorithm and stacked denoising autoencoders (SDAEs). The results showed that the RIME-SDAE network achieved an accuracy of over 98% on the test set, demonstrating the feasibility of the method [71]. Despite the significant achievements, many severe challenges in centrifugal pump cavitation monitoring remain unresolved. Precise detection of early-stage and weak cavitation is still a difficult task. Existing systems can generally determine the presence or absence of cavitation or its severity, but they struggle to accurately distinguish between different types of cavitation.

Path 3 primarily discusses numerical simulation. Numerical simulation of centrifugal pump cavitation has become an important tool for studying cavitation phenomena and optimizing pump performance [72,73,74]. Numerical simulation can reveal the inherent laws of cavitation occurrence and development without relying on complex experiments and assist in optimization design. It also allows for rapid comparison of cavitation resistance performance across different structural designs of key components, such as the impeller and volute, reducing the cost of physical prototypes and experiments. Numerical simulation provides crucial evidence for assessing the potential damage caused by cavitation to flow-passing components. The key to improving the accuracy of numerical simulation lies in how to more realistically describe turbulence and cavitation.Currently, the main numerical simulation methods for turbulence are the Reynolds-Averaged Navier–Stokes (RANS) model and Large Eddy Simulations (LESs). Among them, the unsteady RANS method, based on the Reynolds-Averaged Navier–Stokes equations, is the primary choice for numerical simulation of cavitating flows [75]. RANS calculations are relatively cost-effective and can be applied to complex, fully three-dimensional centrifugal pump geometries and transient computations. RANS is crucial in engineering design. Wang used the RANS method to simulate gap cavitation in a bidirectional axial flow pump. The results showed that, under high-flow conditions, as the cavitation number decreased, the efficiency of the bidirectional axial flow pump slightly increased, and the length of the tip gap cavitation and the area of the cavitation triangle continuously grew [76]. Mehmet used the RANS method to study cavitation under different operating conditions. The results indicated that the cavitation structure is influenced by changes in inlet velocity and pressure conditions, and the distribution of steam volume aligns with the blade passages through the critical flow region [77]. However, the RANS method overestimates turbulent viscosity, suppressing the evolution of multiscale vortex structures in unsteady cavitation flows. This leads to distorted predictions of dynamic processes such as bubble detachment [78], which limits the accuracy of the simulation. To address this issue, the Large Eddy Simulation (LES) [79] method, through the concept of filtering, directly simulates large-scale vortices. LES uses subgrid-scale models to simulate small-scale vortices, enabling precise capture of cavitation turbulence vortex motion and its instability. Therefore, LES is considered a key method for advancing the precise quantitative prediction of cavitating fluid dynamics [80]. LES calculations are extremely costly and are currently primarily used for mechanism studies or localized high-resolution simulations of critical regions [81] (Figure 12). For example, Sigfried W. proposed an active model splitting hybrid RANS/LES approach, demonstrated on fully developed incompressible channel flow and periodic hills, with improved accuracy of the results [82]. Han used the LES method and the ZGB cavitation model for numerical simulations. The results showed that the development of cavitation enhanced vortex generation and flow instability around the pump tip gap. During the cavitation process, the vortices were primarily located around the cavitation zone [83]. Hang studied the energy loss caused by cavitation in centrifugal pumps using an improved entropy production method (EPMI) that considers water–vapor phase interaction. The results showed that EPMI could predict energy loss more accurately [84].

Cavitation is a multiphase flow problem, and most of the simulated cavitating flows use homogeneous flow models. The widely used homogeneous flow model assumes that the entire cavitation flow field is composed of a single-phase fluid with variable density. The homogeneous equilibrium model (HEM) applies a set of partial differential equations to control fluid motion and state [75]. Common cavitation models incorporate one or more empirical coefficients, whose accuracy and universality are constrained by the simplifications made to physical reality. When applied to pumps operating in different media, temperatures, or geometric configurations, the predictive accuracy may significantly deteriorate [85]. By incorporating bubble dynamics effects into the source term of the bubble volume fraction equation, various cavitation models have been derived (

Table 2. Cavitation models.

Model	Formula	Advantage
Singhal model	$p \le p_v: {\dot{m}}^{+}=C_{\mathit{dest}}{\displaystyle \frac{\sqrt{k}}{\sigma }}{\rho }_l{\rho }_v\sqrt{{\displaystyle \frac{2}{3}}{\displaystyle \frac{p_v-p}{{\rho }_l}}}(1-{\alpha }_v)$ $p > p_v:{ \dot{m}}^{-}=C_{\mathit{prod}}{\displaystyle \frac{\sqrt{k}}{\sigma }}{\rho }_l{\rho }_v\sqrt{{\displaystyle \frac{2}{3}}{\displaystyle \frac{p-p_v}{{\rho }_l}}}{\alpha }_v$	The physical description of cavitation is more comprehensive, capturing the phase change processes triggered by the periodic variations in the cavitation source mass.
Schnerr-Sauer model	$p \le p_v:{\dot{ m}}^{+}={\displaystyle \frac{3p_v{\rho }_l}{\rho }}{\alpha }_v(1-{\alpha }_v){\displaystyle \frac{1}{R_b}}\sqrt{{\displaystyle \frac{2}{3}}{\displaystyle \frac{p_v- p}{{\rho }_l}}}$ $p > p_v:{\dot{ m}}^{-}={\displaystyle \frac{3p_v{\rho }_l}{\rho }}{\alpha }_v(1-{\alpha }_v){\displaystyle \frac{1}{R_b}}\sqrt{{\displaystyle \frac{2}{3}}{\displaystyle \frac{p-p_v}{{\rho }_l}}}$	It is more aligned with real-world conditions, with strong accuracy in predicting cavitation in engineering flow.
Zwart-Gerber-Belamri model (ZGB)	$p \le p_v: {\dot{m}}^{+}=C_{\mathit{vap}}{\displaystyle \frac{3{\alpha }_v{\rho }_v}{R_b}}\sqrt{{\displaystyle \frac{2}{3}}{\displaystyle \frac{p_v-p}{{\rho }_l}}}$ $p > p_v:{\dot{ m}}^{-}=C_{\mathit{cond}}{\displaystyle \frac{3{\alpha }_v{\rho }_v}{R_b}}\sqrt{{\displaystyle \frac{2}{3}}{\displaystyle \frac{p-p_v}{{\rho }_l}}}$	The convergence speed is fast, balancing computational efficiency and stability, making it highly practical for engineering applications.

), such as the Singhal model, Schnerr–Sauer model, Zwart model, etc. Among these, the Zwart cavitation model has good computational stability and fast convergence speed, and can accurately predict cavitation inception and evolution through empirical coefficients. The Zwart cavitation model is particularly widely used in engineering applications. Modifications to the Zwart cavitation model primarily focus on three aspects: first, correcting the phase transition rate of bubble formation and collapse; second, introducing considerations for the effects of turbulent pressure pulsations or liquid compressibility; and third, refining key parameters such as the nucleation density and critical radius to enhance the model’s predictive accuracy under various operating conditions. Many scholars have currently improved the Zwart cavitation model to better predict cavitation effects. Wang et al. improved the Zwart cavitation model and the RNG turbulence model’s rotational motion characteristics. The results showed that the modified ZGB model’s cavitation performance curve was closer to experimental values [86]. Lai et al. modified the Zwart cavitation model through local entropy generation, and the results indicated that the improved Zwart cavitation model could better capture cavitation morphology and its evolution, with the impeller being the main location for turbulence and near-wall entropy generation [87]. Su et al. improved the Zwart model by incorporating the second-order term and surface tension term from the Rayleigh–Plesset equation, as well as the influence of the initial gas pressure inside the bubble. The results showed that the improved cavitation model could better predict the cavitation morphology of the hydrofoil, and the centrifugal pump cavitation performance curve derived from the modified model was closer to the experimental results than the original Zwart cavitation model [88].

Path 4 focuses on complex multiphase flow. The current computational methods for cavitation research mainly include interface tracking methods and two-phase flow methods [89]. In interface tracking methods, since the vapor density is much smaller than the liquid density, it is only necessary to solve the governing equations for the liquid phase [90]. The two-phase flow method treats the gas–liquid phases as a mixed medium and simulates the cavitation phase change process by solving the phase fraction transport equation. When conveying fluids containing solid particles, cavitation issues become more severe [91,92], resulting in complex solid–liquid–gas three-phase coupling flow. The presence of solid particles exacerbates wear on flow-exposed components and may also serve as cavitation nuclei, promoting the initiation of cavitation bubbles. The collapse of the bubbles then alters the flow field structure, affecting the trajectory and distribution of particles, creating a vicious cycle. This multiphase flow interaction causes a sharp decline in pump performance and significantly shortens the pump’s service life. Therefore, in-depth research into the mechanisms of multiphase flow in centrifugal pumps is crucial for optimizing pump design and improving operational reliability under harsh conditions. Future research will focus on algorithm optimization and the study of multiphase flow coupling mechanisms to uncover the synergistic effects between particle characteristics and cavitation. For example, Wang et al. studied the interaction between sand particles and cavitation in the cavitating flow field of centrifugal pumps. They identified the dominant roles of cavitation erosion and sand particle impingement in the cavitation erosion–sand particle erosion process [74]. Li et al. proposed a cavitation multiphase unsteady model to study the cavitation characteristics and mass transfer processes of the gas–liquid two-phase flow inside centrifugal pumps under different operating conditions and impeller structural parameters [93]. Song et al. developed a prediction model for gap erosion in gas–liquid–solid three-phase flow, incorporating a gas core factor into the gap erosion model for accurate predictions [94].

3.2.3. Analysis of Keyword Burst Graph

By systematically revealing its evolutionary patterns through temporal distribution and burst intensity, we further assess research trends in centrifugal pump cavitation using keyword emergence maps (Figure 13). “Centrifugal pumps” is a core keyword throughout the entire period, appearing as early as 2007 (burst strength 3.8) and further intensifying in 2009 (burst strength 4.16). It spans key stages such as 2010–2014 and 2020–2021, indicating that centrifugal pumps constitute a fundamental research focus that has received sustained attention. From 2007 to 2018, numerical simulation (burst strength 4.84) served as a core technical approach in centrifugal pump research. After 2017, the research focus shifted toward key phenomena under actual operating conditions, particularly pressure fluctuation (burst strength 4.14 in 2017 and 4.05 in 2019). This shift reflects a transition from simulation-based studies toward practical problem-solving in real operating environments.

As research into centrifugal pump cavitation deepens, research cycles are shortening and hot topics are evolving at an accelerated pace. Early keywords, such as centrifugal pump and numerical simulation, typically exhibited active periods of 3–4 years (2010–2014 and 2015–2018). In contrast, after 2017, emerging keywords such as erosion and pressure fluctuation generally showed much shorter active periods of 1–2 years (2017–2018 and 2018–2019). This trend reflects a faster research pace in the centrifugal pump cavitation field, with more intensive exploration of new directions and emerging issues.

4. Conclusions and Recommendations

This study analyzed 645 English-language publications on cavitation in centrifugal pumps from the Web of Science Core Collection database. Using Space 6.3 R1 software, we conducted a visual analysis of annual publication trends, institutional collaborations, and keyword networks.

(1) Keyword co-occurrence network and temporal evolution analysis indicate that research on cavitation in centrifugal pumps has progressed from describing macroscopic phenomena to investigating microscopic mechanisms, forming a relatively systematic theoretical framework. Over the past two decades, research on cavitation in centrifugal pumps has evolved toward high-precision numerical simulation and mechanism modeling. The focus has gradually shifted from the cavitation phenomenon itself to the analysis of unsteady mechanisms, predictive control, and the expansion of engineering applications.

(2) Cluster structure analysis reveals that existing research still exhibits shortcomings in terms of the universality of cavitation models and complex operating conditions. Research on cavitation numerical simulation for centrifugal pumps dominates the literature landscape, yet a disconnect persists between theoretical cavitation modeling and practical engineering applications. Future studies urgently need to enhance the applicability of cavitation models in the design and operation of actual centrifugal pumps.

This paper proposes the future development focus of cavitation in centrifugal pumps: (1) Current models still have deviations in predicting cavitation, and developing more accurate unsteady multiscale coupling models will be a key focus moving forward. (2) With the rise of artificial intelligence, developing intelligent monitoring and management systems that can provide early and accurate cavitation state warnings, as well as predict cavitation development trends, will combine sensor technology with AI algorithms. This holds tremendous application value for future development. (3) The anti-corrosion capabilities of centrifugal pumps can also be enhanced through the application of new materials and processes.

Although this bibliometric study has made valuable contributions, it is important to acknowledge several limitations. This analysis is based solely on the Web of Science Core Collection database. Web of Science is widely recognized for its high-quality and well-structured literature records in engineering and energy-related fields; however, relevant research indexed in other databases may not be fully covered. This study exclusively included English-language research from the literature. Some relevant research published in other languages may have been excluded. Future research may achieve a more comprehensive and reliable bibliometric analysis by integrating multiple databases and incorporating multilingual literature.