Pressure Fluctuation Signals Research Articles

Prediction of Pressure Fluctuation of Double-Suction Centrifugal Pump in Different Cavitation Conditions based on EMD-GRU

Abstract The double-suction centrifugal pumps play a crucial role in various applications such as in irrigation-drainage projects, water diversion projects, and urban water supply and drainage projects. The pressure fluctuation is one of the crucial parameters for assessing the operational stability of pump. The pressure fluctuation exhibits distinct characteristics in various cavitation conditions. A pressure fluctuation prediction method based on Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) is proposed in this paper. The model enables accurate forecasting of pressure fluctuation signals when pump operates at different cavitation conditions. The data were obtained from experimental measurements of a double-suction centrifugal pump. The original signal was decomposed into multiple sub-signals with varying energy levels using the EMD method. The neural network of GRU was employed for training and data prediction on individual sub-signals. The predicted results of each sub-signal were combined to form the final pressure fluctuation signals. The method based on EMD-GRU proves effective in predicting pressure fluctuation in three different cavitation conditions. The mean absolute error (MAE) in time-domain characteristics is below 6%, and the prediction error for the main frequency is below 1%. The prediction of low-frequency characteristics still requires improvement.

Study on Power Losses and Pressure Fluctuations of Diffuser Mixed Flow Pump as Turbine on Different Power Generation Speeds Based on Energy Power Models

Variable speed regulation represents an effective means to ensure the efficient operation of the diffuser mixed flow pump as turbine (DMFPAT). This paper employed computational fluid dynamics-based energy power models and high-frequency water pressure fluctuation tests to investigate the power losses and pressure fluctuations of DMFPAT under different power generation speeds. Analysis of power losses reveals that internal power losses in DMFPAT predominantly originate from the turbulent dissipation power () in the entropy production and the turbulent kinetic energy production power () in the energy balance equation, indicating that fully developed turbulence significantly contributes to substantial power losses in DMFPAT. A strong correlation exists between power generation speed and power losses, and decreasing rotational speed results in gradual reduction of power losses in DMFPAT. Additionally, a comprehensive analysis of the pressure fluctuation signals of DMFPAT under variable speed mode was conducted, revealing that the main frequency at each monitoring point is the rotational frequency and its harmonics. As speed decreases, the pressure fluctuation amplitude diminishes. The research presented demonstrates that adjusting the speed not only markedly reduces power losses but also eliminates adverse pressure fluctuations, thereby providing a theoretical basis for the efficient, stable and safe operation of DMFPAT.

Research on pressure propagation in long-distance water pipelines for fault conditions by VMD and wavelet transform

Long-distance water pipelines in complex geographical environments face potential risks such as leaks. This study uses variational mode decomposition (VMD) and continuous wavelet transform (WT) to analyze pressure fluctuation signals in pipelines for detecting and locating leaks and other anomalies. CFD simulations are conducted to obtain pressure fluctuation data under various fault conditions. The pressure signals are then decomposed into multiple Intrinsic Mode Functions (IMFs) using VMD, and WT is applied to analyze their time–frequency characteristics. The results show significant differences in amplitude and frequency of IMF components between fault and normal conditions, with reduced amplitude and altered frequency under fault conditions, providing a basis for fault detection. Additionally, the IMF characteristics differ at different fault locations, aiding in fault localization. This research offers an effective method for leak detection and localization in long-distance pipelines, crucial for enhancing safety and stability.

Pressure And Noise Estimation Of Tandem Cylinder Flow Based On PIV

The present work proposes an optimized method for pressure reconstruction and far-field noise prediction for tandem cylinder flow based on time-resolved planar particle image velocimetry (PIV). Proper orthogonal decomposition (POD) low-order reconstruction is applied to mitigate the incoherent noise introduced during PIV measurement and processing and enhances the identification of dynamic characteristics of relative structures within the flow field. As a result, PIV-based pressure reconstruction through solving the Poisson equation encompasses fewer errors arising from contaminated boundary conditions and Poisson source terms, thereby mitigating the propagation of errors into pressure. The time-marching algorithm accelerates the convergence and stabilizes the iterative progress while solving the Poisson equation, leading a considerable computation savings for cases with multiple snapshots like aeroacoustics relative investigation. The reconstructed wall pressure is compared with the reference pressure fluctuation signal simultaneously measured, yielding good agreement. Finally, the far-field noise was predicted through Curle’s analogy based on reconstructed wall pressure, considering the spanwise correction derived through an additional spanwise planar PIV. The predicted far-field noise agrees well with the reference microphone measurement. The overall assessment demonstrates that the employment of POD low-order reconstruction and time-marching algorithm significantly improve the speed and accuracy for estimating pressure field and far-field noise.

Dynamic multiscale pressure fluctuation features extraction of mixed-flow pump as turbine (PAT) and flow state recognition of the outlet passage using variational mode decomposition and refined composite variable-step multiscale multimapping dispersion entropy

The feature extraction of pressure fluctuation signal (PFS) and flow state recognition of outlet passage have crucial engineering significance to guarantee the safe and reliable operation in mixed-flow pump as turbine (PAT). To accurately extract the dynamic multiscale features of PFS, a method based on variational mode decomposition (VMD) and refined composite variable-step multiscale multimapping dispersion entropy (RCVMMDE) is proposed. By applying VMD to PFS, intrinsic mode functions (IMFs) are obtained, and the RCVMMDE values for each IMF is then calculated. Model parameters based on the RCVMMDE indicator are then established and used as feature vectors for flow state recognition. Using the PFS at the outlet passage inlet as an example, this method extracts dynamic multiscale feature information of the outlet passage, which is validated through experimental and numerical simulations. The results show that this method achieves high accuracy, providing well-defined feature vectors and effectively capturing the dynamic multiscale features of the PAT and turbine systems.

Hybrid method for hydraulic transient analysis of liquid pipe networks based on characteristic lines and equivalent circuits

Water hammer poses a significant concern in the transportation of pipelines and pipe networks. Due to the passage of reflection of water hammer waves at nodes, a large number of high-frequency pressure fluctuation signals appear in the pipe network, demanding an intensive computational workload. A novel hybrid method based on characteristic lines and equivalent circuits is proposed for the accurate and fast simulation of pipe networks. Multiple methods are employed for the simulation of pipelines and pipe networks, followed by a comparative analysis. The accuracy of the hybrid method is validated by comparing the proposed method with simulation results of commercial software. The hybrid method reduces the significant computational workload inherent in the classic method of characteristics and enhances the accuracy at high frequencies in the state-of-the-art elastic water column model. A trade-off between simulation accuracy and computation speed is achieved by adjusting the time step. The accuracy of calculating higher-frequency pressure fluctuations is further validated with an increase in the pipe diameter and a corresponding reduction in the friction loss. Generally, this method enables the simultaneous descriptions of high-frequency characteristics and nonlinear friction loss, enhancing calculation speed with little impact on the accuracy. The results demonstrate that the proposed model can be utilized for the transient analysis of pipe networks, providing support for predicting system performance and designing optimal strategies.

Numerical and field experimental study on the start-up process of a prototype axial-flow pump system considering the motion characteristics of cutoff facilities

Clarifying the evolutionary mechanism of the start-up process of a large axial-flow pump that includes cutoff facilities is of great significance for ensuring the safe and stable operation of such a system. However, the three-dimensional dynamic characteristics of the start-up process of a prototype axial-flow pump system (PAPS), considering cutoff facilities, are still unknown. In this paper, a method combining motor starting characteristic experiment, computational fluid dynamics, and field test of prototype pump system is proposed to study the starting process of PAPS. The results indicate that flow interruption facilities will significantly affect the start-up process of the pump system. High-speed forward fluid particles collide with reverse fluid particles in the guide vane channel during the acceleration of the pump owing to the influence of the cutoff facilities. A large number of wall vortex structures block the guide vane channel. This leads to a brief plateau period during the increase in instantaneous head. As the start-up process progresses, a clear horseshoe-shaped vortex structure is formed at the trailing edge of the guide vane, which subsequently falls back. The accelerated shedding of horseshoe-shaped vortical structures at the trailing edge of the guide vane induces high-frequency pulsating components, leading to a high-energy region of the pressure fluctuation signal at the outlet of the guide vane, which gradually expands toward higher frequencies. During this transition process, the flow field near the cutoff facility also exhibited significant unstable flow behavior. After the interaction between the outflow at the flap gate and the outflow at the gate, entrainment occurs, forming a region of circulating motion, and a large number of vortices with a jet-like structure are formed at the exit of the flap gate.

Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features.

To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes.

Pressure fluctuations in a fluidized bed of binary particles with significant differences in particle size

Pressure fluctuation signals are investigated in a binary particle fluidized bed in which two types of particles have similar physical properties but significant differences in particle size (90 μm Vs. 1800 μm). The relationship of the measured pressure drop to gas velocity exhibits a distinct ‘‘step’’ profile, indicating that the incipient fluidization of binary particle bed can be characterized by different stages, i.e. the fixed bed, the fine particle fluidization, the coarse particle partial fluidization and the binary particle complete fluidization. And then the statistical method and scale decomposition method are used to quantify pressure fluctuation amplitude and frequency in each stage. The result shows that when binary particles are completely fluidized, the bubble size and frequency in binary particle systems are larger than that in sole fine particle systems. Additionally, binary particle systems need higher gas velocity to achieve turbulent fluidization. Furthermore, the local solids holdup measured with optical fiber probes is also employed to verify the result of pressure fluctuation analysis.

LSTM Reconstruction of Turbulent Pressure Fluctuation Signals

This paper concerns the application of a long short-term memory model (LSTM) for high-resolution reconstruction of turbulent pressure fluctuation signals from sparse (reduced) data. The model’s training was performed using data from high-resolution computational fluid dynamics (CFD) simulations of high-speed turbulent boundary layers over a flat panel. During the preprocessing stage, we employed cubic spline functions to increase the fidelity of the sparse signals and subsequently fed them to the LSTM model for a precise reconstruction. We evaluated our reconstruction method with the root mean squared error (RMSE) metric and via inspection of power spectrum plots. Our study reveals that the model achieved a precise high-resolution reconstruction of the training signal and could be transferred to new unseen signals of a similar nature with extremely high success. The numerical simulations show promising results for complex turbulent signals, which may be experimentally or computationally produced.

A new type of velocity averaging tube vortex flow sensor and measurement model of mass flow rate

Accurate mass flow rate measurement of fluid is one of the key technologies to ensure product quality and utilization of energy resources. However, the accuracy of traditional combined measuring devices is limited. In this paper, a new type of velocity averaging tube vortex flow sensor (VATV) is designed, and the mass flow rate measurement model is proposed and analyzed to achieve the high precision measurement of mass flow rate. The size, shape and structure of the VATV are designed in this study and there are no moving parts inside the integrated VATV. The method of double differential pressure is proposed to achieve the measurement of the average differential pressure signal and differential pressure fluctuation signal. Then, the method of Empirical Wavelet Transform (EWT) is used to extract the vortex shedding frequency from the differential pressure fluctuation signal. It indicated that the proposed VATV achieves an accuracy of ±0.50%, and the repeatability is also lower than 0.07%. A remarkable improvement of accuracy is achieved compared to the original combined measurement devices. The proposed sensor shows good prospect for the detection of gas-liquid two-phase flow such as wet stream due to the feature of strong anti-interference, high stability and reliability.

Critical State Calculation of Saddle-Shaped Unstable Region of the Axial-Flow Pump Based on Bifurcation SST k–ω Model

This study aims to investigate the critical state of the saddle-shaped unstable region of the axial-flow pump and propose a suitable criterion for identifying this state. The bifurcation SST k–ω model considered the rotation effect is used in the present work and verified in the numerical calculation of a water jet pump. Then, it is used to simulate the critical state of the axial-flow pump. Results show that the leading-edge separation vortex generates at 0.6Qd, while the head declines only at 0.55Qd. Therefore, using the inflection point of the head-flow curve as the critical state criterion is unsuitable. In addition, the fixed monitoring point is unsuitable for identifying the critical state due to the insensitivity to the amplitude, main frequency, and periodicity changes at the critical state. Finally, to identify the critical state, it is essential to arrange a monitoring point at the leading edge of the blade suction near the shroud, which should rotate with the impeller. The critical state criterion is that the main frequency position of the pressure fluctuation signal is offset at the monitoring point, and the amplitude is increased by 10 times.

Flow regime identification using pressure fluctuation signals in an aerated vessel stirred

A novel method was proposed to determine the flow regime based on the bottom pressure fluctuations of the stirred tank. Statistical, Hurst, Shannon entropy and chaos analysis methods were used to analyze the pressure time series in an aerated stirred tank with a Rushton impeller, and the relationship between the characteristic parameters and the flow regimes was established. The results showed that the bubble motion in the loading regime was obviously stronger than that in the flooding regime, and the homogeneity of the system was rapidly improved. Compared with the first two stages, the chaotic extent of the complete dispersion regime was slightly weaker, and the benefit generated by continuously increasing the speed was limited at this time. In addition, considering the sensitivity of pressure pulsations to the gas–liquid dispersion state, the transition points of the three flow regimes were successfully identified by the standard deviation and the largest Lyapunov exponent. The data were well consistent with the measurements from the visual method and power method, with a maximum deviation of 16%. This demonstrated that it was a reliable non-invasive method for flow regime identification in gas–liquid stirred tanks.

Pressure fluctuations in a gas-solid fluidized bed at temperatures up to 1650 °C

Fluidized beds operating at ultra-high temperatures have great application potential for synthesizing essential materials and chemicals, but they are relatively unexplored. This study, for the first time, investigates fluidization characteristics at temperatures up to 1650 °C by measurement and analysis of pressure fluctuations in a laboratory fluidized bed of 30 mm diameter with corundum particles of an average size of 900 μm. Standard statistical and spectral methods are used to analyze pressure fluctuation signals and characterize fluidization behavior based on the effect of temperature on pressure fluctuation parameters, including the probability density function, standard deviation, autocorrelation, power spectra density, amplitude, dominant frequency, and average cycle frequency. The results indicate that, for the coarse corundum particles used in this study, fluidization behavior inverts at a crucial temperature of approximately 1400 °C. Below this temperature, gas–solid flows in the bed are predominantly controlled by gas bubble movements. In this temperature range, periodicity and maximum amplitude decrease and dominant frequency increases with temperature. Above 1400 °C, interparticle forces, such as van der Waals and viscous forces, are significantly strengthened due to changes in the structural and physiochemical properties of solid materials. The greatly enhanced interparticle forces at ultra-high temperatures promote the dominance of microstructured flows over the overall pressure fluctuations, making interparticle forces the controlling factor influencing gas–solid flows in fluidized beds at ultra-high temperatures. These findings provide a better understanding of ultra-high temperature fluidized beds and can promote their research and development for industrial applications.

Mode Switching in a Compressible Rectangular Cavity Flow

This study determines the mean and fluctuating pressures for flow through a rectangular shallow cavity (ratio between the length and the depth = 2.43, 4.43, and 6.14; ratio between the length and the width = 0.5, 1.0, and 2.0) at a Mach number of 0.64 in a blowdown transonic wind tunnel. A amplitude modulation analysis is used for the post-processing of the fluctuating pressure signals. The spectral analysis (wavelet) shows the intermittent behavior of the discrete Rossiter–Heller modes. A correlation analysis determines that mode switching is more significant between the second and third modes (organized structures that are associated with shear layer vortices), particularly for two-dimensional and shallower cavities.

Leading-edge bluntness effects on the hypersonic flow over the double wedge at multiple aft-wedge angles

The present numerical investigation focuses on the leading-edge bluntness effects on the double wedge with varied aft-wedge angles exposed to low enthalpy hypersonic free stream conditions. The bluntness ratio in this study varies, ranging from R/L1 = 0 (sharp leading edge) to R/L1 = 0.577 (maximum allowable bluntness), along with the aft-wedge angle varying between θ2 = 45° and 60°. Noticeably, even a small bluntness ratio can completely change the shock interaction pattern compared to its sharp geometrical counterpart due to a detached leading-edge shock, enlarged separation bubble, and location of various shock waves concerning it. Critical bluntness ratios exist for the low aft-wedge θ2 = 45° angle, but increasing the aft-wedge angle makes the flow field highly unsteady for some bluntness ratios. Nevertheless, these bluntness ratios for such double-wedge configurations are reported using the mean of separation bubble size. Moreover, this work unravels the cause of such unsteadiness for the unsteady flow fields using the spatial-temporal evolution of the wall pressure distribution and fast Fourier transform of the pressure fluctuation signal at the compression corner and supports the deduced observation with the help of energy-based proper orthogonal decomposition. The increased shock–boundary layer interaction strength moves the separation point upstream beyond the junction of cylindrical bluntness and inclined fore-wedge surface, accompanying sudden change in its direction of motion that perturbs the shear layer that set to a self-sustained, highly unsteady flow field.

Quantification and investigation of pressure fluctuation intensity in a multistage electric submersible pump

Pressure fluctuation is an important factor affecting the stability of rotating machinery. Electric submersible pumps (ESPs) are generally arranged in a multistage series structure, and its internal unsteady flow is extremely easy to propagate and develop in the lengthy flow passage, which brings about differences in the characteristics of pressure fluctuations in each stage. In contrast to the conventional method of processing pressure fluctuation signals, we propose a parameter called “energy flow density (EFD)” of pressure pulsation by analogy with the definition of wave intensity, in order to directly quantify the intensity of pressure fluctuations. Here, we study these pressure fluctuation characteristics using a typical three-stage ESP as the research object. We apply theoretical analysis, numerical simulation, and test verification. First, in comparisons between numerical predictions of pressure fluctuation and test results, the period, amplitude, and phase of pulsation curves are highly consistent, verifying the accuracy of the numerical method employed in this paper. Next, the mechanism underlying the pressure fluctuations and the characteristics of inter-stage interference are investigated through flow field analysis. Subsequently, the results of the evaluation of the pressure fluctuations based on EFD processing are compared with those obtained in the conventional way. The results are consistent in terms of characterizing the multistage ESP pressure fluctuation characteristics, but the conventional method does not reflect subtle differences due to inter-stage propagation and coupling. However, the EFD method combines the amplitudes of all signals and provides the intensity of pressure fluctuations directly, which reflects inter-stage differences. Our results provide a theoretical basis for improving the operational stability of ESPs connected in a multistage series and have practical engineering significance.

Hierarchical entropy analysis for flow pattern of steam-liquid two phase flow in a rod bundle

Flow pattern is very key parameter in the study of flow characteristics and heat transfer characteristics of two-phase flow. In this Paper, two-phase boiling experiment in a 3×3 rod bundle was conducted within the following parameter range: 0≤G≤600kg/(m2⋅s),0.1≤p≤0.12MPa,10≤ΔTsub≤30℃. We first observed the four typical flow patterns, including bubble flow, bubble-churn flow, churn flow, annular flow, and the physical characteristics were studied and the corresponding differential pressure fluctuation signals measured for the four typical flow patterns. The dynamic characteristic of typical flow pattern was investigated by the of hierarchical entropy algorithm. Then the hierarchical entropy value of different flow patterns was calculated. The results indicate that the hierarchical entropy has a better ability to resist noise, and can reveal dynamics complexity of different flow pattern in rod bundle.

Nonlinear dynamic characteristics of suction-vortex-induced pressure fluctuations based on chaos theory for a water jet pump unit

To improve the effective use of ocean energy by water jet propulsion pump, it is necessary to study the underwater suction vortex (USV), which affects the propulsion and stealth performance of water jet propulsion pump. This paper analyses the time-frequency and nonlinear dynamic characteristics of the pressure fluctuation induced by USV through high-speed photography and high-frequency pressure fluctuation tests. The nonlinear characteristics of pressure fluctuations under the conditions with and without USV are compared and analyzed based on the chaotic time series analysis theory. The results show that the chaotic characteristics of the pressure fluctuation signal are unaffected by the occurrence of USV. However, the singular attractor structure of the pressure fluctuation signals induced by USV is highly complex, and the largest Lyapunov exponent increases. Therefore, the pressure fluctuation signals induced by USV have stronger chaotic characteristics. Note that the saturation correlation dimension of the pressure fluctuation signal in the period without USV is larger than that with USV, mainly because the influence factor of the pressure fluctuation signal in the USV period is relatively single, which is primarily induced by USV. The nonlinear characteristics analysis of the pressure fluctuation signal can effectively identify the presence of USV.

Bubble Morphology Analysis and Pressure Drop of Gas–Liquid Two-Phase Flow inside a Quarto Static Mixer

The upward two-phase flow in vertical static mixers is beneficial for dispersion mixing. The bubble size distribution, aspect ratio, and angle distribution of the dispersed phase at various superficial liquid/gas velocities in an industrial Quarto static mixer (QSM) are experimentally studied. The dispersion performance of the QSM is assessed by Sauter mean diameter (d32), and the empirical correlation of axial d32 distribution is proposed. The gas–liquid pressure drop in the QSM is measured under UL = 0–0.071 and UG = 0–0.042 m/s, and two new dimensionless empirical correlations are proposed. Based on pressure fluctuation signal time series, the standard deviation, kurtosis, and skewness are analyzed, and the inlet and outlet critical velocities of flow pattern transition from pseudo-homogeneous to heterogeneous in the QSM are identified. Finally, the effect of UG on the dominant frequency and power spectral density function is quantitatively analyzed at the inlet and outlet sections.