Two-phase Flow Patterns Research Articles

State of the Art on Two-Phase Non-Miscible Liquid/Gas Flow Transport Analysis in Radial Centrifugal Pumps-Part A: General Considerations on Two-Phase Liquid/Gas Flows in Centrifugal Pumps

Gas–liquid mixtures are present in numerous industrial applications, such as in the process industry, oil production and transport with natural gas, deep-sea extraction, and irrigation. Any pump may have to carry multiphase flows. However, the present document is related to non-miscible liquid/gas flow transport analysis in centrifugal pumps because which topic can be a more challenging task compared with axial and mixed flow machines due to specific body force and buoyancy actions and large density differences between the phases. The present document first introduces the main usual gas–liquid two-phase definitions and simplifications. A dimensional analysis introduces the main flow variables and parameters that are used for pumps. Basic physical aspects of flow motion in an impeller channel are explained, and a rapid description of two-phase flow patterns in radial flow pumps is described. Finally, a review of simplified empirical and semi-empirical analytical models is proposed with their limitations.

Identification of Gas-Water Two-Phase Flow Patterns in Horizontal Wells of Shale Gas Reservoirs Based on Production Logging Data

In order to clarify the gas-water two-phase flow law in horizontal wells and study the gas-water two-phase flow characteristics in horizontal wells, firstly, the gas-water two-phase in a horizontal well is numerically simulated and analyzed, and the flow pattern distribution under different well inclination angles and different phase separation flow rates is obtained. Secondly, a series of production logging instruments including CAT instrument was used to conduct experimental research on gas-water two-phase flow under different flow conditions, and the measured values of each CAT probe were extracted to reflect the local holdup under different flow patterns. Finally, SSA-BP neural network algorithm is used to identify a gas-water two-phase flow pattern in a wellbore by using experimental parameters such as center holdup, well inclination angle, spinner revolution, and CAT probe measurements. The recognition accuracy of the neural network was improved from 83.75% to 91.66%, and the operation speed was accelerated. It provides a research idea to explore the flow characteristics of gas-water two-phase flow in horizontal wells.

Identification of Oil and Gas Two-Phase Flow Patterns in Aero-Engine Bearing Chambers Based on Kriging Method

The identification of the flow pattern within the bearing chamber's oil and gas two-phase flow is crucial for its lubrication design. Aiming at the lack of accuracy and universality of the current bearing chamber oil and gas two-phase flow pattern recognition, this paper introduces a new method for recognising oil and gas two-phase flow patterns based on the Kriging model. In this paper, the oil-gas two-phase flow field in the bearing chamber is modelled in Comsol simulation software using the level set-k- ε model. Subsequently, the finite element analysis results were used as samples to fit the Kriging model, and the effects of different variables on the flow pattern were analysed and compared with the actual results. The results demonstrate that the flow pattern discrimination results fitted by the Kriging model exhibit higher accuracy and greater generalisability. This holds significant implications for future lubrication and structural design of bearing chambers.

Electrochemical performance study of proton exchange membrane electrolyzer considering the effect of bubble coverage

Bubble dynamics are closely related to the electrochemical performance of a proton exchange membrane electrolyzer (PEMEC). However, tiny bubbles need to be clustered together to affect the electrochemical performance of PEMEC significantly. In this paper, the effect of microscopic bubbles on macroscopic electrochemical properties were assessed by bubble coverage. The bubble dynamics, two-phase flow, and electrochemical performance were captured under different conditions using a high-speed, microscopic visualization experimental system. The results show that various factors influence the two-phase flow pattern. At 60 °C, 1.5 A/cm2 and 5 mL/min, the annular flow occupied 76.8% of the gas phase area, and when the water flow increased to 80 mL/min, the annular flow ratio decreased substantially to 2.7%. The two-phase flow of bubbles in the flow channel showed different flow patterns over time. Under the experimental conditions (60 °C, 20 mL/min, 0.8 A/cm2), the bubble flow pattern experienced the emergence of bubbles, bubble flow, segmental plug flow, annular flow, and final steady state with the occurrence times of 0.15 s, 1.5 s, 5.0 s, 10.5 s, and 21.2 s, respectively. The bubble coverage increased with current density and temperature, while it decreased with the increase of water velocity. In addition, the effects of temperature and water velocity on bubble coverage and PEMEC performance vary in principle. Specifically, higher temperature mainly improves the bubble coverage by increasing the electrochemical performance of PEMEC. In contrast, higher water velocity mainly improves the electrochemical performance of PEMEC by decreasing the bubble coverage. This study elucidates the relationship between microscopic bubbles and macroscopic electrochemical performance, contributing to a better understanding of the processes and principles of bubble effects on the electrochemical performance of PEMEC. The results may provide a theoretical basis and experimental data for operating condition optimization, operating efficiency improvement, multiphase flow study, gas diffusion layer structure, and flow field design of PEMEC.

Research on Mechanism of Dynamic Evolution Vibration Characteristics of Horizontal 90 Deg Elbow Flow Pattern

AbstractPipeline vibration induced by fluid flow can cause pipeline fatigue damage, which seriously endangers the safety of the operation. This paper aims to clarify the dynamic variation law of pipeline vibration induced by gas–liquid two-phase flow in the elbow and the influence of different gas–liquid ratios on the dynamic evolution of two-phase flow patterns. The volume of fluid (VOF) method is used to capture the flow pattern characteristics to explore the complex information of dynamic flow pattern evolution. The vibration characteristic mechanism corresponding to the evolution of the flow pattern is explored based on the time and frequency domain, and the origin of the exciting force of the elbow is explored according to the momentum balance equation. The simulation results show that the VOF method can well capture the characteristics of slug flow developed by the interphase instability mechanism. After phase space reconstruction, the dynamic evolution mechanism of the flow pattern is complex and the chaotic characteristics of slug flow are strong. The evolution of the flow pattern is related to the increase in wave height. The superficial velocity of the liquid phase is more sensitive to the flow pattern's formation mechanism than the gas phases. The amplitude of the wave strongly depends on the Vsg and Vsl. There is a strong correlation between the main exciting force pulsation and momentum flux pulsation.

Two-Phase Flow Pattern Identification by Embedding Double Attention Mechanisms into a Convolutional Neural Network

There are inevitable multiphase flow problems in the process of subsea oil-gas acquisition and transportation, of which the two-phase flow involving gas and liquid is given much attention. The performance of pipelines and equipment in subsea systems is greatly affected by various flow patterns. As a result, correctly and efficiently identifying the flow pattern in a pipeline is critical for the oil and gas industry. In this study, two attention modules, the convolutional block attention module (CBAM) and efficient channel attention (ECA), are introduced into a convolutional neural network (ResNet50) to develop a gas–liquid two-phase flow pattern identification model, which is named CBAM-ECA-ResNet50. To verify the accuracy and efficiency of the proposed model, a collection of gas–liquid two-phase flow pattern images in a vertical pipeline is selected as the dataset, and data augmentation is employed on the training set data to enhance the generalization capability and comprehensive performance of the model. Then, comparison models similar to the proposed model are obtained by adjusting the order and number of the two attention modules in the two positions and by inserting other different attention modules. Afterward, ResNet50 and all proposed models are applied to classify and identify gas–liquid two-phase flow pattern images. As a result, the identification accuracy of the proposed CBAM-ECA-ResNet50 is observed to be the highest (99.62%). In addition, the robustness and complexity of the proposed CBAM-ECA-ResNet50 are satisfactory.

Visualized experimental study on pressure drop oscillations of a natural circulation system under low pressure condition

By using deionized water, a visualized experimental study on flow instability phenomenon has been performed in the natural circulation system under condition of low pressure 0.14–0.25 MPa and inlet temperature 45–65 °C.The two-phase flow pattern is recorded by high speed camera and the types of flow instability under different heating power are identified and studied. Besides, the influence of heating power, inlet pressure and temperature on the pressure drop oscillations (PDO) is researched. The experimental results in this paper show that the flow has experienced three stages which are single-phase, sub-cooled boiling two-phase and unstable saturated boiling two-phase, with the increase of heating power. For unstable two-phase flow, both density wave oscillations (DWO) and pressure drop oscillations (PDO) with superimposed DWO are observed. Meanwhile, it is found that the mass flow rate oscillation amplitude of PDO increases while the period of that decreases when increasing the heating power. The parameters analysis results show that the critical heating power of PDO increases with system pressure and decreases with inlet temperature of test section. The experimental results can help to understand the flow instability characteristics and improve the flow stability in the natural circulation loop under low pressure condition.

Experimental study on gas–liquid flow patterns and bubble size in a high-speed rotating impeller of a three-stage centrifugal pump

Head degradation and performance failure will occur when centrifugal pumps work under high gas volume fraction. The gas–liquid two-phase flow patterns and bubble size in a rotating impeller were experimentally studied by high-speed photography technology in a three-stage centrifugal pump. A new impeller was designed with double rings and a transparent shroud, overcoming the speed limitation below 1500 rpm in current studies and reaching the maximum speed of 3500 rpm. Four gas–liquid flow patterns are observed, which are discrete bubble flow, bubble flow, gas pocket flow and stratified flow. The result shows that the transition from bubble flow to gas pocket flow is the critical condition for the severe head degradation. Reducing inlet gas volume fraction, increasing rotational speed and reducing liquid flow rate lower than design value cause the transition from gas pocket flow to bubble flow and the delay of head degradation. By image processing technique, the probability density function (PDF) of bubble diameter in impeller is found to follow a lognormal distribution. Moreover, bubble Sauter diameter dSa has a linear relationship with inlet gas volume fraction λ, rotational speed n−0.75 and liquid flow rate Qw−0.6, respectively. A new correlation is established for predicting bubble Sauter diameter in impeller under high rotational speeds (1500–3500 rpm). The strong relationship between flow pattern transition and head degradation is revealed. Based on the same head performances of three booster stages in pump, the conclusions driven from the visual impeller in the first stage can also be extended to other stages.

Condensation heat transfer coefficient during steam intermittent flows in a horizontal minichannel

Contents of the paper refer to experimental investigations and subsequent prediction of the heat transfer coefficient for condensation of steam flowing in the 2.13 mm i.d. horizontal smooth circular minichannel. Experimental data obtained cover comprehensive span of two-phase flow conditions such that total mass flux is in the range of 178 kg/(m2·s) ≤ G ≤ 885 kg/(m2·s), steam quality varies within 0.003 ≤ x ≤ 0.118 and saturation temperature is ranging between 120.2 °C ≤ Ts ≤ 151.8 °C. The flow patterns were observed visually and images of interest were recorded and subsequently identified based on a map from the literature. There are three basic two-phase flow patterns studied such as slug, slug-annular and churn occurring in the tested minichannel during intermittent flow. Measurements of the local peripherally averaged condensation heat transfer coefficient for each of the patterns considered were carried out using of a heat flux sensor.Based entirely on empirical results achieved the correlation equations specific to the patterns studied have been developed in terms of pertinent dimensionless numbers predicting values of the condensation heat transfer coefficient measured at deviation of ±15%. The correlations show that the condensation process studied combines contributions by gravity induced falling-film condensation with superimposed influence of the liquid phase axial flow, steam quality and interactions by capillarity and surface tension forces.Validity of the correlations proposed is presented in terms of variability ranges of the dimensionless numbers employed specific for each of the pattern studied such as slug, slug-annular and churn. The data predicted on the correlations developed are then compared against those computed on well recognized contributed relationships referenced to steam in flow condensation found in the literature and discrepancies between the predicted and contributed results are displayed and discussed. The comparison reveals some cases for which the results predicted on the correlations proposed can differ substantially from those computed on existing contributions particularly when referenced to intermittent flows at slug and churn patterns.

Theoretical and experimental research on two-phase flow image reconstruction and flow pattern recognition.

Two-phase flow is a kind of complex fluid flow state, and the flow pattern characteristics are very difficult to obtain accurately. First, the principle of two-phase flow pattern image reconstruction based on electrical resistance tomography technology and the complex flow pattern recognition method are developed. Next, the back propagation (BP), wavelet, and radial basis function (RBF) neural networks are applied to the two-phase flow pattern image identification process. The results show that the RBF neural network algorithm has higher fidelity and faster convergence speed than the BP and wavelet network algorithms, and the fidelity is more than 80%. Then, deep learning of the pattern recognition algorithm fusing the RBF network and convolution neural network is proposed to improve the precision of the flow pattern identification. Additionally, the recognition accuracy of the fusion recognition algorithm is more than 97%. Finally, a two-phase flow test system is constructed, the test is finished, and the correctness of the theoretical simulation model is verified. The research process and results provide important theoretical guidance for the accurate acquisition of two-phase flow patterns.

Dynamic balance of the mesoscale repulsion and attraction with varying surface tension and interaction coefficients on two-phase flow patterns simulated by an improved non-dimensional lattice Boltzmann model

An improved mesoscale two-phase flow non-dimensional lattice Boltzmann model (NDLBM) is developed for simulating dynamic balance effects of varying surface tension and interaction coefficients. The mesoscale surface tension coefficient is related to the ratio of surface tension strength to the product of the mesoscopic length scale and the equilibrium particle group size. Relative attraction and repulsion strengths among fluid particles of both different and same phases are modeled by positive and negative interaction coefficients, respectively. Both macroscopic and mesoscopic governing parameters are expressed with physical properties for each coefficient. Three basic types (liquid crystal, viscous fluid, and chain rod vorticity patterns) and two combined types (lattice viscous and stretched viscous vorticity patterns) are observed. Phase volume fraction distributions and vorticity structure patterns are illustrated to show the underlying mechanism of the balance effects of the mesoscale repulsion and attraction. The full map is shown with division curves of different pattern regimes of key mesoscale coefficients, and the division curves quantitatively show how the coefficients affect the transition of flow patterns.

The Recognition Algorithm of Two-Phase Flow Patterns Based on GoogLeNet+5 Coord Attention

The two-phase flow in a microchannel consists of liquid-liquid and gas-liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of flow patterns in the two-phase flow images. The different convolutional kernels in the GoogLeNet algorithm extracted the image features with different scales. In order to strengthen the important channel and spatial features, this paper proposes the combined five-layer Coord attention and GoogLeNet algorithm to enhance the accuracy of the new algorithm. The optimized algorithm model was derived from image datasets with different liquid-liquid two-phase flows (NaAlg-Oil, GaInSn-Water), and its accuracy was 95.09% in training and 98.12% in testing. This new model was also applied to predict the flow patterns, with a recognition accuracy of more than 97% in both the liquid-liquid and gas-liquid two-phase flows (water-soybean oil, water-lubricating oil, and argon-water).

Gas/Liquid Two-Phase Flow Pattern Identification Method Using Gramian Angular Field and Densely Connected Network

Accurate identification of flow patterns is of great importance for industrial production. A flow pattern identification method based on Gramian angular field (GAF) and densely connected network (DenseNet) was proposed. Experimental data of gas–liquid two-phase flow in a vertical upward pipeline were collected using a resistance sensor array, and the multivariate measurement data were downscaled into univariate time series for feature reduction. Then, the data were encoded into 2-D images using the Gramian angular summation field (GASF) and Gramian angular difference field (GADF) to highlight the characteristic differences between flow patterns, and the evolutionary behavior of flow patterns was further analyzed based on the images. A DenseNet model was established, and the GAF images were used as model inputs for training and testing to achieve the flow pattern identification. The results show that the GAF images can effectively reflect the characteristics of different flow patterns; in particular, the combination of GADF and DenseNet has the best recognition effect, and the average flow pattern identification accuracy reaches 98.3%. This method provides a new perspective for distinguishing complex gas–liquid two-phase flow patterns.

Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine.

In this work, a visualization experiment for rectangular channels was carried out to explore gas-liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time-frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas-liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems.

Analysis of dynamic acoustic resonance effects in a sonicated gas–liquid flow microreactor

In this work, we characterize acoustic resonance phenomena occurring between gas bubbles in a segmented gas-liquid flow in a microchannel irradiated with a frequency around 500kHz. A large acoustic amplitude can be reached, leading to gas-liquid interface deformation, atomization of micrometer sized droplets, and cavitation. A numerical approach combining an acoustic frequency-domain solver and a Lagrangian Surface-Evolver solver is introduced to predict the acoustic deformation of gas-liquid interfaces and the dynamic acoustic magnitude. The numerical approach and its assumptions were validated with experiments, for which a good agreement was observed. Therefore, this numerical approach allows to provide a description and an understanding of the acoustic nature of these phenomena. The acoustic pressure magnitude can reach hundreds of kPa to tens of MPa, and these values are consistent with the observation of atomization and cavitation in the experiments. Furthermore, volume of fluid simulations were performed to predict the atomization threshold, which was then related to acoustic resonance. It is found that dynamic acoustic resonance gives rise to atomization bursts at the gas bubble surface. The presented approach can be applied to more complex acoustic fields involving more complex channel geometries, vibration patterns, or two-phase flow patterns.

Numerical simulation of two-phase flow in gas diffusion layer and gas channel of proton exchange membrane fuel cells

Liquid water within the cathode Gas Diffusion Layer (GDL) and Gas Channel (GC) of Proton Exchange Membrane Fuel Cells (PEMFCs) is strongly coupled to gas transport properties, thereby affecting the electrochemical conversion rates. In this study, the GDL and GC regions are utilized as the simulation domain, which differs from previous studies that only focused on any one of them. A Volume of Fluid (VOF) method is adopted to numerically investigate the two-phase flow (gas and liquid) behavior, e.g., water transport pattern evolution, water coverage ratio as well as local and total water saturation. To obtain GDL geometries, an in-house geometry-based method is developed for GDL reconstruction. Furthermore, to study the effect of GDL carbon fiber diameter, the same procedure is used to reconstruct three GDL structures by varying the carbon fiber diameter but keeping the porosity and geometric dimensions constant. The wall wettability is introduced with static contact angles at carbon fiber surfaces and channel walls. The results show that the GDL fiber microstructure has a significant impact on the two-phase flow patterns in the cathode field. Different stages of two-phase flow pattern evolution in both cathode domains are observed. The liquid water in the GDL experiences water invasion, spreading, and rising, followed by the droplet breakthrough in the GDL/GC interface. In the GC, the water droplets randomly experience accumulation, combination, attachment, and detachment. Due to the difference in surface wettability, the water coverage of the GDL/GC interface is smaller than that of the channel side and top walls. It is also found that the water saturation inside the GDL stabilizes after the water breakthrough, while local water saturation at the interface keeps irregular oscillations. Last but not the least, a water saturation balance requirement between the GDL and GC is observed. In terms of varying fiber diameter, a larger fiber diameter would result in less water saturation in the GDL but more water in the GC, in addition to faster water movement throughout the total domain.

Liqnet: A real-time monitoring network for two-phase flow patterns

Real-time identification of gas-liquid two-phase flow can help fluid systems maintain safe operating conditions. A flow pattern identification method based on a convolutional neural network (CNN) algorithm (after this referred to as liqnet) is proposed in this paper to realize automatic detection and real-time identification of two-phase flow patterns. This paper mainly focuses on solving two problems of CNN algorithm flow pattern identification (1): the experimental samples for two-phase flow classification are few, and (2): the existing methods do not fully consider the real-time nature of two-phase flow identification. Therefore, this paper constructs a two-phase flow database containing 6242 images using data enhancement, proposes a lightweight network liqnet, and compares it with six mainstream CNN models. The results show that liqnet can achieve the highest accuracy (98.65%), has the least amount of parameters (1.3708 M), and can achieve the purpose of real-time prediction (32.11FPS).

Calculating densities and viscosities of natural gas with a high content of C2+ to predict two-phase liquid-gas flow pattern

The paper is devoted to the two-phase flow simulation. The gas-condensate mixture flow in a horizontal pipe under high pressure is considered. The influence of the equation of state (EOS) choice for mixture properties modelling on the flow regime calculation results is studied for gas with high content of methane homologues. An analytical overview of the methods to predict the flow pattern is provided. Based on this analysis, two techniques are selected. For these techniques, values of density and viscosity for each phase are required. Density calculation for the gas phase is performed with Van der Waals based EOS. The propriate EOS is selected based on studies of calculation errors for test mixtures. Calculation of liquid phase density is done by means of Patela-Teja and Guo-Du equations, two different models are considered for viscosity estimation. The flow patterns of gas-condensate mixture in a range of temperatures and pressures are calculated and verified via probability map. The results of study allow to recommend the Brusilovsky EOS for calculation of densities for similar gas mixtures and make more rigorous flow regime evaluation. The probability map shows that for the chosen composition and parameters of media the flow pattern is mostly transitional between segregated and annular independent from EOS.

Symbolic transition network for characterizing the dynamics behaviors of gas–liquid​ two-phase flow patterns

Gas–liquid two-phase flows are widely encountered in many industrial applications, and the evolutionary dynamics of the flow patterns are fundamental knowledge for the establishment of the two-phase controlling system. In this work, we propose a symbolic complex network-based method for identifying the evolutionary dynamics of the gas–liquid​ two-phase flow system. First, we establish a series of symbolic networks with the derived gas–liquid two-phase flow symbolic series. Then we suggest a symbolic network index, which is the number of enumerated elementary cycles, to reveal the periodic oscillation properties of various gas–liquid flow patterns. In addition, we estimate the symbolic network average degree C and average shortest path length L to reveal the evolutionary dynamics of the flow patterns. The results suggest that our developed method is efficient for detecting the dynamics of the two-phase flow system, which can be further applied to other complex fluid systems.

Experimental study on flow patterns in narrow rectangular channels

Plate fuel elements are commonly used in compact nuclear reactors due to the advantages of high fuel consumption. In this paper, the two-phase flow pattern in the coolant channel of plate fuel elements, i.e. the rectangular narrow channel was studied. Visualization flow pattern experiments of upward steam-water two-phase flow in narrow rectangular channels, 745 mm long and 67 mm wide with gap widths of 1.3 mm and 1.9 mm, were investigated. The experiments were performed in the conditions of pressure ranging from 1 to 2 MPa, mass flow rate ranging from 200 to 1500kg/(m2⋅s), inlet subcooling ranging from 20–100 °C and tilt angle ranging from 0 to 45°. Three flow patterns, consisting of bubble flow, churn flow and annular flow, were obtained in a wide range of thermal parameters. Flow patterns maps were plotted and the influences of pressure, mass flow rate, inlet subcooling, channel gap and tilt angle on the flow pattern transformation of narrow rectangular channel were studied. This study is of great significance for investigating the subsequent heat transfer process of two-phase flow in compact nuclear reactors.