Upward Gas-liquid Two-phase Flow Research Articles

Performance model evaluation of turbine flow meter in vertical gas-liquid two-phase flows

Aiming at the need for flow measurement of gas-liquid flows in domestic gas well production, this paper proposes a measurement method based on the combination of the turbine flow meter (TFM) and a rotating electric field conductance sensor (REFCS). In experiments, the REFCS is used for the measurement of the gas holdup. To verify the applicability of the TFM models investigated in the previous study, for the modeling part, the mass, momentum and torque models are evaluated in vertical upward gas-liquid two-phase flows. In our model test, the meter factor model of TFM considers the effects of the slip ratio between the gas and liquid phases and flow patterns. In particular, the gas holdup involved in calculating the slip ratio in the model evaluation is obtained from the REFCS measurements. Model test results show the torque model has better volumetric flow rate prediction accuracy than the mass and momentum models. In the present study, the ranges of the liquid and gas phases are Qw = 2–30 m3/d and Qg = 1–16 m3/d, it was found that the average absolute deviation (AAD) in the predicted volume flow rate is equal to 1.23 m3/d and the average absolute percentage deviation (AAPD) is equal to 7.69%. The evaluated results presented in this paper will allow better estimates of the volumetric flow rates of gas-liquid flows based on the combined TMF and REFCS measurements during the monitoring of gas well production.

Co-Attention-Based Cross-Stitch Network for Parameter Prediction of Two-Phase Flow

Gas-liquid two-phase flow is pervasive in industrial processes, and accurate measurement of its parameters can effectively improve industrial efficiency. Void fraction, a key parameter in gas-liquid two-phase flow, plays a crucial role in mixture density determination and flow structure analysis. In this paper, a novel multi-task learning method is proposed to predict the gas void fraction. Firstly, we conduct vertical upward gas-liquid two-phase flow experiments to obtain fluid signals relying on the four-sector distributed conductance sensor. Then, we pair the measurement of gas void fraction and its classification and design a Co-Attention based Cross-Stitch Network (CACSNet) to process these two closely related tasks simultaneously. In CACSNet, two parallel embedding branches are adopted to extract task-specific features. There are three feature extractors in each branch to capture in-depth representations of temporal and spatial. To learn an optimal combination of these task-specific features and generate shared representations, we propose the Cross-stitch Embedding with Attention Modules (CEAM), which is incorporated the Channel Co-Attention Module (CCAM) and the Temporal Co-Attention Module (TCAM). Finally, we compare our CACSNet with single-task baseline and other competitive methods, which proves that our framework achieves superior performance in gas void fraction prediction. We also conduct detailed model ablation and parameter analysis to illustrate the efficiency of our proposed structure.

The void fraction and frictional pressure drop of upward two-phase flow under high pressure brine condition

The offshore methane hydrate production test conducted in Japan has demonstrated a successful production of methane gas from marine gas hydrates using depressurization methods. The method produces methane gas by dissociating methane hydrate, which is stable at low temperature and high pressure conditions, by lowering the bottom-hole pressure in the wellbore. Under this method, stable control of bottom-hole pressure is necessary to ensure continuous methane gas production. Therefore, understanding the behavior of upward gas–liquid two-phase flow, involving gas-brine mixture, in a wellbore is essential for the proper pressure drop evaluation in the production line. In the present study, void fraction and frictional pressure drop behaviors under nitrogen-brine two-phase flow conditions is investigated using the high-pressure test facility of 52.7 mm inner diameter and 28 m height. The measured void fraction was assessed using drift-flux model, and the results showed that the decrease in bubble diameter under bubbly flow in gas-brine two-phase flow conditions resulted in decrease in bubble rise velocity and uniformity of bubble distribution. It was also found that, under bubbly flow in gas-brine mixture, the frictional pressure drop tends to further decrease with elevation. Based on these findings and obtained experimental dataset, the new frictional pressure model for gas-brine two-phase flow under high-pressure conditions is developed in the present study.

Identification of high-pressure two-phase flow regime transition using image processing and deep learning

The commercial production process of methane gas from methane hydrate has been developed by the MH21-S R&D consortium (MH21-S) in recent years. In order to establish the gas production method, an accurate gas-liquid two-phase flow regime identification under high-pressure conditions is indispensable. In the present study, a 28 m long test section with an inner diameter of 52.7 mm was newly constructed, and a nitrogen-water two-phase flow experiment was conducted at 3.0 MPa G. Two-phase flow images were successfully acquired using the high-speed camera from the visualization port. The sequential image of upward gas-liquid two-phase flow was merged into a single image by the time strip method. Then, flow regime identification using the convolutional neural network (CNN) trained under sequential images was carried out. The proposed method has shown an excellent capability to identify flow regime images obtained at the current high-pressure conditions. In addition, for the slug flow regime, it was found that the Taylor bubble length highly influences the identification performance, and the model proposed by Street and Tek showed good agreement for the present dataset. Furthermore, the bubbly-slug transition region was successfully evaluated and quantified through the present model. The current methods have shown a good correlation with the existing model. These results indicate that the gas void fraction at the transition region to be larger than the theoretical value for the elevated pressure system.

Flow Pattern and Transformation Mechanism of Upward Gas-Liquid Two-Phase Flow in Vertical Tubes Under Undulating Vibration

Flow Pattern and Transformation Mechanism of Upward Gas-Liquid Two-Phase Flow in Vertical Tubes Under Undulating Vibration

Multitask-Based Temporal-Channelwise CNN for Parameter Prediction of Two-Phase Flows

Gas-liquid two-phase flow is of great importance in various industrial processes. How to accurately measure the flow parameters in the gas-liquid two-phase flow remains a challenging problem. In this article, we develop a novel deep learning based soft measure technique to predict the gas void fraction, which is one key parameter in a gas-liquid two-phase flow. We conduct the vertical upward gas-liquid two-phase flow experiments to measure the flow signals by using the four-sector distributed conductance sensor. Then, we design a novel multitask-based temporal-channelwise convolutional neural network (MTCCNN) to predict the gas void fraction. In MTCCNN, we first utilize the decomposed convolutional block to extract temporal dependence and channel connection from fluid data. After further fusion by the dense layer, we apply multitask learning to make full use of the extracted features through both classification branch and gas void fraction prediction branch. We compare our MTCCNN with its variations to demonstrate the proposed improvements. We also present other competitive methods for comparisons, which shows that our MTCCNN presents a better performance in gas void fraction prediction.

Stage-Wise Densely Connected Network for Parameter Measurement of Two-Phase Flows

Gas-liquid two-phase flow is attracting increasing attention in industrial areas. Precise measurement of total rate and void fraction, key parameters in two-phase flows, can significantly improve the industrial efficiency, which is still a challenging issue due to their complex characteristics. To cope with this problem, we design a Stage-wise Densely Connected Network (SDCN) to simultaneously measure the total rate and void fraction. First, we conduct series of vertical upward gas-liquid two-phase flow experiments to collect signals from the four-sector conductance sensor. Then, we combine the stage-wise strategy, dense connections and decomposed convolution kernels to construct the SDCN. Using the stage-wise strategy, we introduce the flow structures to improve the accuracy of the measurement. During this process, dense connections between blocks and decomposed convolution kernels are conducted to better extract the features. Finally, we compare the SDCN and its variations to demonstrate the importance of these improvements. We also show some other competitive methods for parameter measurement. The results suggest that our proposed SDCN could accurately measure both the total rate and void fraction of gas-liquid two-phase flow, which provides a new solution in parameter measurement of two-phase flows.

Flow regime and void fraction predictions in vertical rod bundle flow channels

This paper has studied the flow regimes and their transitions in upward gas-liquid two-phase flows in vertical rod bundle flow channels. The past experimental observations on the flow regimes have been extensively reviewed and analyzed. Various flow regimes with the observer-dependent names are re-classified into 5 flow regimes, namely, bubbly, finely dispersed bubbly, cap-bubbly, churn and annular flows according to their different flow characteristics in the vertical rod bundle flow channels. In view of the problems in the available flow regime transition criteria and the drift-flux correlations, this paper has proposed the mechanisms for the bubbly to cap-bubbly flow transition, the cap-bubbly to churn flow transition, the transition to annular flow and the transition to the finely dispersed bubbly flow and developed the corresponding flow regime transition criteria to predict the five flow regimes based on these mechanisms. This paper has further improved Han et al. correlations for the drift-flux parameters of the distribution parameter and the drift velocity to predict the void fractions in the 5 flow regimes, together the newly-developed transition criteria, in the vertical rod bundle flow channels. The newly-developed flow regime transition criteria and the improved Han et al. correlation for the drift-flux parameters of the distribution parameter and the drift velocity have been examined with the experimental data. The examination has concluded that the newly-developed flow regime transition criteria can correctly predict 82.9% of the 451 observed flow regime data in the vertical rod bundle flow channels and the drift-flux model using the improved Han et al. correlation and the newly-developed flow regime transition criteria can give satisfactory prediction for the 959 experimental data of the void fraction with the mean relative error of 0.0766.

Identification of Upward Two-Phase Flow Regime Transition Region under High-Pressure Condition Using Deep Learning

Currently, the MH21-S R&D consortium (MH21-S), supported by the Ministry of Economy, Trade, and Industry (METI), is developing the commercial production process of methane gas from methane hydrate. In order to develop a gas production method, the objective flow regime identification technique of gas-liquid two-phase flow under high-pressure conditions would be useful. Therefore, in the present research, a new method for identifying the flow regime by a convolutional neural network (CNN) using high-speed images obtained at a narrow view was proposed, and its performance was evaluated. Specifically, the upward gas-liquid two-phase flow images were recorded using a high-speed camera, and they were merged into a single image containing information from entire frames. The CNN was created and trained with these newly merged images, and it was utilized to identify flow regimes of unlabeled test images. As a result, the proposed method using the CNN model was capable of identifying the flow regime with high accuracy. Thus, the present study has shown that the CNN model with the proposed image conversion method can properly identify the upward gas-liquid two-phase flow regime. In addition, by utilizing the developed network, the bubbly-slug transition region, which has only been studied through visual observation or void fraction signals in the past, was successfully evaluated quantitatively.

Multilayer limited penetrable visibility graph for characterizing the gas-liquid flow behavior

The research of the dynamical flow behavior of gas-liquid two-phase flow remains a challenge of great importance. We first carry out the vertical upward gas-liquid two-phase flow experiment in a 50 mm inner-diameter pipe and capture flow information via four-sector distributed conductance sensor. Then, we develop multilayer limited penetrable visibility graph (MLPVG) to analyze multichannel measurements, aiming to characterize the complicated flow behavior. We calculate two projection networks of MLPVG from two different perspectives (i.e. the overlapping information and the whole information). The ratio between the global clustering coefficients of these two projection networks is calculated as a quantitative indicator to characterize the multilayer network. We find that the proposed multilayer network measure can effectively characterize the evolution of gas-liquid flow, from randomly dispersed bubbles to periodically arisen gas slugs. The results suggest that our method can effectively fuse multichannel measurements and reveal the evolution of the complicated flow behavior.

Drift-flux correlation for upward gas-liquid two-phase flow in vertical rod bundle flow channel

This paper has reviewed 17 available drift-flux correlations and collected 959 existing void fraction experimental data for air-water and steam-water two-phase flows in the vertical rod bundle flow channels. The performances of these drift-flux correlations have been evaluated with the collected experimental data. The evaluations conclude that the recently-developed drift-flux correlation of Schlegel and Hibiki gives the best predictions, and the drift-flux correlations of Clark et al. and Ye et al. can provide reasonable predictions for the collected experimental data. However, these drift-flux correlation series of Clark et al., Ye et al. and Schlegel and Hibiki are found to have the following three shortcomings. (1) Their distribution parameter correlation has ignored the flow regime and bubble behavior effects due to the different combinations of the non-dimensional superficial gas and liquid velocities at the same non-dimensional mixture volumetric flux under the high non-dimensional mixture volumetric flux conditions. (2) Their distribution parameter correlation has used the non-dimensional superficial gas and liquid velocities which cannot effectively reflect the pressure effect on the distribution parameter. (3) Their drift velocity correlation has ignored the drift velocity difference due to the flow regime transitions and has used the bubbly flow drift velocity correlation for all flow regimes, including the cap-bubbly, churn and annular flows. In view of these shortcomings, the present drift-flux correlation development adopts the segmented drift velocity correlations considering the features of bubbly, cap-bubbly, churn and annular flows in vertical rod bundle flow channel. The analysis for the experimental data of the asymptotic distribution parameter has shown that the asymptotic distribution parameter depends on the gas-phase Reynolds number with a feature that it linearly increases with gas-phase Reynolds number in the low gas-phase Reynolds number region, peaks at a critical gas-phase Reynolds number and exponentially decreases in the high gas-phase Reynolds number region under a certain liquid-phase Reynolds number flow conditions. This phenomenon is found to be caused by the formation and breakup processes of large-cap bubbles and their resultant intensive secondary flows corresponding to the flow regime transitions in the vertical rod bundle flow channel. So, a new distribution parameter correlation has been developed based on the gas- and liquid-phase Reynolds numbers reflecting the ratio of each phase inertial force to viscous force for the two-phase flows in vertical rod bundle flow channel. The newly-developed drift-flux correlation has been checked against various experimental data of air-water and steam-water two-phase flows in the rod bundle flow channel and a good agreement has been reached.

A Deep Branch-Aggregation Network for Recognition of Gas–Liquid Two-Phase Flow Structure

Gas–liquid two-phase flow widely exists in petroleum, natural gas, and other industries. Recognition of flow structure is an important issue in the study of two-phase flow, and it is of great significance for the optimization of industrial processes. Therefore, how to recognize complex flow structures effectively represents a challenge. To cope with this problem, we develop a novel deep learning network to recognize flow structures under different flow conditions. In particular, we conduct vertical upward gas–liquid two-phase flow experiments to obtain the flow structure data set on the basis of images collected by a high-speed camera. Then, we design a branch-aggregation network (BAN), where the branch structure is utilized to increase the width of the network, and multilevel aggregation structure is used to fuse features of different levels. In recognition of flow structure, the proposed network achieves an accuracy of 99.60% with a fast convergence speed and shows the advantages in antinoise ability, which is significant for online recognition in industrial processes. The results indicate that BAN can be a feasible method to recognize complex flow structures.

Characterization of Two-Phase Flow Structure by Deep Learning-Based Super Resolution

Two-phase flow exists widely in the industrial field, and its research is of great significance for industrial production. In this brief, we design a wire-mesh sensor system with 256 measurement points and conduct vertical upward gas-liquid two-phase flow experiments to capture the flow information under different flow conditions. We find that flow structure can be characterized by the gas-liquid distribution imaging obtained through the wire-mesh sensor. To obtain more detailed imaging, we apply super-resolution methods based on deep learning to improve the imaging quality and choose No-Reference Structural Sharpness to evaluate the effects. The results show that compared with traditional interpolation method, the deep learning-based super resolution can restore more abundant details such as edges and textures, thereby characterizing the flow structure more finely. This provides an important support for further analysis of the flow characteristics and flow regularity, which can effectively optimize the industrial process.

Effect of cross-sectional shape on void fraction characteristics of vertically upward gas-liquid two-phase flows in mini-channels

Effect of cross-sectional shape on void fraction characteristics of vertically upward gas-liquid two-phase flows in mini-channels

Detecting the Flow Pattern Transition in the Gas-Liquid Two-Phase Flow Using Multivariate Multi-Scale Entropy Analysis

Abstract Due to the complex flow structure and non-uniform phase distribution in the vertical upward gas-liquid two-phase flow, an eight-electrode rotating electric field conductance sensor is used to obtain multi-channel conductance signals. The flow patterns of the vertical upward gas-liquid two-phase flow are classified according to the images obtained from a high-speed camera. Then, we employ the multivariate weighted multi-scale permutation entropy (MWMPE) to detect the instability of flow pattern transition in the gas-liquid two-phase flow. Afterwards, we compare the results of the MWMPE with those of the single-channel weighted multi-scale permutation entropy (SCWMPE) and multivariate multi-scale sample entropy (MMSE). The comparison results indicate that, compared with the SCWMPE and MMSE, the MWMPE has superior performance in terms of the high-resolution presentation of flow instability in the gas-liquid two-phase flow. Finally, we extract the mean value of the MWMPE in whole scales and the entropy rate of the MWMPE in the small scales. The results indicate that the normalized mean value and normalized entropy rate of MWMPE are very sensitive to the transitions of flow patterns, thus allowing the detection of the instability of flow pattern transition.

Upward gas–liquid two-phase flow after a U-bend in a large-diameter serpentine pipe

We present an experimental study on the flow behaviour of gas and liquid in the upward section of a vertical pipe system with an internal diameter of 101.6mm and a serpentine geometry. The experimental matrix consists of superficial gas and liquid velocities in ranges of 0.15–30m/s and from 0.07 to 1.5m/s, respectively, which cover bubbly to annular flow. The effects on the flow behaviours downstream of the 180° return bend are significantly reduced when the flow reaches an axial distance of 47 pipe diameters from the U-bend. Therefore, reasonably developed flow is attained at this development length downstream of the bend. Other published measurements for large-diameter film thickness show similar trends with respect to the superficial gas velocity. However, the trends differ from those of small-diameter pipes, with which the film thickness decreases much faster with increasing gas flow. As a result, only a few of the published correlations for small pipe data agreed with the experimental data for large pipe film thickness. We therefore modified one of the best-performing correlations, which produced a better fit. Qualitative and statistical analyses show that the new correlation provides improved predictions for two-phase flow film thickness in large-diameter pipes.

Multi-Scale Morphological Analysis of Conductance Signals in Vertical Upward Gas–Liquid Two-Phase Flow

Abstract The multi-scale analysis is an important method for detecting nonlinear systems. In this study, we carry out experiments and measure the fluctuation signals from a rotating electric field conductance sensor with eight electrodes. We first use a recurrence plot to recognise flow patterns in vertical upward gas–liquid two-phase pipe flow from measured signals. Then we apply a multi-scale morphological analysis based on the first-order difference scatter plot to investigate the signals captured from the vertical upward gas–liquid two-phase flow loop test. We find that the invariant scaling exponent extracted from the multi-scale first-order difference scatter plot with the bisector of the second-fourth quadrant as the reference line is sensitive to the inhomogeneous distribution characteristics of the flow structure, and the variation trend of the exponent is helpful to understand the process of breakup and coalescence of the gas phase. In addition, we explore the dynamic mechanism influencing the inhomogeneous distribution of the gas phase in terms of adaptive optimal kernel time–frequency representation. The research indicates that the system energy is a factor influencing the distribution of the gas phase and the multi-scale morphological analysis based on the first-order difference scatter plot is an effective method for indicating the inhomogeneous distribution of the gas phase in gas–liquid two-phase flow.

Effect of diameter and axial location on upward gas–liquid two-phase flow patterns in intermediate-scale vertical tubes

In the present research, a two-phase flow system is designed, manufactured, assembled and adjusted to study two-phase flow behavior isothermally. Test sections are tubes standing in vertical position and are made of transparent acrylic with inner diameters of 40mm and 70mm. Two axial locations of 1.73m and 3.22m are chosen for data acquisition. Flow pattern maps are presented for both tubes. Effects of tube diameter and axial location on pattern transition boundaries are investigated. Air and water are chosen as working fluids. The range of air and water superficial velocities are 0.054–9.654m/s and 0.015–0.877m/s for the 40mm diameter tube, but these values are 0.038–20.44m/s and 0.036–1.530m/s for 70mm diameter tube. The results show that for both tubes, increasing axial location does not affect flow transition boundaries significantly. However, slug pattern region shrinks considerably by changing tube diameter from 40mm to 70mm. Using image processing techniques, recorded high speed movies were investigated accurately. As a result, bubbly flow in the 40mm tube can be divided into three sub-patterns as dispersed, agitated and agglomerated bubbly. Also, two types of slug pattern are also recognized in the same tube diameter which are called small and large slugs. Semi-annular flow is observed as an independent pattern in the 70mm tube that does not behave as known churn or annular patterns.

The measurement of gas–liquid two-phase flows in a small diameter pipe using a dual-sensor multi-electrode conductance probe

We design a dual-sensor multi-electrode conductance probe to measure the flow parameters of gas–liquid two-phase flows in a vertical pipe with an inner diameter of 20 mm. The designed conductance probe consists of a phase volume fraction sensor (PVFS) and a cross-correlation velocity sensor (CCVS). Through inserting an insulated flow deflector in the central part of the pipe, the gas–liquid two-phase flows are forced to pass through an annual space. The multiple electrodes of the PVFS and the CCVS are flush-mounted on the inside of the pipe wall and the outside of the flow deflector, respectively. The geometry dimension of the PVFS is optimized based on the distribution characteristics of the sensor sensitivity field. In the flow loop test of vertical upward gas–liquid two-phase flows, the output signals from the dual-sensor multi-electrode conductance probe are collected by a data acquisition device from the National Instruments (NI) Corporation. The information transferring characteristics of local flow structures in the annular space are investigated using the transfer entropy theory. Additionally, the kinematic wave velocity is measured based on the drift velocity model to investigate the propagation behavior of the stable kinematic wave in the annular space. Finally, according to the motion characteristics of the gas–liquid two-phase flows, the drift velocity model based on the flow patterns is constructed to measure the individual phase flow rate with higher accuracy.

The measurement of local flow parameters for gas–liquid two-phase bubbly flows using a dual-sensor probe array

Conductivity methods are commonly used in two-phase flow measurement due to their high sensitivity to the conductivity contrast between both phases. In this study, a dual-sensor probe array is newly designed to measure the local parameters of vertical upward gas–liquid two-phase flows in a small inner diameter pipe. Firstly, the measurements of gas volume fraction and gas velocity profiles are conducted using the dual-sensor probe array. Then series of bubble chord lengths at different locations are derived based on the rising velocities of the bubbles, and are compared with a model of maximum bubble diameter. Additionally, based on the simultaneously measured signals from the dual-sensor probe array, the structures of multiple bubble groups are visualized using a technology of phase density image. In view of the existence of the multiple bubble groups, the transformation from bubble chord length distribution to bubble size distribution is conducted based on the decomposition of the multiple bubble groups in the chord length distribution, and the evolution characteristics of the bubble size distributions at the pipe cross section are investigated as the flow condition changing.