Vertical 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.

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.

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.

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.

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.

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.

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.

Gas–liquid two-phase flow structure in the multi-scale weighted complexity entropy causality plane

The multi-scale weighted complexity entropy causality plane (MS-WCECP) is proposed for characterizing the physical structure of complex system. Firstly we use the method to investigate typical nonlinear time series. Compared with the multi-scale complexity entropy causality plane (MS-CECP), the MS-WCECP can not only uncover the dynamic information loss of complex system with the increase of scale, but also can characterize the complexity of nonlinear dynamic system. In particular, the algorithm of MS-WCECP performs strong anti-noise ability. Then we calculate the MS-WCECP for the conductance fluctuating signals measured from vertical upward gas–liquid two-phase flow experiments in a small diameter pipe, the results demonstrate that the MS-WCECP is a useful approach for exploring the stability and complexity in gas–liquid two-phase flows.

Analysis of dynamic of two-phase flow in small channel based on phase space reconstruction combined with data reduction sub-frequency band wavelet

A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems (Lorenz and Rössler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2mm×0.81mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns (bubbly flow: 100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.

Flow pattern map and time–frequency spectrum characteristics of nitrogen–water two-phase flow in small vertical upward noncircular channels

We experimentally investigate the vertical upward gas–liquid two-phase flow in a 2×0.81mm small rectangular channel and an equilateral triangle channel with a 2mm side length. The two channels have same hydraulic diameters (Dh=1.15mm). We first present an experimental flow pattern map with nitrogen and water superficial velocities ranging from 0.08m/s to 11.82m/s and 0.12m/s to 1.52m/s, respectively. We also compare the flow pattern transition criteria between the triangle and rectangular cross sections by using the same hydraulic radius. We employ the influence rule that small passage geometry limits the flow pattern transition criteria. Thereafter, we comparatively analyze the transition boundaries of experiment flow patterns with other results in literature and classical prediction models. Results show that the cross-sectional shapes and experimental conditions of the experimental pipeline significantly affect the changes in the flow regime. Given the differential pressure signal of the two-phase flow, we propose two effective quadric time–frequency representations, namely, the adaptive optimal kernel time–frequency representation (AOK TFR) and data reduction sub-frequency band wavelet (DA SFBW) to investigate the complex behavior of vertical upward gas–liquid flows. We extract the positive power spectral density of the singular value–frequency entropy, singular value–frequency entropy, damping ratio, and vibration mode to characterize the evolution of flow patterns. The results suggest that the AOK TFR method can reveal flow dynamic details, whereas the DA SFBW based method is a powerful tool for characterizing the dynamical characteristics of different vertical upward gas–liquid flow patterns.

Effect of Void Fraction and Two-Phase Dynamic Viscosity Models on Prediction of Hydrostatic and Frictional Pressure Drop in Vertical Upward Gas–Liquid Two-Phase Flow

In gas–liquid two-phase flow, the prediction of two-phase density and hence the hydrostatic pressure drop relies on the void fraction and is sensitive to the error in prediction of void fraction. The objectives of this study are to analyze dependence of two-phase density on void fraction and to examine slip ratio and drift flux model-based correlations for their performance in prediction of void fraction and two-phase densities for the two extremes of two-phase flow conditions, that is, bubbly and annular flow or, alternatively, the low and high region of the void fraction. It is shown that the drift flux model-based correlations perform better than the slip ratio model-based correlations in prediction of void fraction and hence the two-phase mixture density. Another objective of this study is to verify performance of different two-phase dynamic viscosity models in prediction of two-phase frictional pressure drop. Fourteen two-phase dynamic viscosity models are assessed for their performance against 616 data points consisting of 10 different pipe diameters in annular flow regime. It is found that none of these two-phase dynamic viscosity models are able to predict the frictional pressure drop in annular flow regime for a range of pipe diameters. The correlations that are successful for small pipe diameters fail for large pipe diameters and vice versa.

CHARACTERIZATION OF HORIZONTAL GAS–LIQUID TWO-PHASE FLOW USING MARKOV MODEL-BASED COMPLEX NETWORK

Horizontal gas–liquid two-phase flow widely exists in many physical systems and chemical engineering processes. Compared with vertical upward gas–liquid two-phase flow, investigations on dynamic behavior underlying horizontal gas–liquid flows are quite limited. Complex network provides a powerful framework for time series analysis of complex dynamical systems. We use a network generation method based on Markov transition probability to infer directed weighted complex networks from signals measured from horizontal gas–liquid two-phase flow experiment and find that the networks corresponding to different flow patterns exhibit different network structure. To investigate the dynamic characteristics of horizontal gas–liquid flows, we construct a number of complex networks under different flow conditions, and explore the network indices for each constructed network. In addition, we investigate the sample entropy of different flow patterns. Our results suggest that the network statistic can well represent the complexity in the transition among different flow patterns and further allows characterizing the interface fluctuation behavior in horizontal gas–liquid two-phase flow.

Analysis of total energy and time-frequency entropy of gas–liquid two-phase flow pattern

In this paper, we first make a simulation investigation to evaluate three time-frequency representation algorithms, i.e., Wigner–Ville distribution, Choi–Williams distribution and adaptive optimal kernel time-frequency representation (AOK TFR). The results indicate that AOK TFR can suppress the cross-term while keeping a high time-frequency concentration. In this regard, we use AOK TFR to analyze the fluctuating signals measured from vertical upward gas–liquid two-phase flow experiments. Moreover, we extract total energy and time-frequency entropy from the AOK TFR to characterize dynamic behaviors underlying different flow patterns. We find the total energy and entropy are sensitive to the flow pattern evolutions and the joint distribution of total energy and entropy can be a powerful tool for not only distinguishing different flow patterns but also revealing complex dynamic behaviors underlying gas–liquid flow patterns.

界面活性剤添加垂直上昇気液二相流における流動特性 -第1報，圧力損失低減 -

界面活性剤添加垂直上昇気液二相流における流動特性 -第1報，圧力損失低減 -

界面活性剤添加垂直上昇気液二相流における流動特性 -第2報，気液界面構造 -

界面活性剤添加垂直上昇気液二相流における流動特性 -第2報，気液界面構造 -

Mass flow measurement of gas–liquid bubble flow with the combined use of a Venturi tube and a vortex flowmeter

Development of effective techniques for gas–liquid two-phase flow measurement is of interest to both academic research and industrial applications. This paper presents a novel approach to the measurement of the mass flow rate of homogeneous gas–liquid bubble flow with the combined use of a Venturi tube and a vortex flowmeter. The Venturi tube and the vortex flowmeter were mounted in the same pipeline with a spacing interval of ten times the pipe's inner diameter. A measurement correlation was established based on the differential pressure generated across the Venturi tube and the frequency extracted from the vortex flowmeter signal. Experiments were conducted on a vertical upward gas–liquid two-phase flow rig under the bubble flow pattern, with the air mass flow rate from 0.2 × 10−3 to 3.2 × 10−3 kg s−1, the water mass flow rate from 3.3 to 5.2 kg s−1 and the volumetric void fraction from 0.004 to 0.246. The results show that the relative errors of the correlation for the mixture mass flow rate measurement were within ±5%, and the maximum standard deviation of the relative errors was 2.0%. This method provides a simple and practical solution to the mass flow measurement of homogeneous gas–liquid bubble flows.