Gas-liquid Two-phase Flow Measurement Research Articles

Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.

Soft measurement model for wet gas flow rate based on ultrasonic and differential pressure sensing

Non-separation measurement of gas–liquid two-phase flow is of great significance to industrial production and theoretical research on two-phase flow. In this paper, a soft measurement method of wet gas flow rate based on ultrasonic and differential pressure (DP) sensors is proposed. A total of 129 sets of experimental data are obtained in DN50 horizontal pipe, the superficial velocity of gas and liquid ranging from 5 m s−1 to 33 m s−1 and 0.015 m s−1 to 0.6 m s−1, respectively. Ten feature parameters of ultrasonic signals such as the time difference between two echoes and kurtosis factor are proposed from time and frequency domain. As well as the power spectral density, mean value and standard deviation value are analyzed based on DP signals. Furthermore, the Spearman correlation coefficient is introduced to evaluate these feature parameters quantitatively. And nine parameters which are highly correlated to gas–liquid flow rate are selected as the inputs of the soft measurement model. Finally, Decision Tree, least square support vector machines and artificial neural network (ANN) models are established through training with the experimental data. Comparison results show that ANN has the lowest prediction errors on the test set, with the mean absolute percentage errors of 1.49% and 3.2% for gas and liquid, respectively.

Simulation and experimental investigation on gas-liquid two-phase flow separation behaviors in multitube T-Junction separator

A specially designed separator for gas-liquid two-phase flow separation and measurement is proposed. The flow characteristics and working scope are studied under different gas/liquid superficial velocities and different flow patterns through FLUENT numerical simulation and experimental research. The working scope of the separator is related to both the gas and liquid superficial velocity. The separator work well under the when the gas superficial velocity ranges from 0.65 to 21 m/s, and the liquid superficial velocity ranges from 0.01 to 0.31 m/s. When the actual working condition is beyond this range, the performance is not so outstanding in case of partial slug flow and annular. Under the working range of the separator, the measurement error of gas and liquid mass flow rates is less than ±2.5%. The special structure provides a buffer space for liquid slug, which shows good shock resistance capacity under high liquid superficial velocity. The investigation offers a valuable guidance for multiphase flow rates measurement.

Development of Gas-Liquid Two-Phase Flow Measurement Method Using Multi-View Camera System

Although there exist several methodologies to measure gas-liquid two-phase flows, such as wire-mesh sensors and X-ray CT, a direct measurement of bubble coalescence and breakup is difficult due to their multidimensionality and unsteady nature. However, a comprehensive understanding of gas-liquid two-phase flow under such conditions is essential for predicting the effectiveness of scrubbing, i.e. to remove fission products during severe accidents in nuclear power plants. The objective of this study is to develop a new methodology to directly measure and analyze gas-liquid two-phase flow under high void, churn-turbulent regime in a large space through an initially stagnant pool of water. For this purpose, a new technique to measure two-phase flow using multi-view images has been developed. A total of 29 synced cameras housed in a water-proofed casing were placed inside a test section with an inner diameter of 1.5 m and a height of 4 m, to construct a multi-view camera system. The test section was filled with water up to a height of 2 m, and images were taken as air was supplied from a cylindrical nozzle at the bottom with flow rates of 500 to 2000 L min-1. The cameras were calibrated, and an algorithm was developed to reconstruct the air flow in 3D voxel space. The results suggest that the developed technique, together with data assimilation and other techniques, could be applicable for measuring gas-liquid two-phase flow under churn-turbulent regime in a large space. In this paper, the progress of the development of the multi-view measurement and analysis technique, and the results of preliminary investigations are discussed.

Application of time-domain gating technique in water content measurement of gas-liquid two-phase flow.

This paper introduces an application of the time-domain gating technique in water content measurement of gas-liquid two-phase flow. According to the principle that water and gas have different absorption effects on microwaves, a sensor with transmitting and receiving antennas is designed. Combined with the back propagation neural network, the water content of two-phase fluids can be obtained from the complex transmission coefficient (S21) of antennas. In order to reduce the influence of the sensor structure on antenna measurement, the time-domain gating technique can be used to filter out the interference components in S21 so that the water content can be more easily predicted. Built on the above, an automatic test system is established. In the range of 0%-78%, the absolute test error of the water content is lower than 3%.

A novel heterogeneous ensemble approach to variable selection for gas-liquid two-phase CO2 flow metering

Variable selection is an important preprocessing step in the development of effective data-driven models for CO2 flow measurement in carbon capture and storage systems. In order to effectively quantify the importance of potential input variables to the desired output, ensemble learning is proposed and incorporated into variable selection methodology. This paper presents a tree-based heterogeneous ensemble approach to variable selection and its application to gas-liquid two-phase CO2 flow measurement. The importance of each variable is determined through combining the importance scores from four tree-based algorithms, including decision tree regression, bootstrap aggregating of regression trees, gradient boosting decision tree and gradient boosting random forest. Then the backward elimination algorithm is applied to remove the relatively less important variables and hence a small set of input variables for data-driven models. The selection results demonstrate that the significant variables for CO2 mass flow measurement include apparent mass flow rate, time shift, differential pressure and pressure drop while observed density, density drop, observed flow velocity and outlet temperature for prediction of gas volume fraction. To assess the validity of the selected variables, data-driven models based on gradient boosting random forest are developed. Results suggest that the relative error of the model output is mostly within 1% for CO2 mass flowrate measurement and 5% for gas volume fraction prediction by taking the selected variables as model inputs.

Influence of probe geometry on the characteristics of optical fiber gas-liquid two-phase flow measurement signals.

A conical tip-shaped plastic optical fiber sensor for gas-liquid flow measurement is described. Experimental results show that a distinctive spike signal occurs before the output signal when using a conventional conical fiber probe, whereas this spike signal was greatly suppressed when a cleaved probe is used. A full simulation is implemented based on a three-dimensional ray-tracing method providing a means of comparison with theoretical analysis. The results show that an appropriately cleaved tip provides a promising method of enhancing the bubble measurement signal-to-noise ratio.

Characteristics of horizontal gas-liquid two-phase flow measurement in a medium-sized pipe using gamma densitometry

Two-phase flows are common occurrences in many industrial applications. The understanding of their characteristics in industrial piping systems is vital for the efficient design, optimization, and operation of industrial processes. Most of the previous experimental studies involving the use of gamma densitometers for holdup measurements in air-water mixtures are limited to smaller diameter pipes (generally regarded as those with < 50 mm in nominal diameter). Further, very few literature report experimental data obtained using gamma desitometers. This paper presents an application of a gamma densitometer in the measurement of two-phase flow characteristics in an intermediate diameter pipe (nominal diameter between 50 mm and 100 mm). Scaled air-water experiments were performed in a 17-m long, 0.0764-m internal diameter horizontal pipe. Liquid superficial velocity ranged between 0.1–0.4 m/s while gas superficial velocity ranged from 0.3 to 10.0 m/s. The measured parameters include liquid holdup, pressure gradient, flow pattern, and slug flow features. The flow patterns observed were stratified, stratified-wavy, plug, slug, and annular flows. Plug and slug flow patterns showed good agreement with established flow pattern maps. Furthermore, the slug translational velocity was observed to increase with increasing mixture velocity, as reported by previous authors, hence establishing the reliability of the instrumentation employed. The slug body length was also measured using the gamma densitometer and was found to be within the range 24–36D with a mean length of 30.6D.

Experimental investigation of gas-liquid two-phase splitting in parallel pipelines with risers

To study two-phase flow splitting at a T-junction, Parallel vertical riser pipelines were set up in the laboratory. Each of these pipelines was completely symmetrical. The experiment was conducted with various combinations of gas velocity and liquid velocities. Accurate measurement of gas-liquid two-phase flow were performed to determine whether the two-phase flow was evenly distributed in the two riser pipelines. The conductance probe technique was used to measure the average liquid holdup in the cross section of the pipe. Three flow patterns in two risers were observed during the experiment: slug-slug (S-S), churn-churn (C-C), and churn-slug/churn (C-SC). Gas-liquid flow of S-S and C-SC is asymmetric, while that in C-C is symmetric. When the flow state in the two risers is S-S, the gas-liquid two-phase split could be uneven. When the gas and liquid velocities were sufficiently high for sustaining C-C, the gas and liquid were split evenly. The last state was the transition state between S-S and C-SC. A model based on pressure-minimization was established to predict two-phase flow splitting. The measured and predicted flow splits were observed to be in agreement.

Gas-liquid two-phase flow measurement by combining a Coriolis flowmeter with a flow conditioner and analytical models

Abstract In oil-gas fields, two phase flow measurement of liquid-gas mixture for the whole Gas Void Fraction (GVF) range remains a challenging task. Coriolis flow meter is proven to be one of the most accurate device for single phase flow measurement. However, for two phase flow metering, its accuracy gets altered for liquid continuous phase (respectively gas continuous phase) in case of GVF that exceeds 20% (respectively low GVF that is lower than 80%). This paper presents a new Coriolis-based two phase flow meter (TPFM) that uses an upstream inline flow conditioner which separates liquid (i.e. water in this paper) from gas. Each of the gas dominant phase and liquid dominant phase are carried by two outlets, namely gas and liquid outlets respectively. A Coriolis flow meter and an actuated valve are placed in each outlet to measure and regulate the GVF that passes in each line. A new measurement algorithm based on a multistage artificial neural network (ANN) and which explores the physical model of the Coriolis flow meter is also suggested. This latest combines the pressure-volume-temperature (PVT) and bubble theory models to estimate the amount of gas (respectively liquid) dissolved in the liquid phase (respectively gas phase). Experimental results that were conducted on a multiphase flow loop reveals that for the whole GVF range (i.e. 0 to 100% GVF), the absolute value of the relative error on both the mass flow rate and density does not exceed 2.5%. This suggests that the proposed TPFM can be considered as a new non-hazardous alternative for accurate two phase flow measurement.

A new method to determine conductivity distribution based on wire-mesh sensor by iteration

The wire-mesh sensor (WMS) is a kind of advanced instrument for gas-liquid two-phase flow measurements. In previous research, the sensitive volume was assumed to be uniformly distributed in the cross-section. The received current was assumed to represent the conductivity averaged over a uniform sensitive volume (USV), which is defined by symmetry considerations within the geometry of the electrode grids of the sensor. The void fraction within USV can be further determined based on the conductivity of USV. However, it is unphysical to determine the USV’s conductivity by the received current directly, since the true sensitive volume has an irregular shape due to the complexity of the potential field in the WMS. This study proposes an iterative method to determine the normalized conductivity averaged over each USV from the received current of WMS. The iterative method is based on potential field simulations within the electrode geometry of the WMS and an iterative process. To evaluate the performance of the iterative method, different normalized conductivity distributions were pre-defined. The maximum deviation between the pre-defined normalized conductivity and the normalized conductivity value determined by the new method was within 3%, instead of 30% for the conventional method. The performance of the new method for WMSs with different axial distance between transmitter and receiver grids La was also evaluated. For a larger La, more iterations need to be carried out to reach this accuracy. The limitation of this iterative method has been discussed. To reconstruct the sub-USV non-conductive phase distribution, the resolution of iterative method should be enhanced to describe the distribution of sub-USV non-conductive phase in future studies.

Dynamic measurement of gas volume fraction in a CO2 pipeline through capacitive sensing and data driven modelling

Gas volume fraction (GVF) measurement of gas-liquid two-phase CO2 flow is essential in the deployment of carbon capture and storage (CCS) technology. This paper presents a new method to measure the GVF of two-phase CO2 flow using a 12-electrode capacitive sensor. Three data driven models, based on back-propagation neural network (BPNN), radial basis function neural network (RBFNN) and least-squares support vector machine (LS-SVM), respectively, are established using the capacitance data. In the data pre-processing stage, copula functions are applied to select feature variables and generate training datasets for the data driven models. Experiments were conducted on a CO2 gas-liquid two-phase flow rig under steady-state flow conditions with the mass flowrate of liquid CO2 ranging from 200 kg/h to 3100 kg/h and the GVF from 0% to 84%. Due to the flexible operations of the power generation utility with CCS capabilities, dynamic experiments with rapid changes in the GVF were also carried out on the test rig to evaluate the real-time performance of the data driven models. Measurement results under steady-state flow conditions demonstrate that the RBFNN yields relative errors within ±7% and outperforms the other two models. The results under dynamic flow conditions illustrate that the RBFNN can follow the rapid changes in the GVF with an error within ±16%.

Measurement of vertical gas-liquid two-phase flow by electromagnetic flowmeter and image processing based on the phase-isolation

In this paper, the electromagnetic flowmeter and the image processing are used to measure vertical gas-liquid two-phase flow. In order to reduce the effect of randomly distributed bubbles on the weight function of the electromagnetic flowmeter and the bubbles overlap on the image processing in raw two-phase flow, a special implementation method of phase-isolation for vertical upward flow is developed to achieve a stable and uniform swirling core-annular flow. For this flow pattern, the behavior of the electromagnetic flowmeter is analyzed and an automatic image-processing algorithm is developed. Both the simulated gas column experiment and dynamic air-water two-phase flow experiment were carried out to verify the theoretical equation of liquid flowrate. The results demonstrate that the instrument constant of electromagnetic flowmeter does change in the annular flow cross-section. A cross-section shape coefficient kα is needed in the equation of liquid flowrate, and kα is proved to be only related to the void fraction, independent of the type of electromagnetic flowmeter and the properties of central insulated column. Moreover, the swirl has no effect on the electromagnetic flowmeter. The overall average error of the liquid flow rate is 2.6%, and the relative error is within ±5% in bubble and slug flow condition. The measurement result is immune to the gas velocity.

Experimental Measurement and CFD Simulation for Characterization of Coriolis Flowmeter Performance

Gas-liquid two-phase flow measurement is challenging in comparison to single phase flow measurement as their dynamics are more complex. This study aims to investigate the performance of a U shape Coriolis mass flowmeter (CMF) to measure gas liquid two-phase flow via both experimental measurements and numerical simulation. It was observed from both measured and simulation results that the measurement performance of CMF was mainly affected by variation of mixture density and variation of flow pattern associated with gas entrainment. The findings provide fundamental fluidic dynamics of on-line multiphase flow measurements in harsh environments.

Void Fraction Measurement of Gas-Liquid Two-Phase Flow Based on Empirical Mode Decomposition and Artificial Neural Networks

ABSTRACTA new void fraction estimation method for gas–liquid two-phase flow combining two differential pressure (DP) signals acquired from a single Venturi tube and based on Empirical Mode Decomposition (EMD) and Artificial Neural Networks (ANN) was experimentally investigated. In order to study gas–liquid distribution in horizontal pipes, two DP signals from the top and bottom sections of the Venturi tube are acquired and EMD is adopted to extract stable and fluctuating components of the DP signals. Experimental data revealed that fluctuating index increases nearly linearly with increasing void fraction when void fraction is less than 0.4. When void fraction is larger than 0.4, this near-linearity ceases. A combination of ANN method and the fluctuating index of DP signals is developed to estimate void fraction. Experimental results show that void fraction based on DP signal from top section of Venturi tube is overestimated because of clustering of bubbles and the scarcity of liquid information when gas–liquid mixture velocity is low. Void fraction is underestimated when the mixture velocity is high. A high gas–liquid slip ratio results in void fraction underestimation. Void fraction prediction performance is satisfactory when void fraction is less than 0.4 and fluctuating index of DP signal less than 1.2.

Gas-liquid two-phase flow measurements by the electromagnetic flowmeter combined with a phase-isolation method

The electromagnetic flowmeter has presented excellent performance in single-phase flow. However, there are many fundamental difficulties when it is applied in two-phase flow. The presence of the insulating phase at various positions in two-phase flow strongly influences the distribution of the weight function, consequently resulting in the uncertainty and instability of the electromagnetic flowmeter's output. When the insulating phase contact with the electrode, it even leads to wrong alarm. In this paper, at the upstream of the electromagnetic flowmeter a phase-isolation method was used to change the inlet flow pattern into a uniform and symmetrical swirling core-annular flow, in which gas-liquid mixture was isolated to gas core and liquid annular flows flowing concurrently in the transmission pipe with a clear smooth interface between them. Then we analysed the behavior of the electromagnetic flowmeter performed in this specific flow pattern, and built the liquid flow rate measurement model. Besides, the void fraction was measured by image processing technique. Owing to the clear smooth interface, the difficulty of image processing and the measurement error of the void fraction can be reduced. The experimental results showed that, the combination of the phase-isolation method could improve the measurement accuracy and successfully make the electromagnetic flowmeter available for the gas-liquid two-phase flow where the original phase distribution is not uniform.

Flow rate measurement of low GVF gas-liquid two-phase flow with a V-Cone meter

Accurate online measurement of the liquid flow rate in a low GVF gas-liquid two-phase flow is of great importance for engineering and science. In the present study, a cost-effective metering method is developed on the basis of a V-Cone meter. Experiments are carried out in a horizontal test section and the fluids are air and water with GVF of 0–12.2%. The flow characteristics of the V-Cone throttle device with low GVF gas-liquid two-phase flow passing through are investigated. Experiments show that the V-Cone throttle device can modulate the flow pattern and the gas presence in a liquid flow can accelerate the development of the flow downstream of the cone. To correct the gas-induced liquid flow rate prediction error, a dimensionless parameter termed the two-phase mass flow coefficient is proposed. Experimental results indicate that the coefficient is dependent on the gas densiometric Froude number, the liquid densiometric Froude number and the gas-to-liquid density ratio, and the consequent correlation is obtained by data fitting. Besides, the permanent pressure loss of the V-Cone is investigated and a prediction correlation for the two-phase pressure loss is proposed. Finally, by incorporating the two-phase mass flow correlation and the pressure loss correlation, a metering method for the liquid flow rate in a low GVF gas-liquid two-phase flow is developed. The relative error of the liquid mass flow rate predicted by the method is less than ±4.0% at the confidence level of 97.5%. The newly developed metering method provides a high-accuracy as well as cost-effective measurement technology for the industry.

Visible measurement of gas-liquid two-phase flow in aircraft engine

Visible measurement of gas-liquid two-phase flow in aircraft engine

Gas-Liquid Two-Phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine, and Genetic Programming Algorithms

Coriolis flowmeters are well established for the mass flow measurement of single-phase flow with high accuracy. In recent years, attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated into Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 to 14500 kg/h and a gas volume fraction between 0% and 30%. Artificial neural network (ANN), support vector machine (SVM), and genetic programming (GP) models are established through training with the experimental data. The performance of backpropagation-ANN (BP-ANN), radial basis function-ANN (RBF-ANN), SVM, and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN, and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49% of the experimental data yield a relative error less than ±1% on the horizontal pipeline, while 96.17% of the results are within ±1% on the vertical installation. The SVM models predict the gas volume fraction with a relative error less than ±10% for 93.10% and 94.25% of the test conditions on the horizontal and vertical installations, respectively.

Gamma-ray CT from incomplete projections for two-phase pipe flow.

A low-energy low-dose γ-ray computed tomography (CT) system used in the gas-liquid two-phase pipe flow measurement has been studied at Tianjin University in recent years. The γ-ray CT system, having a third-generation X-ray CT scanning configuration, is comprised of one 300mCi 241Am source and 17 CdZnTe detector units and achieves a spatial image resolution of about 7 mm. It is primarily intended to measure the two-phase pipe flow and provide improvement suggestions for industrial CT system. Recently we improve the design for image reconstruction from incomplete projection to optimize the scanning parameters and reduce the radiation dose. First, tomographic problem from incomplete projections is briefly described. Next, a system structure and a hardware circuit design are listed and explained, especially on time parameter setting of the pulse shaper. And then a detailed system analysis is provided in Section II, mainly focusing on spatial resolution, temporal resolution, system noise, and imaging algorithm. Finally, we carry on necessary static and dynamic experiments in a full scan (360°) and two sets of partial scan reconstruction tests to determine the feasibility of this γ-ray CT system for reconstructing the images from insufficient projections. And based on an A-variable algebraic reconstruction technique method, a specially designed algorithm, we evaluate the system performance and noise level of this CT system working quantitatively and qualitatively. Results of dynamic test indicate that the acceptable results can be acquired using a multi-source γ-ray CT system with the same parameters when the flow rate is less than 0.04 m/s and the imaging speed is slower than 33 frames/s.