Model analysis for differential pressure two-phase flow rate meter in intermittent flow

Multiphase flow rate metering is a challenging problem, specially for flow patterns other than wet-gas. This paper brings forward a new comparative analysis of three differential pressure calibration models suited for liquid dominated two-phase flows, in a total of seven model configurations. First, the models are compared theoretically and classified in terms of the type of input data required. Then, experimental data of over 300 horizontal air–water experiments, for $1$” and $2$” pipe diameters, supports quantitative analyses of the prediction accuracies and sensitivity of the superficial velocities of gas and liquid to measurement errors in the model input variables. Finally, a method for assessing the decoupled measurement errors for the void fraction and gas velocity is shown, as these variables are typically subject to higher uncertainties. It results that, though the void fraction is shown to be systematically under evaluated in more than 10%, the total mass flow rate is estimated through the Paz et al. (2010) model with an overall root mean squared deviation (RMSD) of 5.75% for the $2$” data. Also, the use of gas velocity measurements, even if subject to considerable errors, decreased the RMSD for the gas superficial velocity by more than half for the $1$” data.     

Identification of two-phase flow pattern in porous media based on signal feature extraction

The statistical analysis methods based on differential pressure signals of two-phase flow are employed in the present study to identify the flow patterns in packed porous bed. The typical flow pattern images of two-phase flow in the packed porous beds are recognized and the corresponding differential pressure signals are recorded based on the visualization experiments. Then the statistical analysis methods, including probability density function (PDF), power spectral density (PSD), and wavelet energy spectrum (WES), are employed to extract the features of differential pressure signals in the time domain, frequency domain, and time-frequency domain respectively. The dimensionless parameters are proposed as the evaluation index to quantify the differences among flow patterns. The results show that the PDF, PSD, and WES methods can effectively characterize different flow patterns in the time, frequency, and time-frequency domain, respectively. The comprehensive recognition efficiency is about 88.5% using the introduced dimensionless parameters.     

Theoretical and experimental investigation for developing a gas-liquid two-phase flow meter

Despite the intricacy, inline metering of two-phase flow has a significant impact in multitudinous applications including fusion reactors, oil, nuclear, and other cryogenic systems. Since measurement of individual flow rate is prominent in various systems, it warrants the establishment of a flow meter system that can monitor the mass flow rates of liquid. In this regard, an approach was taken towards the development of a two-phase flow meter system in the present study. The concept involves two-phase flow through narrow parallel rectangular channels resulting in laminar, stratified flow with a slope at the liquid-vapor interface. The height of the liquid column at specific channel locations is measured for determining the flow rate. However, the geometric configurations of the channels and fluid properties are pivotal in ensuring accurate measurement. Consequently, theoretical and experimental studies are performed to investigate the correspondence between flow rate and change in liquid height. Based on the governing equations, a theoretical model is established using MATLAB®. The model investigated the intricate influence of various flow and fluid properties in the estimation of the mass flow rate. The experimental investigation was done with various conditions under different liquid and vapor volume flow rates for validating the proposed supposition and the theoretical model. Both the theoretical and experimental analyses showed fair correspondence. The proposed system estimated the mass flow rate within a tolerance of ±10% and showed potential towards the development of the cryogenic two-phase flow meter.     

Regime independent flow rate prediction in a gas-liquid two-phase facility based on gamma ray technique and one detector using multi-feature extraction

One of the key challenges in petroleum related industries is how to precisely measure the flow rates of individual phases in a pipeline. To address this challenge, in the present study, an automated two-phase test loop capable of generating different flow patterns in horizontal pathway is used to perform experiments on the flow rates. The measuring package set-up consists of a Cs-137 radiation source with photon energy of 662 keV and one NaI (Tl) scintillation detector to register transmission counts. Multi-layer perceptron (MLP) is the selected processing element. Distinguished property of this paper is considering a feature vector with diverse-nature elements as input and the flow rate values of water and air as the target elements. Several combinations of features were investigated to determine the feature vector which shows the best quality to predict the flow rates. Moreover, two structures of MLP with different scenarios of hidden layers were utilized for examining every feature vector set. Numerous experiments were performed to collect adequate data to train and test the ANN in a wide range of air and water flow rates. The results indicate that the proposed model achieved MAE of less than 1 and 5.9 L/min and MRE% of 1.09% and 1.45% to predict water and air flow rates, respectively. The results show that the presented ANN model outperforms existing methods on multiphase flow rates measurements. Therefore, the proposed feature extraction method is applicable to estimate phase flow rates in a two-phase flow for industrial goals.     

Support vector machine modelling applied to benchmark data set for two-phase Coriolis mass flow metering

An earlier paper introduced a dataset of Coriolis meter mass flow and density errors, induced by the effects of two-phase (gas/liquid) flow, as a benchmark for which various error correction strategies might be developed. That paper further presented a series of error correction models based on neural nets. The current paper presents an alternative analysis of the same data set, using a support vector machine (SVM) approach. The analysis demonstrates that, for the benchmark data set, more accurate models are generated than those developed using neural nets. More specifically, it is found that a linear SVM model provides better performance than non-linear SVM. This improved performance may arise from over-fitting by both non-linear SVM and neural nets on this relatively small data set.     

Multiphase flow measurements using coupled slotted orifice plate and swirl flow meter

Multiphase flow metering is a major focus for oil and gas industries. The performance of a modified version of a close coupled slotted orifice plate and swirl flow meter for multiphase flow was evaluated to provide further development of a new type of multiphase flow meter. The slotted orifice provides well homogenized flow for several pipe diameters downstream of the plate. This characteristic provides a homogeneous mixture at the inlet of the swirl meter for a wide range of gas volume fractions (GVF) and flow rates. In order to evaluate the performance of the designed flow-meter, its response was investigated for varying pressures and water flow rates. The proper correlations were established to provide high accurate two-phase flow measurements. The new proposed approach provides the GVF measurement with less than ±0.63% uncertainty for GVF range from 60% to 95%.     

Calculation of the virtual current in an electromagnetic flow meter with one bubble using 3D model

Based on the theory of electromagnetic induction flow measurement, the Laplace equation in a complicated three-dimensional (3D) domain is solved by an alternating method. Virtual current potentials are obtained for an electromagnetic flow meter with one spherical bubble inside. The solutions are used to investigate the effects of bubble size and bubble position on the virtual current. Comparisons are done among the cases of 2D and 3D models, and of point electrode and large electrode. The results show that the 2D model overestimates the effect, while large electrodes are least sensitive to the bubble. This paper offers fundamentals for the study of the behavior of an electromagnetic flow meter in multiphase flow. For application, the results provide a possible way to estimate errors of the flow meter caused by multiphase flow.     

A simple and inexpensive design for volume fraction prediction in three-phase flow meter: Single source-single detector

The problem of how to correctly predict the volume fraction in three-phase flows in sensitive operational conditions is an important parameter in the oil and petroleum industry. This research presents a new methodology for volume fraction percentage prediction in water-gasoil-air multiphase systems based on transmitted gamma ray method and multilayer perceptron (MLP). The detection system uses an appropriate single source-single detector set-up, consists of a dual-energy gamma-ray source and one NaI(Tl) detector. The experimental setup provides the required data for training and testing the network. Using this proposed method, the volume fraction was predicted in water-gasoil-air three-phase flows with mean relative error percentage (MRE%) less than 4.64% and root mean square error (RMSE) of 1.49. MER% quantities for each phase of water, gasoil and air were calculated 3.72, 4.54 and 5.65, respectively. Also, the RMSE quantities were measured 0.79, 1.46 and 1.98 for water, gasoil and air, respectively. The results revealed that a good agreement is between the predicted volume fraction percentage and actual data due to the comparison of the performance of the proposed method (MLP) and the relationship derived from empirical data. The old systems used were adequately complicated whereas the new design is certainly very simple. Also, radiation safety and shielding conditions ans electronic requirements are optimized. So the cost is minimized as low as possible.     

A two-phase flow meter for determining water and solids volumetric flow rates in stratified,inclined solids-in-water flows

This paper describes the design and implementation of a two-phase flow meter which can be used in solids-in-water two phase pipe flows to measure the in-situ volume fraction distributions of both phases, the velocity profiles of both phases and the volumetric flow rates for both phases. The system contains an Impedance Cross Correlation (ICC) device which is used in conjunction with an Electromagnetic Velocity Profiler (EVP). Experimental results were obtained for the water and solids velocity and volume fraction profiles in upward inclined flow at 30° to the vertical, in which highly non-uniform velocity and volume fraction profiles occur.     

Novel mass air flow meter for automobile industry based on thermal flow microsensor. II. Flow meter,test procedures and results

A prototype of mass air flow meter for automobile industry was developed on the basis of thermal flow microsensor. Design and manufacturing technology of the flow meter are described. Test procedure and results are presented. Developed prototype of flow meter can diagnose gas flow rates in a wide range.     

Identification of gas-liquid two-phase flow patterns in a horizontal pipe based on ultrasonic echoes and RBF neural network

This paper proposes a novel flow pattern identification method using ultrasonic echo signals within the pipe wall. A two-dimensional acoustic pressure numerical model is established to investigate the ultrasonic pulse transmission behavior between the wall-gas and wall-liquid interface. Experiments were also carried out at a horizontal air-water two-phase flow loop to measure the ultrasonic echo pulse signals of stratified flow, slug flow, and annular flow. It is interesting to find that the attenuation of the ultrasonic pulse at the wall-liquid interface is faster than the attenuation at the wall-gas interface. An RBF neural network is constructed for online flow pattern identification. The normalized envelop area and the area ratios of the echo spectrum are selected as the input parameters. The results show that the stratified flow, slug flow, and annular flow can be identified with an accuracy of 94.0%.     

Pipe two-phase flow non-invasive imaging using Ultrasound Computed Tomography: A two-dimensional numerical and experimental performance assessment

Pipe two-phase flow non-invasive imaging is of great interest in the field of industry. In particular, small bubble flow imaging through opaque pipes is challenging. Ultrasound computed tomography can be a relevant technique for this purpose. However, perturbation phenomena that are inherent to the configuration (acoustic impedance mismatching, circumferential propagation, reverberation) limit two aspects: the performance of the technique and the use of conventional inversion algorithms. The objectives of the presented work are: (i) to predict the effects of the pipe wall on ultrasonic waves for both metallic and plastic pipe, (ii) to define a consistent inversion algorithm taking into account those effects, (iii) to validate and to assess the limitations of the designed imaging numerical tool using an experimental setup. The benchmark configuration consists of 150 mm diameter 3 mm thick pipes containing 6 mm diameter rods acting as reference scatterers. Two materials of very different acoustical properties were tested: aluminum and PMMA. The results highlighted that the quality of the reconstructed image is very dependent on the pipe material. The results showed that, using an adapted inversion model, consistent target reconstruction is obtained. Based on numerical predictions, performance limitations are reached for metallic pipes.     

Estimation of flow rates of individual phases in an oil-gas-water multiphase flow system using neural network approach and pressure signal analysis

Up until now, different methods, including; flow pressure signal, ultrasonic, gamma-ray and combination of them with the neural network approach have been proposed for multiphase flow measurement. More sophisticated techniques such as ultrasonic waves and electricity, as well as high-cost procedures such as gamma waves gradually, can be replaced by simple methods. In this research, only flow parameters such as temperature, viscosity, pressure signals, standard deviation and coefficients of kurtosis and skewness are used as inputs of an artificial neural network to determine the three phase flow rates. The model is validated by the field data which were obtained from separators of two oil fields and 6 wells over ten-month with 8 h interval (totally 5400 sets of data). A linear relation can be observed between the actual data and the predictions which were obtained from separators and neural network approach, respectively. Furthermore, it is shown that using feed forward neural network with Levenberg–Marquardt algorithm which has two hidden layers is sufficient to determine the flow rates. Also, it is tried to see the effect of flow regimes on the results of neural network approach by determining kurtosis and skewness coefficients for different flow regimes in a horizontal pipeline.     

基于多尺度加权形态网络的燃气流量计健康状态评估

燃气流量计是天然气贸易计量的重要仪器,而其健康状态的改变会造成计量偏差。为了减少燃气企业的经济损失,本文提出了一种基于多模态数据扩充、形态学特征学习和多尺度自适应加权形态学网络的燃气流量计健康状态评估方法。首先,采用基于Wasserstein距离和谱归一化的ACGAN算法进行数据扩充。其次,针对燃气流量计振动信号数据的复杂性和噪声影响,提出了一种基于平均帽变换的形态学方法提取信号的正负脉冲信息。最后,针对非平稳、变工况的工业条件,引入多尺度自适应加权形态学网络,采用具有不同结构元素尺度的多个分量来分别提取脉冲信息,并利用自适应加权融合来增强提供强脉冲分量的尺度。通过实验结果表明,所提出的方法对燃气流量计健康状态评估的准确度超过94%。该方法对实际燃气贸易计量有重要的应用价值。     

Measurement of high-water-content oil-water two-phase flow by electromagnetic flowmeter and differential pressure based on phase-isolation

When the oil field has been exploited by long-term water-flooding, it will be in high water-content stage of production. However, it is a great challenge for high-water-content measurement due to oil droplets extremely dispersed in the water. In this paper, we developed a phase-isolation based method for high-water-content oil-water two-phase flow measurement. Phase-isolation was realized by axial-flow swirler to concentrate scattered and random oil droplets into the pipe center and change the inlet flow pattern into a particular annular flow before measuring. Owing to the axisymmetric velocity and phase distribution, the electromagnetic flow meter avoided the effect of random distribution of insulating phase, and then had a good measurement performance for total volume flow rate. Furthermore, we respectively studied using axial pressure drop, radial pressure drop and the ratio of the two pressure drops to measure water content. The results showed that the ratio of the two pressure drops not only improves the resolution of oil and water, but also effectively reduces the impact of error transfer. In the dual-parameter measurement experiment, the relative errors of total volume flow rate and water content were almost within ±5%.     

Identification of gas/liquid two-phase flow regime through ERT-based measurement and feature extraction

Gas/liquid two-phase flow is of great importance in various industrial processes. As the most important characteristic of a two-phase flow, the flow regime not only characterizes the flow condition in an explicit way, but also determines the measurement model in each measuring method. Based on the application of Electrical Resistance Tomography (ERT) to a gas/liquid two-phase flow on a vertical pipe, features reflecting the characteristics of gas/liquid two-phase flow are extracted directly from the measured data without reconstruction of the cross-sectional images. The statistical features are derived through time series statistical analysis. Meanwhile features in the wavelet-scale domain are derived through both one-dimensional and two-dimensional wavelet transform. All extracted features are considered as the input of a Support Vector Machine (SVM) algorithm to recognize the flow regime. The preliminary results show that the feature extraction methods of multi-feature fusion and high-dimensional wavelet transform are suitable for the identification of gas/liquid two-phase flow regimes.     

Three-phase flow meters based on X-rays and artificial neural network to measure the flow compositions

The methodology presented in this study is based on a 149.5 keV X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions in a three-phase system. Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. A mathematical algorithm based on an artificial neural network was used to correlate the energy spectra from both detectors with the fluids volume fractions. The pulse height distributions obtained by the detectors are used as input data of the network that outputs the volume fractions of gas and water. The mean relative error, using the procedure presented here, for all data, was below 2.5% for both phases investigated. These results show that the methodology based on an X-ray beam has the potential to be used with flow meters.     

基于经验模态分解和BP神经网络的油气两相流流型辨识

基于经验模态分解（empidcal mode decomposition，EMD）BP神经网络，提出了油气两相流流型辨识的新方法。应用EMD将差压信号分解成不同频率尺度上的单组分之和，并提取组分的归一化能量作为流型辨识特征量。BP神经网络以这些能量特征量为输入对油气两相流不同流型（包括泡状流、塞状流、层状流、弹状流和环状流）进行分类。实验结果表明，本文提出的流型辨识方法是有效的，其中泡状流、塞状流、层状流、弹状流和环状流的辨识精度分别为100％、89．4％,93．3％、96.3％和96.9％。     

Analysis of two-phase flow regimes and parameter distribution in 5 x 5 rod bundle using image processing techniques

With the recent developments in image processing and analysis, this paper presents bubble characteristics distribution in adiabatic air-water two-phase flow through a 5 × 5 rod bundle. The experiment covered water superficial velocities (J_l = 0.012 m/s – 0.421 m/s) and air superficial velocities (J_g = 0.042 m/s – 0.987 m/s) in which three distinct flow regimes were identified. The flow regime map was compared with existing flow regime transition criteria for vertical rod bundles. Distinct features from the two-phase flow images were extracted to train a classifier model to distinguish between regimes from a separate experiment. The model distinguishes between the bubbly flow regime and others accurately. The void fraction and velocity distributions were also extracted from the R–CCN masked images. Bubble-induced turbulence that was dominant in the subchannel at (J_l = 0.28 m/s) shifted to the outer subchannels and gaps when the flow rate increased (J_l = 0.42 m/s). These methods over-predicted the void faction around the surfaces of the inner rods.     

Research on calculation method of free flow discharge based on artificial neural network and regression analysis

Taking the Huaidian Sluice on the Shaying River in China as an example, this paper establishes the calculation model of the free flow based on artificial neural network and regression analysis. Four forms of discharge coefficient calculation equations were obtained by regression analysis, and three neural network models were established. The model is fully verified by using the measured data. The experimental results show that the third-order polynomial and multilayer perceptron neural network have better adaptability. The advantages and disadvantages of the different methods are analyzed and the cause of the error is identified. It provides a theoretical basis for dealing with the discharge calculation of small and medium dam.