基于ERT技术的垂直管道两相流流型识别

在两相流测量问题的研究中 ,流型的准确识别是其它流动参数准确测量的基础。基于电学敏感原理的电阻层析成像技术 (ERT) ,由于可视化、无辐射、低成本等优点 ,在两相流动参数的检测中具有很广阔的发展前景。研究中以 ERT系统样机为测量工具 ,以垂直管道气 /液两相管流为研究对象。通过对模拟流动状态实验下几种典型流型的测量数据的采集、处理 ,采用模式识别中的特征提取方法降低数据维数 ,并利用数理统计和神经网络方法识别流型。研究结果表明采用距离判别法和径向基神经网络技术均得到较高的识别率     

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.     

Research on signal amplitude of the Kármán vortex street in gas–liquid two-phase flow with high void fraction

Based on Biot–Savart law and single-phase flow Kármán vortex characteristics, flow field has been analyzed when gas–liquid flow past a fixed bluff body with high void fraction. Vortex signal characteristics have been studied for stratified two-phase flow on atmospheric conditions in a horizontal pipe. To discuss the relation between void fraction and vortex signal amplitude spectrum, this paper sets up the vortex-induced pressure field model for gas–liquid two-phase flow and gives the relationship between void fraction and relative amplitude spectrum of two-phase flow to single-phase flow. An algorithm is proposed for predicting the two-phase flow parameters. Experiments were performed using air–water as working fluid along with a test tube diameter of 50 mm, at gas volume flow rate of 20–68 m³/h, and void fraction of 0.9–1. The results indicate that calculations by the vortex-induced pressure field model on the amplitude spectrum of vortex signal are in good agreement with the experimental data, and relative errors of the algorithm predictions on gas volume flow rate and liquid volume flow rate are 0.08 and 0.56, respectively.     

Gas/liquid two-phase flow regime identification by ultrasonic tomography

A gas/liquid two-phase flow is considered as a strongly inhomogeneous medium with respect to high contrast in acoustic impedance distribution. Based upon a binary logic operation and a method of “time-of-propagation along straight path”, an ultrasonic facility for tomographic imaging of gas/liquid two-phase flow was developed. In this paper the principle and construction of this facility are briefly introduced. Emphasis is placed on the evaluation of its performance in flow regime identification and cross-sectional void fraction measurement. Several flow pattern models were used and the corresponding monitoring results given. Finally, limitations and possible future improvements of the system are discussed.     

The turbine meter applied to void fraction determination in two-phase flow

A new approach to analysing the pulse output information from a standard turbine meter in two-phase liquid/gas flows is presented. After suitable calibration, the meter will register the presence of gas, with an accurate indication of void fraction up to 20%, and provide a measure of the liquid flowrate. The single-phase performance of the meter remains unaffected.     

Imaging of gas–liquid annular flows for underbalanced drilling using electrical resistance tomography

The underbalanced drilling technique, which is also known as managed-pressure drilling, is playing an important role in oil and gas sector, as it reduces common conventional drilling problems such as minimal drilling rates and formation damage, differential sticking and lost circulation. Flow regime monitoring is one of the key topics in annular multiphase flow research, particularly for underbalanced drilling technique. Prediction of the prevailing flow regime in an annulus is of particular importance in the design and installation of underbalanced drilling facilities. Especially, for establishing a suitable pressure-drop model based on the characteristics of the active flow regime. The methods of flow regime prediction (or visualisation) in an annulus that are currently in use are very limited, this is evidently due to poor accuracy or they are simply not applicable to underbalanced drilling operation in practice. Therefore, this paper presents a monitoring method, in which Electrical Resistance Tomography (ERT) is used to rapidly image the prevailing flow regime in an annulus with a metallic inner pipe. Experiments were carried out using an air–water flow loop with a test section 50 mm diameter flow pipe. The two-phase air–water flow regimes are visualised in the upward vertical annulus with a radius ratio (r/R) 0.4. This paper highlights the visualisation results of only three flow regimes, namely bubble flow, transitional bubble-slug flow and slug flow. The flow regimes are visualised through axial images stacked from 50 mm diameter-pixels of 2D tomograms reconstructed with the Conjugate Gradient Method (SCG). Gas volume fraction profiles within the annular flow channel are also illustrated. The profiles are extracted using the Modified Sensitivity coefficient Back-Projection (MSBP) method with a sensitivity matrix generated from a realstic phantom in the finite element method software. The results are compared with visual observations (e.g. photographs) of the active flow regime at the time of ERT measurements.     

Experimental investigation on the void fraction characteristics of a sudden expansion tube using wire-mesh sensors

Void fraction is an essential parameter of gas-liquid two-phase flow and experiments were executed to investigate the void fraction fluctuation characteristics of gas-liquid two phase flow through a sudden expansion tube. Two 16 × 16 wires mesh sensors were applied to measure the phase distribution of upstream pipe(pipe-32) and downstream pipe(pipe-50). The superficial gas velocity is in the range of 3.46 m/s - 22.46 m/s and the superficial liquid velocity ranges from 0.034 m/s to 0.414 m/s. Flow pattern evolution of upstream and downstream pipes was reconstructed and compared. The experiment results show that, in contrast to pipe-32, the void fraction of pipe-50 shows different trends with the increase of liquid and gas velocity. Liquid-carrying capacity is essential in the relationship between the void fraction of pipe-32 and pipe-50. The critical superficial liquid and gas velocities are proposed to characterize the liquid-carrying capacity. The maximum critical superficial gas and liquid velocity is 15.56 m/s and 0.207 m/s, respectively. Besides, a model is proposed to describe the relationship of void fraction between pipe-32 and pipe-50. It is found that the prediction error is less than ±10% in the case of annular flow.     

Independent component analysis of interface fluctuation of gas/liquid two-phase flows — experimental study

It is important to understand the behaviour of two-phase flows in industry. This paper presents a study of the interface fluctuation between gas/liquid two-phase flows in a horizontal pipeline. Having obtained the data of a gas/liquid flow by electrical resistance tomography (ERT), an independent component analysis (ICA) method can be applied not only to extract the flow regime information but also the interface fluctuation of the flow. The efficiency of ICA with the ERT data has been assessed by experiment. The independent components have been interpreted by comparing the obtained independent components with the reconstructed images by ERT, showing that ICA is not only effective in extracting flow regime information, but also provides the fluctuation of each individual phase and the interface between the two phases. Without modelling the forward problem, this method can be applied to other electrical tomography modalities.     

Sectional void fraction measurement of gas-water two-phase flow by using a capacitive array sensor

The sectional void fraction measurement for multiphase flow is usually influenced by flow patterns. Inspired by electrical capacitance tomography (ECT) devices applied to flow imaging (whose measured capacitance data contain both the flow pattern and sectional void fraction information), a capacitive array sensor is developed to realize two functions, flow pattern recognition and void fraction measurement, simultaneously; so that the void fraction measurement can be conducted for a certain flow pattern and the measurement accuracy can be expected to be improved. The main idea of the proposed method can be described as: firstly, the proper feature vectors are extracted from the electrical signal to identify the flow pattern (the BPNN model with GDX learning algorithm is used for flow pattern identification); and then the average of electrical signal is applied to estimates the void fraction by the corresponding calibration curve. An experimental platform of air/water two-phase flow is built (on which 3 flow patterns can be generated stably) to test the performance of the proposed method. The results support the correctness and effectiveness of the proposed method.     

Two-phase flow pattern classification based on void fraction time series and machine learning

Two-phase flow is a complex phenomenon present in several industrial applications such as chemical reactors, power generation, and in the exploration, production, and transport of oil and natural gas. The classification of the flow pattern is a fundamental step in such applications, as it influences several derived parameters and sub-processes such as flow rate, void fraction, and pressure drop estimation. In this paper, we propose an objective approach for classifying flow patterns using time series of void fraction (from a wire-mesh sensor), signal processing and machine learning. As novel approach, the time series is modeled as a stochastic process of independent and identically distributed samples with probability density function described by a Gaussian mixture model. The estimated parameters of the mixture are then fed into a Support Vector Machine (SVM), yielding the flow pattern classification. Tests were performed with a vertical liquid–gas flow database from a 52.3-mm-diameter pipe and the results indicate a great potential for application in real systems. The average accuracy and F-score obtained was higher than 0.94 for different test sets, with standard deviation lower than 0.08 for accuracy a lower than 0.11 for F-Score, demonstrating the efficiency and generalization of the proposed method.     

Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas–liquid two-phase flows

In this study, a simple detection system comprised of one ⁶⁰Co source and just one NaI detector was investigated in order to identify flow regime and measure void fraction in gas–liquid two phase flows. For this purpose, 3 main flow regimes of two-phase flows including stratified, homogenous and annular with void fractions in the range of 5–95% were simulated by Monte-Carlo N Particle (MCNP) code. At first step, 3 features (count under full energy peaks of 1.173 and 1.333 MeV, and count under Compton continuum) were extracted from registered gamma spectrum. These 3 extracted features were used as inputs of artificial neural network (ANNs). A primary network was trained for identifying the flow regimes, but after testing many different structures, it was found that just two regimes of stratified and annular could be completely identified from each other. After identifying the mentioned two flow regimes by the first ANN, two specific ANNs were also implemented for predicting the void fraction. Using the proposed method in this work, void fraction percentages were predicted with a mean relative error (MRE) of less than only 0.42%. Using fewer detectors is of advantage in industrial nuclear gauges, because of reducing economical expenses and also simplicity of working with these systems.     

非完整ERT数据的两相层状流分布图像重建

针对电阻层析成像在气液两相层状流测量中会有电极被气相覆盖，无法正常重建图像的问题，提出基于有效数据和基于敏感场重建的两种气液两相层状流分布重建解决方法。基于有效数据直接重建方法是采用边界测量数据的幅值判断方法确定失效电极的数目和位置，进而对原始测量数据进行筛选，保留正常的测量数据对整个测量区域进行图像重建。基于敏感场重建的方法，是在判断出失效电极之后，将层状流的流动状态作为一种先验信息，去除失效电极包围的区域，图像重建只在有效电极包围的区域进行，从而减少了未知量的数目，期望达到提高重建图像的空间分辨率的目的。仿真与层状流实验结果证明了两种方法的有效性。     

A non-convex regularization method combined with Landweber method for image reconstruction in electrical resistance tomography

Electrical resistance tomography (ERT) is a promising technique with which the conductivity distribution in the detected region can be visualized. Mathematically, the reconstruction of conductivity distribution is a seriously ill-posed inverse problem which poses a great challenge for the ERT sensing technique. The regularization method has been found to be an effective approach in coping with the inverse problem. In this work, a novel reconstruction strategy which combines the non-convex regularization method with Landweber method is proposed for the image reconstruction in ERT. At each iteration, the non-convex regularization is used to constrain the conductivity calculated with the Landweber method. A simple and efficient generalized iterated shrinkage algorithm is developed to solve the proposed method. To validate the performance of the proposed method, a series of numerical simulation is conducted and comparative analysis with other methods is performed. From the results, it can be observed that images with high quality are obtained when reconstructing with the proposed method. The impact of noise on the reconstruction is also investigated which shows that the images reconstructed by the proposed method are the least sensitive to the noise. The performance of the proposed method in the image reconstruction is also verified by experimental data. The results demonstrate that the inclusion is accurately reconstructed and the background is clear when the proposed method is adopted for the image reconstruction.     

Electrical resistance imaging of two-phase flow using direct Landweber method

In this paper, a direct Landweber method is proposed to estimate on-line resistivity distribution of two-phase flows using electrical resistance tomography. The proposed method is formulated such that the iterative Landweber method is modified for on-line computation and the resistivity distribution is estimated directly by multiplying the measured data with a weighting matrix that is computed off-line. Moreover, to improve the reconstruction performance, adaptive step-lengths for the proposed method are computed. Numerical simulations and phantom experiments have been carried out to evaluate the performance of the proposed method.     

Identification of gas/solid two-phase flow regimes using electrostatic sensors and neural-network techniques

In the gas/solid two-phase system, solid particles can accumulate a large number of electrostatic charges because of collision, friction and separation between particles or between particles and the wall. Through the detection and processing of the induced fluctuation charge signal, a measuring system can obtain two-phase flow parameters, such as flow regime, concentration and velocity. A novel methodology via introducing the characteristics of speech emotion recognition into flow regime identification is proposed for improving the recognition rate in gas/solid two-phase flow systems. Three characteristics of electrostatic fluctuation signals detected from an electrostatic sensor are extracted as the input of back propagation (BP) neural networks for flow regime identification. They are short-term average energy, Mel-frequency cepstral coefficients (MFCC) and cepstrum. The results show that the method based on each characteristic of the electrostatic fluctuation signal and BP neural networks can identify the three flow regimes of gas/solid two-phase flow in a horizontal pipe, and the identification rate of the method based on the three characteristics and BP neural networks is up to 97%, much higher than the methods based on a single characteristic.     

Extracting reference voltages from measurement voltages for oil-water two-phase flow measurement of electrical impedance tomography

To apply electrical impedance tomography (EIT) for heterogeneous two-phase flow measurement, reference voltages have to be measured, which requires the entire pipeline filled with a homogeneous medium. Such process is often time-consuming, costly and infeasible in some cases. Reference voltages acquired initially could not be used later, because of electrical conductivity change of two-phase flow caused by temperature change or chemical reaction. It will be ideal if reference voltages could be extracted from measurement voltages. In this paper, a novel multiple measurements (MMs) method is proposed by data fitting and empirical formula to establish the mapping model from measurement voltages to reference voltages, and the effectiveness of the method is proved by the oil-water two-phase flow experiment. Compared with the measurement-scale feature (MSF) and best homogeneous (BH) methods, the proposed MMs method achieves 6.38% average relative error (RE) and 4.96% average volume fraction relative error (VFRE) in the test set. The work enables the accurate estimation of the reference voltage and thus the accurate measurement of the volume fraction of oil-water two-phase flows, which will broaden the application of EIT in the field of two-phase flow measurement.     

Optical method for flow patterns discrimination,slug and pig detection in horizontal gas liquid pipe

An optical method including infrared ray and laser was developed to discriminate flow pattern, and detect liquid slug and pig in horizontal gas–liquid pipe. Based on the principle that infrared ray attenuates differently during penetrating gas and liquid, the infrared ray method was developed to discriminate flow pattern and detect liquid slug. In experiment, infrared ray was emitted on one side of the transparent pipe, and detected on the other side. Simultaneously, a signal of output voltage that is proportional to the intensity of infrared ray detected was generated and recorded. A series of experiments in horizontal air/water loop were carried out to generate bubble, stratified smooth, wavy and intermittent flow, and the output voltages under the four flow patterns were analyzed. The flow patterns can be discriminated by characteristics of output voltage. Meanwhile, the velocity and frequency of liquid slug were measured by this method, and the results were consistent with that calculated by formulas. However, infrared ray is easily affected by interface between gas and liquid, a laser method was explored to detect pig. The laser method is similar to infrared ray, a laser beam was emitted and detected and then a signal of output voltage was recorded. The results from pigging experiments show that the laser method could correctly detect the passing of a pig. The combined use of infrared ray and laser method could rightly detect pig and pigging slug during pigging operation.     

A hybrid regularization method combining Tikhonov with total variation for electrical resistance tomography

Electrical resistance tomography (ERT) is a promising measurement technique in industrial process imaging. However, image reconstruction in ERT is an ill-posed inverse problem. Regularization methods have been developed to solve the ill-posed inverse problem. Since the penalty term is a form of L2-norm, Tikhonov regularization method guarantees the stability of the solution, but it always makes the image edge oversmoothed. Total variation (TV) regularization method has good ability of preserving image edges. A hybrid regularization method, which combines Tikhonov with TV regularization method, is proposed to get better reconstructed images. The choice of the adaptive weighted parameter between TV and Tikhonov penalty term has been discussed in detail. In the proposed hybrid regularization method, the function of conductivity gradients is used as the adaptive weighted parameter to control automatically the weighting between the penalty terms from TV and Tikhonov regularization. For the model with sharp edges, the proportion of the penalty term from TV regularization is increased to preserve the edges, while for the model with smooth edges, the proportion of penalty term from Tikhonov regularization is increased to make the solution stable and robust to noise. Both simulation and experimental results of Tikhonov, TV and hybrid regularization method are shown respectively, which indicates that the hybrid regularization method can improve the reconstruction quality with sharp edges and is more robust to noise, and it is applicable for models with different edge characteristic.     

Voidage measurement of gas–liquid two-phase flow based on Capacitively Coupled Contactless Conductivity Detection

Based on Capacitively Coupled Contactless Conductivity Detection (C⁴D) technique, a new method for the voidage measurement of conductive gas–liquid two-phase flow is proposed. 15 Conductance signals, which reflect voidage distribution of gas–liquid two-phase flow, are obtained by a six-electrode C⁴D sensor. With the conductance signals, the flow pattern of gas–liquid two-phase flow is identified by flow pattern classifiers and then the voidage measurement is implemented by a corresponding voidage measurement model (for each typical flow pattern, a corresponding voidage measurement model is developed). The conductance measurement of the six-electrode C⁴D sensor is implemented by phase sensitivity demodulation (PSD) method. The flow pattern classifiers and the voidage measurement models are developed by partial least squares (PLS) technique and least squares support vector machine (LS-SVM) technique. Static voidage measurement experiments and dynamic voidage measurement experiments show that the proposed voidage measurement method is effective, the developed six-electrode C⁴D sensor is successful and the measurement accuracy is satisfactory.     

A new correlation of wet gas flow for low pressure with a vertically mounted Venturi meter

Wet gas metering has become an increasingly important technique for many industries. However, the over-reading phenomenon reduces the accuracy of Differential Pressure meters. This research fills the vacancy of correlations and presents a new correlation for low pressure between 0.82 and 1.52 MPa with a vertically mounted Venturi meter to calculate the over-reading coefficient accurately. Based on the correlational analysis, the over-reading coefficient is a function of the Lockhart-Martinelli parameter, density ratio, and gas Froude number. The constant coefficients in this correlation are obtained by nonlinear regression. Effect of low gas velocity with gas Froude number under 1.5 is taken into consideration as well. The average relative error is 1.9% and the root mean square error is 3.0%. Furthermore, a new method to calculate the over-reading coefficient for industrial applications is put forward due to the difficulties of online measurements of the Lockhart-Martinelli parameter which is substituted with the void fraction. The void fraction is calculated by an empirical correlation using quality and an approximate algorithm is utilized to obtain gas Froude number. For this new method, the average relative error is 2.3% and the root mean square error is 3.7%. This quality-based method will be helpful to resolve the limited applicability of gamma-ray attenuation for wet gas flow metering in industry regarding vertical low pressure conditions.