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.     

Identification of two-phase flow regime using ultrasonic phased array

Flow regime is one of the key characteristics of gas-liquid two-phase pipe-flows and its identification is essential for several industrial applications. In this paper, the ultrasonic phased array technology is used to identify flow regimes of two-phase (air-water) vertical flow. The ultrasonic phased array can perform multi-point, omnidirectional detection to obtain high-resolution data suitable for image processing. The scanned images, which have distinctive features, are subjected to a series of image-treatment techniques, such as principle component analysis, to extract information necessary for flow regime identification. The K-nearest neighbors (KNN) classification algorithm is then used to identify flow regimes with high accuracy.     

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.     

液固两相流压降规律及超声法压降测量

液固两相流广泛存在于能源动力、石油化工等工业过程,两相流压降作为重要的流动参数,有助于流动建模及流态分析。建立液固两相压降测量模型,提出了一种结合超声多普勒及超声透射衰减的液固两相超声压降测量方法。搭建液固两相流动实验平台,对两相压降规律进行研究。两相混合流速和固相体积分数升高时,液固两相压降均逐渐增加。在固相体积分数为0.28%～1.37%,两相混合流速为0.9～1.65 m/s时,根据液固两相压降测量模型及Churchill模型的超声法得到的两相压降与差压传感器测量的压降平均相对误差为4.93%和5.10%,验证了测量模型的准确性。针对非均匀分布的两相流态进行压降测量,进一步拓展了压降测量模型的应用范围。本研究工作为非侵入超声法测量液固两相压降提供了方法基础。     

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.     

基于槽式孔板的凝析天然气计量技术

介绍了一种新型气液两相流量传感器——槽式孔板的结构特点和工作原理，并将其应用于凝析天然气计量技术研究。结合实验数据和理论模型详细分析了影响槽式孔板两相压降倍率的各种因素，利用曲面拟合技术给出了传感器两相压降倍率与压力、气体富劳德准数、Lockhart-Martinelli参数之间的相关式，该相关式计算精度可以满足生产计量的精度要求，为低含液率的凝析天然气流量计研制奠定了基础。     

A study on the relationship between upstream and downstream conditions in swirling two-phase flow

Inline fluid separation is a concept, which is used in the oil and gas industry. Inline fluid separators typically have a static design and hence changing inlet conditions lead to less efficient phase separation. For introducing flow control into such a device, additional information is needed about the relationship of upstream and downstream conditions. This paper introduces a study on this relationship for gas/liquid two-phase flow. The downstream gas core development was analyzed for horizontal device installation in dependence of the inlet gas and liquid flow rates. A wire-mesh sensor was used for determining two-phase flow parameters upstream and a high-speed video camera to obtain core parameters downstream the swirling device. For higher accuracy of the calculated void fraction, a novel method for wire-mesh sensor data analysis has been implemented. Experimental results have shown that void fraction data of the wire-mesh sensor can be used to predict the downstream behavior for a majority of the investigated cases. Additionally, the upstream flow pattern has an impact on the stability of the gas core downstream which was determined by means of experimental data analysis.     

气液环状流周向液膜测量传感器的优化设计

以气液两相环状流管道横截面的周向液膜为测量对象,采用单台高速摄像机和平面反射镜组构建了虚拟双视角的视觉传感器,并对传感器进行了优化。基于虚拟双目立体视觉原理建立虚拟双视角视觉传感器测量模型。为了尽可能增大有效拍摄视角以获得更多液膜流动信息,综合考虑视场区域、传感器尺寸、测量距离以及管道光路折射等因素,对虚拟双视角视觉传感器模型进行了分析和设计,优化了传感器模型的结构参数。理论分析及实验结果表明:优化后的虚拟双视角视觉传感器可以获得近300°的有效周向测量视角,远远优于使用单台高速摄像机进行直接拍摄。该项研究为通过双视角视觉传感器进行气液两相环状流周向液膜的实时测量提供了理论基础,对研究液膜厚度和分析环状流流动状态具有重要意义。     

High-resolution gas–oil two-phase flow visualization with a capacitance wire-mesh sensor

The application of a novel wire-mesh sensor based on electrical capacitance (permittivity) measurements for the investigation of gas–oil two-phase flow in a vertical pipe of 67 mm diameter under industrial operating conditions is reported in this article. The wire-mesh sensor employed can be operated at up to 5000 frames per second acquisition speed and at a spatial resolution of 2.8 mm. By varying the gas and liquid flow rates, different flow patterns, such as bubbly, slug and churn flow, were produced and investigated. From the images of gas void fraction distribution, quantitative flow structure information, such as time series of cross-sectional void fraction, radial void fraction profiles and bubble size distributions, was extracted by special image-processing algorithms.     

Electrical capacitance tomography for gas–solids flow measurement for circulating fluidized beds

Gas–solids flows in the risers of circulating fluidised beds (CFBs) and cyclones exhibit complex physical behaviour, such as local backflow and recirculation. The difficulties in accurate measurement of gas–solids flows stem from various flow regimes, which exist in multi-phase flows in pipelines and vessels. It is necessary to investigate the solids’ fraction profile, flow regime identification, image reconstruction, flow acceleration and flow velocity. Electrical capacitance tomography (ECT) is regarded to be a successful technology for imaging industrial processes containing dielectric materials. ECT would help understanding of gas–particle interaction, particle–boundary interaction and the influence of gas on the solids’ flow turbulence.The first part of this paper covers some new developments in ECT, i.e., algorithms for 3D image presentation and on-line iterative image reconstruction. The second part presents a novel non-intrusive technique for measuring axial and angular velocities. Theoretical and experimental studies, carried out using cross-correlation techniques in a cyclone separator dipleg, confirm the feasibility of on-line velocity measurement. Experimental results from various gas–solids flow facilities, CFB and cyclone, are presented.     

Two-phase flow regimes for counter-current air-water flows in narrow rectangular channels

A study of counter-current two-phase flow in narrow rectangular channels has been performed. Two-phase flow regimes were experimentally investigated in a 760 mm long and 100 mm wide test section with 2.0 and 5.0 mm gap widths. The resulting flow regime maps were compared with the existing transition criteria. The experimental data and the transition criteria of the models showed relatively good agreement. However, the discrepancies between the experimental data and the model predictions of the flow regime transition became pronounced as the gap width increased. As the gap width increased the transition gas superficial velocities increased. The critical void fraction for the bubbly-to-slug transition was observed to be about 0.25. The two-phase distribution parameter for the slug flow was larger for the narrower channel. The uncertainties in the distribution parameter could lead to a disagreement in slug-to-churn transition between the experimental findings and the transition criteria. For the transition from churn to annular flow the effect of liquid superficial velocity was found to be insignificant.     

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.     

Theoretical study of vertical slug flow measurement by data fusion from electromagnetic flowmeter and electrical resistance tomography

Electrical resistance tomography (ERT) can be used to obtain the conductivity distribution or the phase distribution of gas/liquid flows (e.g. slug flow). Using proper parameter models and flow regime identification models, the measurement of phase size, void fraction, and pattern recognition can be realized. Electromagnetic flowmeters have been used to measure conductive single-phase liquid flows. However, neither ERT nor electromagnetic flowmeters (EMF) can provide accurate measurement of gas/liquid two-phase flows. This paper presents an approach to fuse the information from ERT and an electromagnetic flowmeter. A model for the measurement signal from the electromagnetic flowmeter has been developed based on the flow pattern and the phase distributions, which are obtained from the reconstructed images of ERT, aiming to reduce the measurement error of the electromagnetic flowmeter and enhance the measurement accuracy. Through the simulation research of virtual current density distribution, the feasibility of fusion of electromagnetic flowmeter and ERT to measure gas/liquid two-phase vertical slug flow is verified. By theoretical analysis, the relationship between the output of electromagnetic flowmeter and flow parameters is established. The electrical potential difference of the electromagnetic flowmeter, average velocity, volume flow rate and gas void fraction between the bubble size and location are also investigated. The fusion approach can be used to measure vertical slug flows.     

IDENTIFICATION OF GAS–SOLID TWO-PHASE FLOW REGIMES USING HILBERT–HUANG TRANSFORM AND NEURAL-NETWORK TECHNIQUES

This work presents a new methodology for flow regime identification in a gas–solid two-phase flow system. The approach of identification employs the artificial neural network (ANN) technique, considering the applications with electrostatic sensor as a measuring device and Hilbert–Huang transformation (HHT) as the post-processing method. The electrostatic fluctuation signals detected from an electrostatic sensor are processed using HHT to gain the Hilbert marginal spectrums. Then four characteristic parameters of the marginal spectra are extracted as the input of BP neural network for flow regime identification. They are subband energy (SE), first-order difference of subband energy (DSE), subband energy cepstrum coefficients (SECC), and first-order difference of the subband energy cepstrum coefficients (DSECC). The results show that the characteristic parameters of the Hilbert marginal spectrum of the electrostatic signal can identify the three flow regimes of gas–solid two-phase flow in a horizontal pipe, especially the DSECC.     

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.     

Precise gamma based two-phase flow meter using frequency feature extraction and only one detector

Flow regime information can be used to enhance measurement accuracy of flowmeters. Void fraction measurement and regime identification of two-phase flows including, liquid and gas phases are crucial issues in oil and gas industries. In this study, three different regimes including annular, stratified and homogeneous in the range of 5%–90% void fractions, were simulated by Monte Carlo N-Particle (MCNP) Code. In simulated structure, a Cesium 137 source and only one NaI detector were used to record received transmitted photons. Fast Fourier Transform (FFT) was applied to the registered signals of the detector in order to analyze in the frequency domain. Several features of signals in the frequency domain were extracted. These features were the average value of fast Fourier transform, the amplitude of dominant frequency, variance, Kurtosis and RMS (root mean square). Different combinations of these features were investigated in order to find the best features with the best separation ability for using as the inputs of Artificial Neural Network (ANNs). Two different Multi-Layer Perceptron (MLP) neural networks were used to recognize flow regimes and predict the void fraction. In regime identification procedure, all of the three mentioned regimes were recognized correctly and in the volume fractions prediction procedure, the void fraction was also estimated with a Mean Relative Error (MRE) percentage of less than 0.5%. In all of the previous studies, at least two detectors were used. Using the proposed method in this paper, number of detectors was reduced to one.     

Electromagnetic inspection of a two-phase flow of GaInSn and argon

In the continuous casting process, an adequate control of liquid steel flow through the submerged entry nozzle is essential for maintaining steel cleanliness and ensuring good surface quality in downstream processing. Monitoring the flow in the nozzle presents a challenge for the instrumentation system because of the high temperature environment and the limited access to the nozzle in between the tundish and the mould.In this paper, the distribution of a two-phase liquid metal/gas flow is studied by using a liquid metal laboratory model of an industrial steel caster and an inductive sensor array. The experiments were performed with the liquid eutectic alloy GaInSn as an analogue for liquid steel, which has similar conductive properties as molten steel and allows the measurements at room temperature. A scaled (approx. 1:10) experimental rig consisting of a tundish, a stopper rod, a nozzle and a mould was used. Argon gas was injected through the centre of the stopper rod and the behavior of two-phase GaInSn/argon flows was studied.The electromagnetic system used in the experiments to monitor the behavior of two-phase GaInSn/argon flows consists of an array of 8 equally spaced inductive coils arranged around the object, a data acquisition system and a host computer. The present system operates at 10 kHz and has a capture rate of 10 frames per second.The results show clearly that the injection of the argon gas is distinguishable from the single phase flow by observing the appearance of oscillation patterns. These oscillations become more dominant with the increase of the argon flow. In some cases two main oscillation patterns were present in the raw signals. In general, the signals and the reconstructed void fractions in the nozzles are highly correlated with the observed oscillations of the level height in the mould and the pressure in the nozzle.     

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.     

Miniature conductivity wire-mesh sensor for gas-liquid two-phase flow measurement

A miniature conductivity wire-mesh sensor for gas-liquid two-phase flow measurement in small channels is presented. The sensor design is similar to the conventional wire-mesh sensor for larger flow cross sections with wire electrodes stretched across the flow channel in two adjacent planes and with perpendicular wire orientation between planes. Conductivity measurement is performed by special electronics which consecutively applies bipolar voltage pulse excitation to the sender wires and measures electrical current flow in the wire crossings at the receiver wires. The new design is based on printed circuit board technology. A prototypical sensor made of 2×16 stainless steel wires each of 50 μm diameter was manufactured and applied to two-phase flow measurement inside the mixing chamber of an effervescent atomizer. Accuracy of the sensor was studied for static liquid distributions using microphotography and for dynamic two-phase flow by comparison of wire-mesh sensor data with radial gas fraction profiles obtained from X-ray microtomography measurements.     

Heat transfer correlations for air-water two-phase flow of different flow patterns in a horizontal pipe

Heat transfer coefficients were measured and new correlations were developed for two-phase heat transfer in a horizontal pipe for different flow patterns. Flow patterns were observed in a transparent circular pipe (2.54 cm I. D. and L/D=96) using an air/water mixture. Visual identification of the flow patterns was supplemented with photographic data, and the results were plotted on the How regime map proposed by Taitel and Dukler and agreed quite well with each other. A two-phase heat transfer experimental setup was built for this study and a total of 150 two-phase heat transfer data with different flow patterns were obtained under a uniform wail heat 11 ux boundary condition. For these data, the superficial Reynolds number ranged from 640 to 35,500 for the liquid and from 540 to 21,200 for the gas. Our previously developed robust two-phase heat transfer correlation for a vertical pipe with modified constants predicted the horizontal pipe air-water heat transfer experimental data with good accuracy. Overall the proposed correlations predicted the data with a mean deviation of 1.0% and an rms deviation of 12%.