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.     

Doppler spectrum analysis and flow pattern identification of oil-water two-phase flow using dual-modality sensor

Horizontal oil-water two-phase flow widely exists in petroleum and chemical engineering industry, where the oil and water are usually transported together. As one of most importance process parameters to describe the two-phase flow, the flow pattern can reflect the flow characteristics of inner flow structure and phase distribution. The identification of flow pattern will contribute to develop more accurate measurement model for flow rate or phase fraction and ensure the safety and efficiency of operation in industry. A dual-modality sensor combining with continuous wave ultrasonic Doppler sensor (CWUD) and auxiliary conductance sensor, was proposed to identify flow patterns in horizontal oil-water two-phase flow. In particular, the oil-water flow characteristic was analyzed from Doppler spectrum based on the CWUD sensor. Besides, the dimensionless voltage parameter based on conductance sensor was applied to provide the information of continuous phase in the fluid. Several statistical features were directly extracted without any complicated processing algorithm from Doppler and conductance signals. The extracted features are put into a multi-classification Support Vector Machine (SVM) model to classify five oil-water flow patterns. The results show that the overall identification accuraccy of 94.74% is satisfactory for horizontal oil-water two-phase flow. It also demonstrates that the noninvasive ultrasonic Doppler technique not only can be used for flow velocity measurement but also for flow pattern identification.     

Three-dimensional visualisation of gas-water two-phase flow based on bubble mapping method and size projection algorithm

Electrical impedance tomography (EIT) has been successfully applied on gas-water flow applications, but it is incapable to identify small bubbles or the sharp gas-water interface of a large bubble due to its relatively low spatial resolution. A new visualisation approach, bubble mapping method (BM3D), offers a good 3D visualisation of bubble size and distribution. However, the empirical thresholding value method used in BM3D might meet a challenging from various flow setups and conditions in practice. Recently, the size projection algorithm (SPA) was proposed to determine the closest thresholding value for each frame of tomogram by minimising projection error. In this paper, the performances of BM3D and SPA methods are individually analysed and evaluated. Then a new method based on the combination of BM3D and SPA methods is reported to achieve better visualisation of gas-water flow, where the SPA is employed to determine the optimised thresholding values for BM3D method. Experiments are conducted to evaluate the proposed combination method for typical gas-water pipeline flow regimes, including horizontal stratified, bubble, plug, slug, annular flow regimes and vertical bubble, slug, annular flow regimes. The results are compared with the BM3D method, colour mapping method, and high-speed camera video recorded from a transparent chamber. A brief discussion on the effects of reconstruction algorithms and thresholding value for horizontal and vertical flows visualisation is also given.     

Flow pattern identification for gas-oil two-phase flow based on a virtual capacitance tomography sensor and numerical simulation

Gas-oil two-phase flow is widely encountered in oil exploitation and transportation pipelines. It's complex and transient changes of flow regimes present a great challenge for accurate and real-time measurement. As a non-invasion and real-time measuring method, electrical capacitance tomography (ECT) is suitable for the transient measurement of non-conductive gas-oil flow. However, the highly random and nonlinear nature of multiphase flow make it difficult and limited to investigate the flow parameters based on either static or dynamic measurement. In this research, the whole process of dynamic measurement of ECT applying in gas-oil two-phase flow is thoroughly studied, including simulation calculation, experimental validation and comprehensive data analysis. A simulation approach by coupling the flow and electrostatic field is proposed based on a virtual ECT sensor, in order to monitor the gas-oil two-phase flow characteristics. Based on FLUENT and COMSOL platform, the numerical simulation under six typical flow patterns in a horizontal pipe is carried out. Combining the visualized image generated by ECT measurement and the theory of flow pattern transition, the formation mechanism and structural characteristics of different gas-oil flow patterns are analyzed in detail. Furthermore, this research attempts to analyze the signal fluctuation characteristics caused by flow pattern change, in order to access more in-depth flow information implied in the original capacitance data, via time-series analysis as well as frequency domain analysis based on Flourier Transform. At last, a series of dynamic experiment is conducted to verify the feasibility of the simulation and data analysis approach. The experiment focuses on the flow pattern transition, gas-liquid dynamic characteristics and noise influence in the actual process. It can be concluded from the results of simulation and experiment tests, combining the visualized images and the dynamic characteristics of capacitance signals can make it more effective and intuitive for flow pattern identification, which might be used for the online measurement in real-industry process.     

Effects of flow patterns and salinity on water holdup measurement of oil-water two-phase flow using a conductance method

This research investigates the effects of flow pattern and salinity of oil-water two-phase flow on water holdup measurement using a conductance method. Firstly, vertical upward oil-water two-phase flow experiment is conducted in a 20 mm inner diameter (ID) pipe, in which the salinities of aqueous solutions are set as 151 ppm, 1003 ppm, 2494 ppm and 4991 ppm respectively. Experimental water-cut and mixture velocity are set as 80–100% and 0.0184–0.2576 m/s. In the experiment, three different flow patterns, i.e., dispersed oil-in-water slug flow (D OS/W), dispersed oil-in-water flow (D O/W) and very fine dispersed oil-in-water flow (VFD O/W) are observed and recorded by a high speed camera. Meanwhile, we collect the response of Vertical Multiple Electrode Array (VMEA) conductance sensor excited by a sine voltage signal. The result shows that, for VFD O/W, the water holdup from VMEA sensor shows a satisfied agreement with that of quick closing valve (QCV) method under certain salinities, i.e., 1003 ppm as well as 2494 ppm. For D OS/W flow and D O/W flow characterized by dispersed oil droplets with various sizes, considerable deviations of water holdup between VMEA sensor and QCV method under four kinds of salinity aforementioned are presented. Afterward, according to experimental analysis along with theoretical deviation, it is concluded that the deviation of the measurement system reaches its minimum when reference resistance in the measurement circuit and salinity of the aqueous solution satisfy constraint conditions, and the accuracy of water holdup using the conductance method can be improved through adjusting reference resistance to match the salinity of water phase. Finally, the recurrence plot algorithm is utilized to identify typical flow patterns mentioned above and it shows satisfied results on comprehending the discrepancies among different flow patterns, demonstrating that the recurrence plot algorithm can be effectively applied in flow pattern identification regarding oil-water flows.     

基于密度-距离的t混合模型流式数据聚类

传统流式数据采用人工设门法分析,效率低下且依赖于专家。近几年,很多自动流式数据聚类算法纷纷被提出,然而针对数据量不多且分布稀疏的小样本类群始终没有很好的解决办法。提出了一种基于密度-距离的t-混合模型流式数据聚类优化方法,能够较好地解决小样本类群区分困难的问题。该方法通过密度-距离中心算法定位各类群的初始中心,作为t-混合算法的初值对样本数据进行处理,通过最大似然估计求出各类群对应的样本数目,从而实现样本聚类。实验表明,与经典模型算法相比,基于密度-距离的t-混合模型优化算法具有更好的稳定性和可靠性,对小样本类群以及混叠的类群具有较强的适应能力。     

Flow measurement based on the combination of swirler and differential pressure under slug flow

The alternating appearance of elongated bubbles and liquid slugs of slug flow in the pipe causes severe pressure fluctuation. As a result, measuring the flow rate of the slug flow with the throttling unit based differential pressure method is difficult. This paper investigates a new swirler-based flow measurement method in slug flow. The swirler converts the slug flow into a swirling annular flow, and the differential pressure method is used to measure the flow rate. The influences of gas and liquid flow rates on the differential pressure ΔP_X across the swirler as well as its downstream axial differential pressure ΔP_Z are investigated. ΔP_X^0.5 increases linearly as the liquid mass flow rate increases, and the slope of the curve increases as the gas mass flow rate increases. The influence of gas mass flow rate on ΔP_X^0.5 is comparable to that of liquid mass flow rate on ΔP_X^0.5. ΔP_Z^0.5 increases linearly with increasing gas/liquid mass flow rate, and the slope of the curve of ΔP_Z^0.5 with m_l differs slightly from the slope of the curve in single-phase water conditions. Based on the research presented above, new empirical correlations of mass flow rate based on ΔP_X and ΔP_Z are established respectively. The superficial liquid velocity ranges from 0.6 to 2 m per second, while the superficial gas velocity ranges from 2 to 6 m per second. If the gas mass flow rate and ΔP_X are known, the relative error of liquid mass flow is less than 3%. The relative error of the gas mass flow rate is less than 10% if the liquid mass flow rate and ΔP_X are given. The calculation accuracy of the flow measurement model using ΔP_X is better than the calculation accuracy of the flow measurement model using ΔP_Z.     

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％。     

基于模态区间的数控机床切削状态监测

随着高速高精数控加工技术的发展,对数控机床切削加工状态的稳定性提出了更高的要求,传统的切削加工状态监测方法中对不确定性处理存在不足。提出了一个基于模态区间的切削状态监测不确定性处理方法,利用模态区间的宽度对传统监测方法中的不确定性加以表述,以解决监测中的不确定性问题。为了验证提出方法的有效性,搭建了切削加工实验平台,通过加速度传感器获取数控机床切削加工信息,由时频分析方法将切削状态划分成稳定、过渡及颤振3个加工阶段,利用基于模态区间的小波包能量百分比方法,提取不同加工阶段的区间特征量,通过Lloyd算法进行编码后作为基于模态区间的广义隐马尔科夫模型的输入特征向量,最后利用广义隐马尔科夫状态辨识方法,对数据机床切削状态进行了识别。实验结果表明,基于模态区间的广义隐马尔科夫模型辨识方法优于传统的隐马尔科夫模型辨识方法。     

Simulation and analysis of grinding wheel based on Gaussian mixture model

This article presents an application of numerical simulation technique for the generation and analysis of the grinding wheel surface topographies. The ZETA 20 imaging and metrology microscope is employed to measure the surface topographies. The Gaussian mixture model (GMM) is used to transform the measured non-Gaussian field to Gaussian fields, and the simulated topographies are generated. Some numerical examples are used to illustrate the viability of the method. It shows that the simulated grinding wheel topographies are similar with the measured and can be effective used to study the abrasive grains and grinding mechanism.     

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.     

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.     

基于FLUENT数值模拟的离心泵内冰浆两相流流动特性分析

针对冰浆两相流在离心泵的流动特性问题,基于欧拉法建立冰浆Mixture两相流模型,通过FLUENT软件对冰浆流动特性进行数值模拟,得到了在不同流量工况下的离心泵内部压力场、速度场以及冰晶颗粒分布特性.多次数值计算,获得离心泵在输送含冰率为10％的冰浆时的性能特性曲线,并与该离心泵在输送清水时的性能曲线进行了对比分析.研...     

Flow regime identification of steam-water two-phase flow using optical probes,based on local parameters in vertical tube bundles

Flow regime identification based on local parameters of axial upward two-phase flow in vertical tube bundles, at high-temperature and high-pressure, was performed using optical probes. A staggered arrangement of the tube bundles was simulated inside a non-circular test channel, the tube size and pitch are same as that in a real steam generator of a PWR under design. Optical probes were utilized to acquire the void fraction, interface frequency, and fluctuation characteristics of the local void fraction at two typical locations (centroid of the three tubes, named op-1, and centre of the minimum gap between two tubes, named op-2). The system pressure ranged from 5 to 9 MPa, mass flux from 100 to 350 kg m⁻² s⁻¹, thermodynamic steam quality from 0 to 1, and inlet fluid temperature from 263.9 to 303.3 °C, depending on the saturation pressure. This study investigated local parameters and flow pattern characteristics of high-pressure steam-water two-phase flow in vertical tube bundles using optical probes, with the measurement error of less than 2%. Results showed that local void fraction at op-1 was much larger than that at op-2, and the local void fraction difference between op-1 and op-2 increased first and then gradually decreased, which was primarily affected by the transition in flow regimes. The flow pattern characteristics of steam-water two-phase flow were described based on three aspects, namely, variation in interface frequency with local void fraction, fluctuation characteristics of local void fraction, and statistical analysis of local void fraction deviating from the average. Additionally, the flow regime identification criteria, applicable to the steam-water two-phase flow in vertical tube bundles, were proposed based on local parameters.     

Coupled model of dual differential pressure (DDP) for two-phase flow measurement based on phase-isolation method

Phase-isolation is a novel ever-increasing multiphase separation technology, which can facilitate the multiphase fluid flowing concurrently with a substantially clear interface between two phases, and the phenomenon is promisingly employed for the separation and measurement of multiphase flows. Phase-isolation can be implemented by different kinds of lateral forces, of which the centrifugal force induced by the swirlers is the most convenient method. The radial pressure drop between pipe wall and pipe center, and the axial pressure drop along the pipe wall occurs at the downstream of the swirler. In the paper, the coupling model of dual differential pressure (DDP) including the radial-axial differential pressure and radial-radial differential pressure was built employing centrifugal phase-isolation for oil-water two-phase flow, and the theoretical measurement models were validated by our experimental data. At certain cross sections downstream of the swirler, the deviations between theoretical and experimental result of the volumetric oil fraction λ_o and mass flowrate Q_m were below ±7.16% and ±1.14% respectively when the radial-axial differential pressure was adopted, while the deviations between theoretical and experimental result of λ_o and Q_m were below ±6.91% and ±1.13% respectively using the radial-radial differential pressure. The acceptable deviation indicates that the DDP model can be the reference for the analysis and application of two-phase flow in the academic research and practical engineering.     

基于Fluent的超薄连续铸轧三维流场仿真分析

基于计算流体软件Fluent,比较全面地分析双辊超薄连续铸轧的流场,特别是铸嘴型腔三维流动现象及其特点.分析铸轧区对流场的作用.铸轧区有利于铸嘴出口速度的均匀流动,有利于速度均匀混合,但不能彻底消除速度梯度.把计算的铸嘴出口处的流体速度与实验结果相比较,两者基本吻合,并得出结论:分流块的形状与位置是影响铸轧流场的主要原因之一,常规铸轧常用铸嘴熔体出口速度相对误差达38%,不能满足超薄连续铸轧技术的需要.     

基于oldroyd-B本构模型的磨料流加工壁面滑移仿真分析

基于oldroyd-B粘弹性本构模型,应用POLYFLOW软件对流体磨料在圆管中的壁面滑移状态进行了模拟仿真。将得到的圆管中流体磨料的压力值与磨料流加工机床上测试点的压力值进行比较,得到二者的相对误差不超过5%,验证了模拟仿真的可行性。通过仿真可知,流体磨料在工件壁面上的滑移存在速度临界点。通过比较不同管道入口流量、流体磨料黏度和壁面滑移系数对壁面滑移速度的影响可知,当管道入口流量越大、流体磨料的黏度越高,以及壁面滑移系数越小时,加工过程中的壁面滑移速度越大。     

基于 CEEMDAN 和概率神经网络的 起伏振动气液两相流型识别

起伏振动气液两相流型准确识别对漂浮核动力平台安全稳定运行有重要意义。通过对比静止和起伏振动管道的压差信号以及对应的频谱图发现,起伏振动管道内的压差信号波动幅度更大且包含更多的频率分量,两种流型均含有主频率,该频率为起伏振动频率。针对起伏振动状态气液两相流压差信号的复杂性,分别采用自适应白噪声的完备总体经验模态分解(CEEMDAN)和集合经验模态分解(EEMD)对小波降噪后的压差信号进行模式分解,发现CEEMDAN能够在减少模式分量的同时获得更多有效的分量。通过计算spearman相关系数选择具有表征意义的IMF分量进行Hilbert变换计算能量作为特征值,采用概率神经网络对流型进行识别。结果表明,采用CEEMDAN进行模式分解结合概率神经网络的识别方法准确率达到95.83%,能够用于起伏振动下气液两相流型识别。