基于Hilbert-Huang变换和支持向量机的油水两相流流型识别

针对油水两相流的测量难题，利用文丘里管对水平管内油水两相流流型进行了研究。基于差压波动信号，提出了Hilbert-Huang变换与支持向量机相结合的流型识别方法。首先计算差压波动信号的均方根，并对其进行归一化处理后作为表征流型的特征向量之一；然后对差压信号进行Hilbert-Huang变换，利用经验模态分解后的多分辨率特征，提取第一层和第二层的能量比作为表征流型的另外两个特征向量；最后利用训练好的支持向量机进行流型识别，对分层流型及分散流型取得了较好的流型识别效果。如果将流型识别与推导得到的文丘里油水两相流流量测量模型相结合，可以较好地实现油水两相流的流量测量。     

HHT与神经网络在油气两相流流型识别中的应用

引　言两相流动广泛存在于自然界和许多工业过程中 ,如电力、能源、石油、核工业等 .两相流动中介质的几何分布状况 ,即流型 ,极大地影响着两相流动压力损失、传热特性、流量测量的准确性和流动系统的运动特性 .流型的识别方法研究是目前国际多相流研究领域的热点问题之一[1,2 ] .利用神经网络识别流型 ,需要解决两个问题 :一是特征向量的选取 ;二是神经网络的选择 .其中前者更加重要 .Embrechts等[3] 采用Kohonen网络模型 ,把差压波动信号的傅氏变换系数和正交小波　变换后的小波系数作为网络的输入 ,对水平管内的两相流流型进行辨识 .Mi…     

小流量高扬程离心漩涡泵气液混输的设计和试验研究

The design method of small-flow high-head centrifugal-vortex pump was presented. This pump, configured with inducer, complex-centrifugal impeller and open-vortex impeller, was put forward to deliver gas-liquid two-phase mixture. An HTB-5/60 type sample pump was developed and tested on a closed-loop test rig. Experimental studies on performance and cavitation tests for gas-liquid two-phase mixture were carried out compared with pure-water experimental results. Also the effect of gas phase on pump was analyzed and discussed. The experimental results show that performance and cavitation characteristics of the sample purnp deteriorates progressively with increasing volume fraction of gas. When the total capacity Qm is between 4.5 m^3·h^-1 and 6 m^3·h^-1 and the gas flow rate qg below 0.66 m^3·h^-1, or qg/Qm is lower than 15%, the characteristic curves are approximately parallel to those in pure water test, but the performance deteriorates sharply until an abrupt flow-cutting at a critical volume fraction of gas. This pump is found suitable for transporting gas-liquid two-phase mixture when working around rated capacity of 5 m^3·h^-1 with qglQm below 15%.     

气液两相流差压波动信号的Hilbert-Huang变换特性

将Hilbert-Huang（HHT）变换应用于中小管径水平管气液两相流差压波动信号的分析.利用经验模态分解（EMD）方法把信号分解成多个内模函数(IMF),再对各内模函数进行Hilbert变换,得到信号的Hilbert谱,并提取各内模函数的信号能量特征.在此基础上比较了不同管径下信号分析的结果,分析了不同频段的能量分布与流型变迁的关系,并讨论了小波变换和HHT两种信号处理方法各自的特点.实验结果表明,HHT可以用于非线性非平稳信号的分析,提取的能量特征值反映了水平管气液两相流的流动状态和流型的变迁,该方法为水平管气液两相流差压波动信号的研究提供了借鉴.     

基于双槽式孔板组合和神经网络技术的凝析天然气流量计开发

A slotted orifice has many superiorities over a standard orifice. For single-phase flow measurement, its flow coefficient is insensitive to the upstream velocity profile. For two phase flow measurement, various characteristics of its differential pressure （DP） are stable and closely correlated with the mass flow rate of gas and liquid. The complex relationships between the signal features and the two-phase flow rate are established through the use of a back propagation （BP） neural network. Experiments were carried out in the horizontal tubes with 50ram inner diameter, ooerated with water flow rate in the range of 0.2m^3·h^-1 to 4m3·h^-1, gas flow rate in the range of 100m^3·h^-1 to 1000m^3·h^-1, and pressure at 400kPa and 850kPa respectively, where the temperature is ambient temperature. This article includes the principle of wet gas meter development, the experimental matrix, the signal processing techniques and the achieved results. On the basis of the results it is suggested that the slotted orifice couple with a trained neural network may provide a simple but efficient solution to the wet gas meter development.     

槽式孔板气液二相流差压信号的时间序列分析

槽式孔板是一种新型的气液二相流流量传感器,为了研究其二相流测量特性,提出了一种新的信号处理方法。首先应用小波变换模极大值滤波方法对差压测量信号去噪,然后对滤波结果进行经验模式分解得到有限个经验模式函数(IMF),进而对IMF建立自回归(AR)模型,研究了AR模型的参数与气液二相流流型以及分相流量之间的关系。这种信号处理方法能够使具有非平稳特性的差压信号满足时间序列分析的建模条件,AR模型的参数可以有效地区分气液二相流流型,并与气液分相流量的变化密切相关,为气液二相流流型识别和流量计量算法的研究奠定了基础。     

负压差立管气固流动的不稳定性实验分析

负压差立管内气固两相流具有流动的不稳定性,比较典型的是颗粒流量的不稳定性. 实验表明这种不稳定性主要有两种形式,一种是流量偏移,流量从一个值逐渐或突然转变为另一个值,这种现象一般发生在立管的入口或出口,是颗粒失流化架桥产生的;另一种是流量振荡,流量在一定的范围内发生波动变化,即低频脉动流动,这是颗粒逆压力梯度流动压缩气体造成的. 负压差立管气固流动的不稳定性对工艺过程的运行具有潜在的危害性.     

基于神经网络的两相流流型识别方法研究

采集了水平管内气水两相流动的差压信号,利用概率密度函数(PDF)对差压信号特征进行了分析;定义了PDF的四个特征参数,即PDF波峰个数K1、波峰峰值K2、波峰位置K3、以及PDF的方差K4来反映流型的特征。运用四个参数构成的特征向量对径向基函数(RBF)神经网络进行训练并识别流型,结果表明,该方法具有识别速度快、准确率高的特点,从而为两相流的流型识别提供了一种有效的手段。     

基于EMD和ICA的AR模型在两相

为了对气液两相流流型进行准确识别,提出了一种基于经验模态分解（EMD）和独立成分分析（ICA）的AR模型的流型识别方法。该方法首先提取气液两相流动态差压信号,通过EMD技术对其进行分解,对分解出来的高频模态进行小波包消噪,然后通过ICA技术实现原始信号的降维处理。对通过ICA得到的独立分量建立AR模型,将模型参数和残差方差作为特征向量,建立不同流型的模板向量。计算未知流型信号的特征向量与模板特征向量的综合Mahalanobis距离,通过比较各判别距离的大小得到流型识别的结果。对40 mm水平管气水两相流进行实验,利用文丘里管采集动态差压信号,采用上述处理过程可以对泡状流、弹状流、塞状流进行有效识别,识别率达94.3％。该方法受环境条件影响小,可以有效滤除信号中的噪声成分,识别率高,易于工程实现。     

Study on gas-liquid flow characteristics in stirred tank with dual-impeller based on CFD-PBM coupled model

Study on gas-liquid flow in stirred tank with two combinations of dual-impeller (six-bent-bladed turbine(6BT)+six-inclined-blade down-pumping turbine (6ITD),the six-bent-bladed turbine (6BT)+six-inclined-blade up-pumping turbine (6ITU)) was conducted using computational fluid dynamics (CFD) and popula-tion balance model (PBM) (CFD-PBM) coupled model.The local bubble size was captured by particle image velocimetry (PIV) measurement.The gas holdup,bubble size distribution and gas-liquid interfacial area were explored at different conditions through numerical simulation.The results showed that the 4 mm bubbles accounted for the largest proportion of 33％ at the gas flow rates Q =0.76 m3·h-1 and 22％ at Q =1.52 m3·h-1 for combined impeller of 6BT + 6ITU,while the bubbles of 4.7 mm and 5.5 mm were the largest proportion for 6BT + 6ITD combination,i.e.25％ at Q =0.76 m3·h-1 and 22％ at Q =1.52 m3·h-1,respectively,which indicated that 6BT + 6ITU could reduce bubble size effectively and promote gas dispersion.In addition,the gas holdup around impellers was increased obviously with the speed compared with gas flow rate.So it was concluded that 6ITU impeller could be more conductive to the bubble dispersion with more uniform bubble size,which embodied the advantages of 6BT + 6ITU combination in gas-liquid mixing.     

基于距离评估的气液二相流流型识别方法

为了克服气液二相流特征融合后不相关特征过多的问题,提出了基于距离评估和支持向量机(SVM)的气液二相流流型识别方法。首先利用经验模式分解和小波包方法对原始的压差波动信号进行分解,分别提取原始信号和各分解信号的时域特征参数组成融合特征,然后采用距离评估方法对融合特征进行评估,根据距离评估因子的大小挑选出敏感特征作为SVM的输入,进而实现对流型的自动识别。水平管内空气-水二相流流型识别结果表明:该方法能够准确获取流型的敏感特征,减小运算规模,提高识别准确率。     

基于子波能量特征的气液两相流流型辨识方法

气液两相流的流型影响着两相流的流动特性和传热特性，同时也影响着流动参数的准确测量以及两相流系统的运行特性。针对压差信号的非平稳和非线性特点，尝试利用Hilbert-Huang变换(HHT)和小波包分解对差压波动信号进行信号处理，进而建立流型的子波能量（IMF能量和小波包能量）特征，并以此特征向量作为Elman神经网络的输入量，从而实现对流型的智能识别。实验结果表明：这两种特征向量与Elman神经网络结合都能够较准确地识别出4种流型，并且各自都有不同的优缺点。另外与BP神经网络相比，采用Elman神经网络进行流型识别可以获得更高的识别率。     

基于图像动态纹理特征的气固流化床流型识别

准确识别流型是气固流化床二相流参数检测的重要内容,文中提出一种基于图像光流法和动态纹理特征相 结合的气固流化床流型识别的新方法.实验是在气固流化床二相流实验系统上利用高速摄影系统获取流型图像.流型图像分别为鼓泡床,节涌床,湍动床,快速流化床,稀相输送等5种典型流型.首先对获取的不同流型图像分别进行去噪和对比度拉伸...     

气液固三相并流系统流型的混沌识别

运用确定性混沌分析技术，研究了气液固三相并流系统散式鼓泡流、聚式鼓泡流、柱塞流、泡沫流及环状流压力波动信号的混沌动力学行为。结果表明，吸引子可以用来表征气液固三相并流系统的动力学行为，混沌特征参数相关维D2和K熵可以用来定量识别以上五种流型。以混沌定量识别为基础，给出了三相并流系统的流型图。     

基于空隙率的油气两相流流量测量的研究

利用文丘里管进行油气两相流流量测量的研究,在考虑了气液滑移比对流量测量的影响,对均相流流量测量模型进行了修正,得到了空隙率和文丘里管两端差压与两相流总质量流量及液相质量流量的关系式。实验结果表明利用这两个关系式,在干度小于2％,空隙率在15%～83％范围内,总质量流量测量和液相质量流量测量的均方根误差均小于5％。针对不同的流型,对关系式中的系数作了修正计算,进一步降低了测量误差。     

基于离散粒子群的气液二相流型特征选择

针对气液二相流型识别中存在的大量无关或冗余的特征会降低分类器性能的缺陷,提出了离散粒子群算法(BPSO)和最小二乘支持向量机(LS-SVM)封装模式的流型特征选择方法.该方法分别采用小波包和经验模式分解方法(EMD)对原始压差波动信号进行分解,分别提取原始信号和各分解信号的时域量纲一指标组成融合特征.然后采用BPSO进...     

基于图像多特征融合和支持向量机的气液两相流流型识别

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bubbly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intel-ligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identifica-tion accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identifica-tion.     

应用文丘里管和空泡份额传感器测量油气两相流的实验研究

油气两相流流量测试技术一直是油田两相流研究的重要领域,对油井产能评估和油田的现场管理意义重大.但由于缺乏高精度的测量装置以及气液间滑速比的存在,一直很难获得高精度的两相流的相流量.今利用文丘里管和空泡份额传感器对油气两相流流量测试方法进行了理论和实验研究,首先采用辐射式的空泡份额传感器对流动管道的截面含气率进行了测量,进而计算得到混合物的密度;然后利用喉径比为0.45的经典文丘里管测量得到了两相流总质量流量.在此基础上,考虑了油、气两相界面滑速比对气相流量计算产生的影响,改进了传统的均相流模型,得到了气液两相流气相流量新的计算方法.实验结果表明,利用该方法计算得到的油气两相流总流量、液相流量以及气相流量都具有较高的精度,能够满足现场测试要求,从而为现场测量提供了一种新的计算方法.     

基于流体诱发振动的非接触式湿气流流型识别(英文)

A novel noninvasive approach, based on flow-induced vibration, to the online flow regime identification for wet gas flow in a horizontal pipeline is proposed. Research into the flow-induced vibration response for the wet gas flow was conducted under the conditions of pipe diameter 50 mm, pressure from 0.25 MPa to 0.35 MPa, Lockhart-Martinelli parameter from 0.02 to 0.6, and gas Froude Number from 0.5 to 2.7. The flow-induced vibration signals were measured by a transducer installed on outside wall of pipe, and then the normalized energy features from different frequency bands in the vibration signals were extracted through 4-scale wavelet package transform. A “binary tree” multi-class support vector machine(MCSVM) classifier, with the normalized feature vector as inputs, and Gaussian radial basis function as kernel function, was developed to identify the three typical flow regimes in-cluding stratified wavy flow, annular mist flow, and slug flow for wet gas flow. The results show that the method can identify effec-tively flow regimes and its identification accuracy is about 93.3%. Comparing with the other classifiers, the MCSVM classifier has higher accuracy, especially under the case of small samples. The noninvasive measurement approach has great application prospect in online flow regime identification.     

负压差立管内气固两相流的流态特性及分析

对于出口插入密相床的立管，管内气固两相的流动特点是下行的颗粒速度大于气体速度和颗粒的逆压差流动，颗粒下行是一个减速运动过程. 管内的气固两相流的流态有两种形式，当GsGsc时，流态是浓相输送流态，气流下行. 两种流态可以互相转变，主要取决于颗粒质量流率的大小. 负压差立管的流态变化与气固两相之间滑落速度和轴向压力的变化密切相关，滑落速度随颗粒质量流率的增加逐渐减小，而轴向压力则逐渐增大以平衡立管的负压差.