水平井油水两相流型实验研究

为了研究水平井油水两相在不同流量和不同含水率条件下的流型流态,采用多探头系列仪器组合常规涡轮,在多相流模拟实验室进行水平井油水两相模拟实验.通过实验研究,油水两相在水平井中的流型类别可分为分层流、油包水-水、水包油-水、油包水和水包油五种,建立了流型图,可为水平井生产测井施工设计和状态分析提供流型参数.     

基于离心泵性能曲线拐点特征的流型识别方法

通过识别低比转速离心泵气液两相流性能曲线的拐点特征,间接获得了旋转叶轮的内部气液流型和流型转化边界。并基于气液两相流测试管路,开展了旋转离心泵气液两相流水力性能测试和叶轮内部流型可视化实验。结果表明：低比转速离心泵气液两相性能曲线的拐点可视为其叶轮内部不同流型转化的边界。     

浓相气力输送的流型及稳定性判定

在对浓相输送的相图、流型及稳定性研究的基础上,提出了一种检测流型与判断稳定性的方法．在分析和处理计算机采集到的输送管内压力波动信号后,发现水平输送管内压力波动信号的较大振幅的概率密度之和与表观气速之间有较好的对应关系,找到了一种能实际应用于工程上判断流型和稳定性的判据,并以此作为自动控制的基础．     

湍动流化床的流型与流型过渡

分别在直径为114mm 和244mm 的流化床中,采用压力探头和光导纤维探头考察了湍动流态化的上、下限及实验判别准则。发现床内局部点处的床空隙率随气速的变化符合于修正的 Richardson-Zaki 关系,并以此可以揭示出局部区域进入湍动流态化的流型过渡(上限)。压力脉动法的测量结果(U_c)还表明:此时,床内湍动流态化区域实际上已经占有相当的份额。另一方面,分析床内压力及床密度的脉动信号随气速的变化,可以判定脱离湍动流态化时的流型转变(下限)。本文还应用 Kolmogoroff 的湍流理论定性地分析了湍动流态化的湍流结构。     

液固导流管喷动流化床流型及流型转变的实验研究

以粒径为0.9mm的铜颗粒为流化颗粒,常温水为流化介质,在截面为59.2×15.6mm的矩形床中,对液固导流管喷动流化床的流型及流型转变进行实验了研究。通过对床层压降流量曲线的分析并结合实验观察,确定了液固导向管喷动流化床的流型及流型的划分,提出了流型图。研究结果对进一步开展导流管喷动流化床电极研究有重要的参考意义。     

气固流化床流型特性及其识别的复杂性研究

运用复杂性C2、涨落复杂性Cf及Lempel-Ziv复杂性C(n)等复杂性参数对气固流化床压力脉动信号进行分析,研究它们随流化床操作气速增大历经不同流型的变化趋势并将结果作了比较,进一步探讨了流化床流型特性的内在规律性,研究结果表明,在起始流化致鼓泡态转变的过程中,气－固体系会进行一种所谓的“重构”现象,并证实了气泡的存在是影响压力脉动信号复杂性的重要因素,同时实验显示复杂性参数能明确地指示固定床,鼓泡流化及湍动流化等不同流型之间的转变过程,为流型识别提供了新思路。     

矩形微通道内气液两相流型及其转换边界的实验研究

气液两相流流型预测是微流动系统设计和控制的基础.实验在带有“十”字形和“T”字形的矩形微通道(主通道水力直径0.29 mm)内,以氮气作为气相,分别以乙醇-表面活性剂水溶液或丙酮-表面活性剂水溶液为互溶混合液相,对气液两相流型及转换边界进行了研究.气相表观速度和混合液相表观速度分别在0.012～22.391 m·s-1...     

三相流环状集输管网流型研究

油气水三相流动广泛存在于石油和天然气开采过程中,因此对油气水三相流流动型态的觃律迚行研究是热力计算模型和油田集输管道水力的基础,也为解决油田中混输问题提供了重要的实用价值及经济效益。以大庆低渗透油田第八采油厂三矿7#--4计量间环2作为试验环路迚行CFD数值模拟,按照油气水三相流的不同油水界面特征、气液界面特征最终划分为4种流型,并模拟了不同含油率、不同含气率、不同速度条件下的流型,得到了不同条件下流型图及流型变化觃律。这为外围油田环状集输管网的流程运行探索了新的途径,为实现环状集输管网设计计算提供理论支撑。     

刮壁桨在槽式釜中的流型研究

用示踪粒子照相法，对浮动刮壁桨多种刮板形式的粘性流流型进行了研究。采用峰高和峰宽两个量来描述刮板附近流线的形态，认为可利用峰高和峰宽值来判断不同刮板形式的传热性能。对照传热曲线得出：Ｒｅ小于２５时，应以峰高值来判别传热效果；Ｒｅ大于２５时，应以峰宽值作为判别传热效果的标准。并从多种刮板形式中挑选出带水平尾板的刮板，其传热效率最高。     

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。     

Neural network based objective flow regime identification in air-water two phase flow

The Kohonen self-organising neural network was applied to identify flow regimes in horizontal air-water flow. The neural network was trained with stochastic features derived from turbulent absolute pressure signals obtained across a range of flow regimes. The feature map succeeded in classifying samples into distinctive flow regime classes consistent with the visual flow regime observation.     

A consolidated flow regime map of upward gas fluidization

A new flow regime map, resulting from more fundamental studies on the hydrodynamics and new flow regimes, is proposed in response to more practical reclassifications of the existing regimes with the development of upward gas-solids fluidization systems. The previously reported flow regime maps and flow structures of some widely used fluidized beds are carefully examined. To better reflect the industrial applications, the fast fluidization regime is reclassified as high-density and low-density circulating fluidization regimes. A consolidated flow regime map is then proposed where the flow regimes of upward fluidization expand to include new types of fluidized beds such as circulating turbulent fluidized bed and high-density circulating fluidized bed. The proposed flow regime map consists of six flow regimes: bubbling, turbulent, circulating turbulent, high-density circulating and low-density circulating fluidization, and pneumatic transport. The transitions between the regimes are discussed with new correlations proposed for fluid catalytic cracking type particles. Analysis on the dominating phase in the different types of fluidized beds reveals the dynamic changeover from solids phase continuous in conventional low-velocity batch/“fixed” fluidization operations to gas phase continuous in high-velocity continuous/“moving” fluidization operations and provides more insights to the transitions between the flow regimes for industrial design and practice.     

基于图像不变矩特征的气液二相流流型识别

气液二相流流型极大地影响气液二相流的流动和传热特性,准确识别流型对相关设备的设计和运行具有重要意义。根据不变矩能有效检测出具有平移、旋转、比例变化的图像特性,提出了一种基于图像不变矩和概率神经网络相结合的气液二相流流型识别的新方法。该方法利用高速摄影系统获取水平管道内气液二相流的流动图像,经过图像处理后提取图像不变矩特征向量,并以此特征向量作为流型样本对概率神经网络进行训练,实现了对流动图像的流型智能化识别。实验结果表明,训练成功的概率神经网络能够快速准确地识别水平管道内的7种典型流型,整体识别率达到99.3%,为流型在线识别提供一种新的有效方法。     

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.     

基于两相流流型的平行流冷凝器整体仿真模型与实验验证

引言平行流冷凝器作为一种新颖的换热器加之其不同于传统翅片管换热器的结构,因此其传热流动性能尚处研究之中。尤其是平行流冷凝器制冷剂侧微通道两相流机制及其转变不同于常规的管子,以往关于平行流冷凝器整体仿真模型的研究中,微通道两相流数值模拟大都采用传统大管径模型或者修正     

旋转流场流态预测模型验证及其速度分量选择的差异性

鉴于目前高速旋流场中的流体流态判定准则不统一、预测模型契合度不高的问题,依据流体力学基本原理及管道、缝隙流场的判定方法,本文对经典一维雷诺数及二维流量因子预测模型进行了理论重构,并尝试提出了适用于旋转流场中流体流态判定和预测的椭球模型。文章首先根据经典雷诺数模型和流动因子模型,对仿真计算和椭球模型进行了理论验证;然后对不同介质和工况参数下的速度场进行了分析计算,并与相关文献进行对比研究;最后结合对旋转流场中拐点的理论剖析,对椭球模型的合理性和科学性进行了论证,并对模型中速度分量的选择及差异性进行了讨论。结果表明：椭球模型对管道流动的预测结果与经典雷诺数模型完全一致,新模型对旋转流场中转折点的预测值较传统模型明显偏低,与实际工况更加贴近;根据椭球模型进行旋转流场的流态判定时,应选择平均直径处的线速度为剪切平均速度、进出口径向速度平均值为径向平均速度及最大轴向速度为模型输入因子。椭球模型的提出,为旋转流场在理论计算时如何科学判定流体流态提供了新的思路和判定方法。     

基于灰度共生矩阵和支持向量机的气液两相流流型识别

根据灰度共生矩阵具有较好的纹理表达能力的特性,提出了一种基于图像灰度共生矩阵和支持向量机相结合的气液两相流流型识别的新方法。该方法利用高速摄影系统获取水平管道内气液两相流的流动图像,经过图像处理后,提取图像灰度共生矩阵的纹理特征,进而建立流型图像的灰度共生矩阵纹理特征向量,并以此特征向量作为流型样本对支持向量机进行训练,实现了对流动图像的流型智能化识别。实验结果表明,支持向量机能够快速准确地识别水平管道内的7种典型流型,整体识别率达到100%,每幅流型图像的判别时间约为1.7 s,为流型在线识别提供一种新方法。     

On flow regime transition in trickle bed: Development of a novel deep-learning-assisted image analysis method

An image analysis method was developed based on deep-learning algorithms to extract phase fractions quantitatively in a rectangular trickle bed, and the average identification error was lower than 5%. Furthermore, the flow regime transition in the trickle bed was studied. In trickle-to-pulse flow transition, the trickle flow could be further classified into the stable trickle flow and accelerated one. The SD of liquid fractions and the peak width at half-height of the probability density curve of liquid fractions were close to zero in stable trickle flow, increased rapidly in accelerated trickle flow, and remained approximately constant in pulse flow. In bubble-to-pulse flow transition, dispersed bubbles in bubble flow induced the outliers outside the upper boundary of the boxplot of gas fraction, while alternative appearance of gas-rich zone and liquid-rich zone in pulse flow induced outliers outside both the upper and lower boundaries of the boxplot of gas fraction.     

双谱核主元分析在气液两相流流型识别中的应用

针对压差波动信号的非线性和非高斯特性,提出了一种基于高阶谱和核主元分析相结合的流型识别方法。通过对气液两相流压差波动信号的双谱分析,提取了不同流型下信号的非高斯特征,以双谱分析核主元数字特征提取流型的特征,最后利用最小二乘支持向量机对流型进行智能识别。实验结果表明,提取的核主元特征反映了两相流的流动状态,最小二乘支持向量机可以有效地识别水平管道内的4种典型流型,整体识别率达到95%,为流型识别提供了一种有效的方法。