油气润滑测试系统中ECT软场效应研究

电容层析成像系统的传感器具有"软场"特性,即敏感场分布受被测介质的影响。为获得高质量的重建图像,利用COMSOL软件快速求解油气润滑测试系统ECT的灵敏度矩阵,运用COMSOL和MATLAB联合仿真,对该管道内层流和环状流2种典型流型的灵敏度场的软场效应进行分析,并且应用LBP算法和OTSU算法,分别在考虑和忽略软场效应的情况下对2种流型进行图像重建。仿真结果表明,软场效应改变了灵敏度分布;润滑油介质靠近管壁时,检测敏感度较高;采用软场效应改变的灵敏度矩阵进行图像重建,将有效提高重建图像精度。     

电容层析成像两相流流型可视化系统

本文基于电容层析成像技术研制了一套两相流型可视化系统,建立了一种新型的具有高速采集和高分辨率测量的电容数据采集系统,采用加权反投影算法进行图像重建以保证流型可视化的实时性和有效性,整套流型可视化系统经气液两相流现场应用测试和气固流化床实验室动态实验证明是有效的,成像速度达20幅／秒以上,满足工业应用要求。     

模型参数对卷积神经网络电容层析成像图像重建的影响

卷积神经网络凭借其较强的非线性拟合能力,在电容层析成像图像重建中逐渐得到应用。本文针对卷积神经网络模型超参数调节问题,研究了模型参数对卷积神经网络电容层析成像图像重建的影响。首先,通过数值方法构建了包含80 000组随机流型与40 000组典型流型的"电容矩阵-介质分布"数据集;然后,通过该数据集中的训练集对不同超参数的卷积神经网络模型进行训练和验证,并系统研究了网络初始化、网格密度、卷积核数、全连接层神经元数以及隐藏层结构等超参数对图像重建精度的影响;接着,利用额外生成的12 000组数据作为测试集对各网络模型性能进行评价;最后通过静态实验,对不同网络模型的图像重建效果进行了比较和分析。结果表明:网络隐藏层结构对图像重建精度影响较大,而网络初始化、网格密度、卷积核数以及全连接层神经元数等超参数对重建精度影响较小。     

基于LU分解的共轭梯度法单步成像算法

单步和迭代电容层析成像（ECT）图像重建算法分别具有成像质量差和成像速度慢的缺点,为了快速得到高质量的重建图像,本文提出了一种新型的基于LU分解的单步共轭梯度成像法。该方法首先将ECT物理模型进行规范化和Tikhonov正则化处理,进而将LU分解的思想引入ECT方程的求解中,从而实现了单步图像重建。理论分析表明,该算法具有良好的单步收敛性。通过典型流型的仿真实验,证明了该算法可以获得优于反投影算法的重建图像。     

基于INGA的电容层析成像图像重建算法的研究

电容层析成像图像重建是一个具有典型的病态问题,在分析遗传算法基础上,针对小生境遗传算法易于早熟、收敛速度慢以及局部寻优能力较差等缺点,提出了一种引入梯度算子的改进小生境遗传算法(INGA)用于ECT图像重建.仿真和实验结果表明,对于简单流型该算法与LBP、Landweber算法相比满足收敛速度快且重建图像误差小,并能以较高的精度重构出两相流体的截面成像,为ECT图像重建算法的研究提供了一个新的思路.     

叶片通用扭曲规律的拓展及抛物线流型的控制

分析了叶片一般通用扭曲规律,在此基础上拓展了一般通用扭曲规律。提出了叶片抛物线流型,适当增加了叶片均径附近的能量头,减小了叶尖与叶根附近的能量头。利用通用扭曲规律的拓展形式,通过选择不同的控制系数,能够获得传统常用流型以及不同能量头分布的抛物线流型。对比了抛物线流型与等环量、等反动度流型,结果表明,抛物线流型改善了反动度、入口相对速度马赫数、气流出口角、气流折转角的分布,克服了传统流型叶根易出现负反动度、叶尖入口相对速度马赫数过大等缺点,缓解了叶根气流出口角和气流折转角过大等问题。     

粒子滤波算法在ECT图像重建中的应用

针对电容层析成像技术(ECT)的图像重建质量精度较低的问题,提出了一种基于粒子滤波的ECT图像重建方法。首先,分析了ECT图像重建基本原理,以系统状态估计的方式描述了ECT图像重建最优解的搜索过程,并建立了状态空间模型。然后,以线性反投影(LBP)算法的图像重建结果作为初始状态,利用测量信息对从状态空间中获取的随机样本进行最优加权,以获得重建图像的最小方差估计。最后,对5种不同的流型进行了仿真实验。实验结果表明,利用本文方法获得的重建图像误差平均值为42.93%,相关系数平均值为0.813 9,比LBP算法、Landweber迭代算法和IMN-SNOF算法得到的相应指标要好。本文方法是一种有效、精度较高的ECT图像重建方法,为ECT图像重建技术提供了新的途径和手段。     

电站空冷风机的设计与流场数值计算

介绍了火电厂直接空冷风机采用5次方流型的设计过程,使用三维N-S方程对所设计的空冷风机在设计点的性能进行了计算,并与采用等α流型的风机性能进行了比较,计算结果表明:采用5次方流型设计,其全压效率要比等α流型的风机高10%左右.     

基于多尺度信息熵特征和RBF神经网络的气液两相流流型识别方法

根据小波包变换能将信号按任意时频分辨率分解到不同频段的特性，提出一种基于小波包多尺度信息熵的流型识别方法。该方法首先对采集到的压差波动信号进行4层小波包分解，在通频范围内得到分布在不同频段内的分解信号，进而建立流型的多尺度信息熵特征向量。并以此特征向量作流型样本对RBF神经网络进行训练，实现流型的智能化识别。试验结果表明，训练成功的RBF网络能很好地识别水平管内的4种流型，为流型识别开辟了一条新的途径。     

气液两相流型在线识别系统的开发和应用

在统计理论和小波理论的基础上,把神经网络应用到流型识别中,采用VB语言编程完成了软件的开发.按照信号测量、信号特征提取和流型识别3个实现流型客观自动识别的步骤,建立了一套气液两相流流型在线识别系统,在气液两相流实验系统上采集了180组仿真样本进行仿真实验.结果显示:流型正确识别率为91.7%,此系统应用于流型识别,不仅具有较高的识别率,而且很好地实现了识别结果的实时显示,运行速度快,达到了流型在线识别的目的.     

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.     

DIC image reconstruction on large cell scans

The width of the emission spectrum of a common fluorophore allows only for a limited number of spectral distinct fluorescent markers in the visible spectrum, which is also the regime where CCD-cameras are used in microscopy. For imaging of cells or tissues, it is required to obtain an image from which the morphology of the whole cell can be extracted. This is usually achieved by differential interference contrast (DIC) microscopy. These images have a pseudo-3D appearance, easily interpreted by the human brain. In the age of high throughput and high content screening, manual image processing is not an option. Conventional algorithms for image processing often use threshold-based criteria to identify objects of interest. These algorithms fail for DIC images as they have a range from dim to bright with an intermediate intensity equal to the background, so as to produce no clear object boundary. In this article we compare different reconstruction methods for up to 100 MB-large DIC images and implement a new iterative reconstruction method based on the Hilbert Transform that enables identification of cell boundaries with standard threshold algorithms.     

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.     

数码相机的彩色图像预处理过程

结合数码相机图像采集特性和人眼视觉误差,讨论了彩色图像预处理过程.通过分析彩色图像的数据格式,将八叉树量化方法用于彩色图像的颜色信息量化,并采用矢量中值滤波消除彩色图像噪声,以实现对彩色图像的合理预处理.结合实验讨论分析了矢量中值滤波法的滤波器窗口参数,并结合颜色信息相似度理论提出了彩色图像预处理效果的评价方法.实验获得了合理的颜色量化方法和滤波器窗口参数,处理得到的图像最大色差变化控制在3个CIELAB色差单位.结果表明,用八叉树结构量化和矢量中值滤波对数码相机采集到的彩色图像进行预处理可达到较理想的效果.     

A multiangle polarised imaging-based method for thin section segmentation

The most crucial task of petroleum geology is to explore oil and gas reservoirs in the deep underground. As one of the analysis techniques in petroleum geological research, rock thin section identification method includes particle segmentation, which is one of the key steps. A conventional sandstone thin section image typically contains hundreds of mineral particles with blurred boundaries and complex microstructures inside the particles. Moreover, the complex lithology and low porosity of tight sandstone make traditional image segmentation methods unsuitable for solving the complex thin section segmentation problems. This paper combines petrology and image processing technologies. First, polarised sequence images are aligned, and then the images are transformed to the HSV colour space to extract pores. Second, particles are extracted according to their extinction characteristics. Last, a concavity and corner detection matching method is used to process the extracted particles, thereby completing the segmentation of sandstone thin section images. The experimental results show that our proposed method can more accurately fit the boundaries of mineral particles in sandstone images than existing image segmentation methods. Additionally, when applied in actual production scenarios, our method exhibits excellent performance, greatly improving thin section identification efficiency and significantly assisting experts in identification.     

基于不变矩及模糊神经网络的螺纹识别研究

根据概率论的基本原理 ,分析了图形的矩表示法 ,利用七个不变矩作为图形的描述子 (特征量 ) ,提出基于七个不变矩及模糊神经网络的螺纹识别方法 ,采用了L M(Levenberg Marquardt)算法训练网络。七个不变矩可以作为一幅图形的特征量进行图像识别 ,模糊L MBP网络收敛较快。     

基于遗传算法/神经网络组合技术的气液两相流型辨识

通过采用遗传算法训练BP神经网络、优化网络权值的技术,对气液两相流的流型进行了辨识研究,在此基础上建立了基于遗传算法/神经网络组合技术的气液两相流流型的预测模型,从而为发动机轴承腔内润滑油气液两相流流型识别提供了技术支持,也为考虑轴承腔气液两相流的相关设计和实验工作提供了技术条件。     

Fast reconstruction of computerized tomography images based on the cross-entropy method

Computerized tomography (CT) has been applied to multi-phase flow measurement in recent years. Image reconstruction of CT often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive, especially for the case of on-line flow regime identification. In this paper, a minimum cross-entropy (MCE) reconstruction based on wavelet multi-resolution processing, i.e., an MRMCE method, is proposed for fast reconstruction of CT images. Each row of the system’s matrix is transformed by 1-D wavelet decomposition. A regularized MCE solution is obtained using the simultaneous multiplicative algebraic reconstruction technique (SMART) at a coarse resolution level, where important information of the reconstructed image is contained. Then the solution in the finest resolution is obtained by inverse fast wavelet transformation (IFWT). Both computer simulation and experimental work were carried out for oil-gas two-phase flow regimes. Results obtained indicate that the MRMCE method improves the resolution of the reconstructed images and dramatically reduces the computation time compared with the traditional linear back-projection (LBP), MCE and algebraic reconstruction technique (ART) methods. Furthermore, the new method can also be used to accurately estimate the local time-averaged void fraction of dynamic two-phase flow. It is suitable for on-line multi-phase flow measurement.     

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.