基于图像小波包信息熵和遗传神经网络的气-液两相流流型识别

根据小波包变换能够将图像信号按不同尺度进行分解的特性,提出了基于图像小波包信息熵特征和遗传神经网络相结合的气-液两相流流型识别的新方法.该方法采用高速摄影系统获取水平管道内气-液两相流的流动图像,经过处理,对图像进行多分辨率分析,提取小波包变换系数的信息熵特征,用主成分分析法降低特征维数构成特征矢量,作为流型样本对遗传神经网络进行训练,实现了对流动图像的流型智能化识别.结果表明:图像小波包信息熵特征可以很好地反映各流型之间的差异;遗传神经网络结合遗传算法和BP算法各自优点,具有收敛速度快、不易陷入局部极小的特性,网络识别率为100%.     

基于递归定量特征的气-液两相流型融合识别

为了进一步提高流型识别的准确率,针对气-液两相流压差波动信号的非平稳特征,提出了一种基于递归定量分析(RQA)和多传感器数据融合技术的流型识别方法.该方法首先采用RQA方法提取压差波动信号的非线性特征参数,对3个不同取压间距压差波动信号的特征参数进行特征层融合,构成融合特征向量,并运用融合的特征向量对支持向量机进行训练并识别流型.对水平管内空气-水两相流4种典型流型的识别结果表明,经过多传感器数据融合,识别结果的可信度明显提高.     

压差波动特性识别流型研究

提出了一种基于小波分析的气液两相流流型模糊识别的新方法。该方法以加热段管程压降ΔP的波动信号作为测试信号,进行基于三阶的Daubechies小波二尺度分解,根据所得的二尺度细节系数的均方值确定了流型的识别方法。研究结果表明,这种识别方法能有效地实现气液两相流中泡状流、弹状流的识别。     

基于PCA-GA-SVM的竖直下降两相流流型预测

为提高小样本条件下的流型识别精度和时效性,提出了一种融合小波包分解（WPD）、主元分析（PCA）、遗传算法（GA）和支持向量机（SVM）的优化识别模型,并成功应用在竖直下降两相流流型辨识工作中。利用WPD对非平稳电导波动信号进行分解、重构,提取小波包能量构造特征向量;通过PCA对特征向量进行降维,降低特征输入的复杂性;同时采取GA全局迭代寻优的方式确定SVM的关键参数惩罚因子（C）和核函数参数（g）。对PCA-GA-SVM识别效果进行验证后与SVM、PCA-SVM、GA-SVM网络进行对比。结果表明,经过PCA和GA优化后的SVM网络在流型识别精度和时效性方面均提升显著,对泡状流、弹状流、搅拌流和环状流的总体预测精度达到了94.87%,耗时仅3.95 s,可满足在线识别需求。      

棒束通道气液两相流流型识别及动力学特性分析

采集棒束通道实验台上气液两相流4种流型压差信号,计算4种流型的多尺度边际谱熵,对其进行流型识别及动力学特性分析。实验结果表明:多尺度边际谱熵能从整体上区分4种流型,从频域细节尺度定量揭示不同流型间的动力学特性;利用多尺度边际谱熵增率和谱熵均值联合分布可定量准确区分4种流型,对泡状-搅混流这种难以区分的过渡流型也有较好效果;与支持向量机结合具有运算速度快、识别率高的优点,准确率高达98.11%,适合流型的在线识别。     

基于奇异值分解和最小二乘支持向量机的气-液两相流流型识别方法

针对气-液两相流压差波动信号的非平稳特征和BP神经网络学习算法收敛速度慢、易陷入局部极小值等问题,提出了一种基于奇异值分解和最小二乘支持向量机（LS-SVM）的流型识别方法。该方法首先采用经验模态分解将气-液两相流压差波动信号分解为多个平稳的固有模态函数之和,并形成初始特征向量矩阵;对初始特征向量矩阵进行奇异值分解,得到矩阵的奇异值,将其作为流型的特征向量,根据LS—SVM分类器的输出结果来识别流型。对水平管内空气-水两相流4种典型流型进行识别,结果表明,与神经网络相比,该方法具有更高的识别率和识别速度。     

基于灰色神经网络和支持向量机的两相流流型辨识

两相流流型直接影响两相流的流动特性和传热传质性能。利用小波分析对气液两相流压降实验数据进行处理,提取不同频率的小波系数。以小波能量为特征,输入BP神经网络进行训练,进行流型的初步辨识。将灰色神经网络模型应用于气液两相流的辨识,同时创立将压差波动数据和小波能量数据输入Lib-SVM机分类器的方法,分别对流型进行辨识。结果显示,这三种方法均可进行流型的辨识,小波能量支持向量机的判别结果比灰色神经网络和BP神经网络的判别结果准确。支持向量机对压差信号直接进行流型辨识时准确率达到95.2%。     

一种基于小波包分解的神经网络识别γ能谱方法

提出了一种基于小波包分解的神经网络识别γ能谱方法,该方法将γ能谱看作非平稳离散信号,对γ能谱做小波包分解得到各频带的能量,以各频带能量为元素构造特征向量作为神经网络的训练样本,利用神经网络的分类功能实现γ能谱的识别.结果表明,该方法不仅能准确地识别不同种类标准源的γ能谱,还能准确识别不同批次标准源的γ能谱,具有很好的实用价值.     

气-液两相流图像灰度脉动信号的多尺度双分形特性研究

应用高速摄影技术拍取气-液两相流水平管中3种典型流型的动态图像视频,对每一帧图像的平均灰度脉动信号进行提取;将提取的信号进行多尺度固有模态函数分解,然后与极差/标准偏差(R/S)分析方法相结合,提取各尺度的HURST指数和双分形特征.对气-液两相流的3种典型流型进行了气泡群和单个气泡2种形式的动力学行为分析,应用峭度系...     

基于噪声信号分析和HMM-SVM混合模型的乏燃料剪切机故障诊断研究

对乏燃料剪切机运行状态进行监测和故障诊断是保证其正常运行,避免酿成严重事故的重要保障。剪切机的工作噪声中包含了丰富的信息,采集后经小波包变换提取其能量特征后可以很好地表征剪切机的故障状态。本文对剪切机工作噪声信号通过三层小波包分解进行特征提取,构建了HMM-SVM混合模型用于剪切机故障的智能诊断。该模型结合了隐马尔可夫模型良好的动态建模能力和支持向量机良好的分类能力及小样本泛化能力强的优点。实验证明,HMM-SVM混合模型用于乏燃料剪切机的故障诊断可取得优于单独HMM或SVM模型的良好效果。     

非线性振动下水平通道内气液两相流动研究

在地震等行为产生的非线性振动下,两相流体会影响回路传热并对装置结构进行冲击,因此对气液界面行为的把握对核安全具有十分重要的意义。本文通过将振动装置与两相流实验回路相结合的方法,对非线性振动下水平通道内气液两相流问题进行了实验研究。基于FLUENT平台,结合动网格模型及UDF编程手段建立了数学模型,并对数学模型进行验证。研究结果表明：模拟结果与实验结果具有很好的一致性;振动工况下气液两相流动形式不同于稳态工况,会出现更复杂的气液界面,主要流型有泡状流、弹状流、搅拌流、波状流及环状流;瞬时摩擦压降的波动幅度随振动幅度和频率的增大而增大,且与振动幅度相比,振动频率对其影响更大。     

Upward vertical two-phase flow local flow regime identification using neural network techniques

Traditionally, the flow regimes in two-phase flow are considered in a global sense. However, a local flow regime is required to understand and model the interfacial structures present in the flow. In this work, a new approach has been used to identify both global and local flow regimes in a two-phase upward flow in a 50.8 mm internal diameter pipe under adiabatic conditions. In the present method, the bubble chord length distributions, which are measured simultaneously with three double-sensor conductivity probes, have been used to feed a self-organized neural network. The global flow regime identification results show a reasonable agreement with the visual observation for all the flow conditions. Nonetheless, only the local flow regimes measured at the center of the pipe agree with the global ones. The local flow regime combinations found are analyzed using the flow map information, cross-correlations between the probe signals, and previous correlations. In this way, it is possible to identify eight different global flow regime configurations.     

Classification of two-phase flow regimes via image analysis and a neuro-wavelet approach

A non-intrusive method of two-phase flow identification is investigated in this paper. It is based on image processing of data obtained partly from dynamic neutron radiography recordings of real two-phase flow in a heated metal channel, and partly by visible light from a two-component mixture of water and air. Classification of the flow regime types is performed by an artificial neural network (ANN) algorithm. The input data to the ANN are some statistical moments of the wavelet pre-processed pixel intensity data of the images. The pre-processing used in this paper consists of a one-step multiresolution analysis of the 2-D image data. The investigations of the neutron radiography images, where all four flow regimes are represented, show that bubbly and annular flows can be identified with a high confidence, but slug and churn-turbulent flows are more often mixed up in between themselves. The reason for the faulty identifications, at least partially, lies in the insufficient quality of these images. In the measurements with air-water two-component mixture, only bubbly and slug flow regimes were available, and these were identified with nearly 100% success ratio. The maximum success ratio attainable was approximately the same whether the raw data was used without wavelet preprocessing or with a wavelet preprocessing of the input data. However, the use of wavelet preprocessing decreased the training time (number of epochs) with about a factor 100.     

Flow regime identification methodology with neural networks and two-phase flow models

Vertical two-phase flows often need to be categorized into flow regimes. In each flow regime, flow conditions share similar geometric and hydrodynamic characteristics. Previously, flow regime identification was carried out by flow visualization or instrumental indicators. In this research, to avoid any instrumentation errors and any subjective judgments involved, vertical flow regime identification was performed based on theoretical two-phase flow simulation with supervised and self-organizing neural network systems. Statistics of the two-phase flow impedance were used as input to these systems. They were trained with results from an idealized simulation that was mainly based on Mishima and Ishii's flow regime map, the drift flux model, and the newly developed model of slug flow. These trained systems were verified with impedance signals measured by an impedance void-meter. The results conclusively demonstrate that the neural network systems are appropriate classifiers of vertical flow regimes. The theoretical models and experimental databases used in the simulation are shown to be reliable.     

基于AFSA-RF的两相流型图扩展技术

为预测高流速条件下的流型并建立流型图,提出一种基于人工鱼群算法(artificial fish swarm algorithm, AFSA)优化的随机森林(random forest, RF)的机器学习模型,基于最优、简化参数出发,进行流型的智能识别。该模型成功地应用于竖直下降两相流流型的识别,通过不同分类模型以及优化方法对实验数据进行计算,发现AFSA-RF模型的流型识别精度与稳定性高于未优化的RF模型以及其他主流优化方法,对高流速区域的流型的识别成功率达到了90.91%,进一步验证了该预测模型的有效性。依托建立的模型,对现有流型图的适应范围进行了扩展,获得了适用于高流速条件下的流型图。