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Flow regime identification of mini-pipe gas-liquid two-phase flow based on textural feature series

机译:基于结构特征序列的微管气液两相流流态识别

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A method for the identification of gas-liquid two-phase flow regime in mini-pipes is proposed based on textural feature series. A high-speed image acquisition system is used to capture images of gas-liquid two-phase flow in the mini-pipe with inner diameter of 2.8mm, and five typical flow regimes (stratified flow, wavy flow, bubbly flow, slug flow and annular flow) are observed in the experiment. Two textural features (dissimilarity and entropy) of images are extracted by gray level co-occurrence matrix (GLCM). And then the mean value and the standard deviation of the textural feature series are used as the inputs of support vector machine (SVM) to identify the current flow regime. The identification accuracies of the five typical flow regimes are all above 91%. The results show that the method is feasible and effective, and can be used for gas-liquid two phase flow regime identification in mini-pipes.
机译:提出了一种基于结构特征序列的微管内气液两相流流态识别方法。高速图像采集系统用于捕获内径为2.8mm的微型管道中气液两相流的图像,以及五种典型的流态(分层流,波浪流,气泡流,团状流和在实验中观察到了环形流动。通过灰度共生矩阵(GLCM)提取图像的两个纹理特征(相异性和熵)。然后,将纹理特征序列的平均值和标准偏差用作支持向量机(SVM)的输入,以识别当前的流态。五个典型流态的识别精度均高于91%。结果表明,该方法可行,有效,可用于微型管道的气液两相流态识别。

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