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Two-phase flow regime assignment based on wavelet features of a capacitance signal

机译:基于电容信号的小波特征的两相流态分配

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In this work, a new method is proposed to determine the two-phase flow regime based on the capacitance trace of the flow. The experimental data set contains 123 capacitance traces measured for a horizontal tube with an inner diameter of 8 mm. The tested refrigerant is R134a. The mass flux is varied between 200 and 500 kg/m(2) s and the vapour quality x is varied between 0 and 1. For each capacitance signal the wavelet variance is estimated based on the maximum overlap wavelet transform of the signal. The used wavelet function is a D8 wavelet of the Daubechies family. A feature space is generated based on the wavelet variance values associated with frequencies below 100 Hz. Principal component analysis and linear discriminant analysis are subsequently applied to this raw feature space, after which the Fuzzy c-means clustering algorithm is used to divide the feature space into clusters corresponding to different flow regimes. The resulting flow regime assignment shows a good agreement with a visual classification of the data set based on flow visualisations. Finally, the classification was performed based on variable training data to show the robustness of the method. (C) 2015 Elsevier Inc. All rights reserved.
机译:在这项工作中,提出了一种基于流动的电容迹线确定两相流动状态的新方法。实验数据集包含针对内径为8 mm的水平管测量的123条电容迹线。被测制冷剂为R134a。质量通量在200至500 kg / m(2)s之间变化,蒸气质量x在0至1之间变化。对于每个电容信号,基于信号的最大重叠小波变换来估计小波方差。使用的小波函数是Daubechies家族的D8小波。基于与低于100 Hz的频率相关联的小波方差值生成特征空间。随后将主成分分析和线性判别分析应用于此原始特征空间,然后使用模糊c均值聚类算法将特征空间划分为与不同流态相对应的聚类。所得的流动状态分配显示出与基于流动可视化的数据集的可视分类良好的一致性。最后,基于变量训练数据进行分类以显示该方法的鲁棒性。 (C)2015 Elsevier Inc.保留所有权利。

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