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Dynamic analysis on topological properties of the complex network of gas-liquid two-phase flow

机译:气液两相流复杂网络拓扑特性的动力学分析

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The pattern of gas-liquid two-phase flow has always been a stress topic since it plays an important role on flow and heat transfer performance. Therefore, the study of flow pattern is always an important subject of two-phase flow. This paper focus on an experiment that obtains pressure signals of gas-liquid two phase flow, which are used for constructing a complex network of gas-liquid two-phase differential pressure fluctuation, recording undulate flow pressure information to compute the dynamics statistics. The compute results reveal that occurrence probability of the degree numbers of top 15 nodes is extremely higher than other ones; therefore they are the key factors of flow characteristic. Otherwise, the network possesses a high clustering coefficient, small average path length and big node degree that reflected it has long-range correlation and shot-range correlation. Also, these dynamics statistics manifested the large network with many small groups. This paper verifies a typical feature of gas-liquid two-phase flow that it brings fluctuant differential pressure from a totally new perspective, providing a reliable reference of the internal laws of different flow pattern and trend. We also achieve good identification of flow pattern in gas-liquid two-phase flow based on complex network theory.
机译:气液两相流的模式一直是一个压力主题,因为它对流动和传热性能起着重要作用。因此,流态研究一直是两相流的重要课题。本文着重于获得气液两相流压力信号的实验,该信号用于构建气液两相压差波动的复杂网络,记录波动的流压信息以计算动力学统计数据。计算结果表明,前15个节点的度数出现概率大大高于其他节点。因此,它们是流动特性的关键因素。否则,网络将具有较高的聚类系数,较小的平均路径长度和较大的节点度,这反映出该网络具有远距离相关性和射击距离相关性。同样,这些动态统计数据显示了具有许多小组的大型网络。本文从全新的角度验证了气液两相流的典型特征,它带来了波动的压差,为不同流型和趋势的内部规律提供了可靠的参考。基于复杂网络理论,我们还可以很好地识别气液两相流中的流型。

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