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

机译:气液复合网络拓扑特性的动态分析

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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:气液两相流的模式始终是压力题目,因为它在流动和传热性能上发挥着重要作用。因此,对流动模式的研究始终是两相流的重要对象。本文侧重于获得气液两相流量的压力信号的实验,用于构建气液两相差压波动的复杂网络,记录波状流量的流量信息来计算动态统计。计算结果表明,前15个节点的度数的发生概率远高于其他节点的概率;因此,它们是流动特性的关键因素。否则,网络具有高的聚类系数,小的平均路径长度和大节点度,反射它具有远程相关性和射击范围的相关性。此外,这些动态统计信息表现出许多小组的大型网络。本文验证了气液两相流的典型特征,即它从完全新的角度带来波动差压,从而提供不同流动模式和趋势的内部规律的可靠参考。基于复杂的网络理论,我们还实现了气液两相流动模式的良好识别。

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