Recognizing Gas Shape in Two-phase Flow by Clustering Numerical Simulation Results

被引：0

作者：

Song, Yan-Po ^{[1
]}

Tao, Yan-Ming ^{[1
]}

Peng, Xiao-Qi ^{[1
,2
]}

Chen, Zhuo ^{[1
]}

Gao, Dong-Bo ^{[1
]}

机构：

[1] School of Energy Science and Engineering, Central South University, Changsha,410083, China

[2] Department of Information Science and Engineering, Hunan First Normal University, Changsha,410205, China

来源：

Kung Cheng Je Wu Li Hsueh Pao/Journal of Engineering Thermophysics | 2019年 / 40卷 / 10期

关键词：

Cluster analysis - Mass transfer - Two phase flow - Digital storage - Numerical methods - Heat transfer - Numerical models;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Gas shape has significant influence on kinetic processes such as heat transfer, mass transfer and physiochemical reactions in two-phase flow. To extract this information from numerical simulation results of two-phase flow, a new hierarchical clustering method using adaptive threshold and a clustering-based gas shape recognition method are proposed. In view of numerical simulation results for two-phase flow are often with large volume, a strategy of partition softly data space, then processing subspace data respectively, and lastly fusing the process results in all subspaces is proposed to reduce the storage complexity of gas shape recognition. As testing on 3 datasets (two of them are synthetic and the other is from simulation results) shown, methods proposed in this paper can recognize effectively the gas shape according to the two-phase flow simulation results, even when the discretization scales are not unified or some discretization cells are distorted to some extent, and the strategy of partition softly data space can reduce the storage complexity and make gas shape recognition realizable even when the volume of simulation results are huge. © 2019, Science Press. All right reserved.

引用

页码：2345 / 2352