- Journal Home
- Volume 36 - 2024
- Volume 35 - 2024
- Volume 34 - 2023
- Volume 33 - 2023
- Volume 32 - 2022
- Volume 31 - 2022
- Volume 30 - 2021
- Volume 29 - 2021
- Volume 28 - 2020
- Volume 27 - 2020
- Volume 26 - 2019
- Volume 25 - 2019
- Volume 24 - 2018
- Volume 23 - 2018
- Volume 22 - 2017
- Volume 21 - 2017
- Volume 20 - 2016
- Volume 19 - 2016
- Volume 18 - 2015
- Volume 17 - 2015
- Volume 16 - 2014
- Volume 15 - 2014
- Volume 14 - 2013
- Volume 13 - 2013
- Volume 12 - 2012
- Volume 11 - 2012
- Volume 10 - 2011
- Volume 9 - 2011
- Volume 8 - 2010
- Volume 7 - 2010
- Volume 6 - 2009
- Volume 5 - 2009
- Volume 4 - 2008
- Volume 3 - 2008
- Volume 2 - 2007
- Volume 1 - 2006
Commun. Comput. Phys., 26 (2019), pp. 1617-1630.
Published online: 2019-08
Cited by
- BibTex
- RIS
- TXT
In this paper, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method is proposed to detect particle clusters in particle-fluid systems. The particles are grouped in one cluster when they are connected by a dense environment. The parameters that define the dense environment are determined by analyzing the structure of the system, therefore, our approach needs little human intervention. The method is illustrated by identifying the clusters in two kinds of simulation trajectories of different particle-fluid systems. The robustness of cluster identification in terms of statistical properties of clusters in the steady state is demonstrated.
}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.2019.js60.09}, url = {http://global-sci.org/intro/article_detail/cicp/13278.html} }In this paper, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method is proposed to detect particle clusters in particle-fluid systems. The particles are grouped in one cluster when they are connected by a dense environment. The parameters that define the dense environment are determined by analyzing the structure of the system, therefore, our approach needs little human intervention. The method is illustrated by identifying the clusters in two kinds of simulation trajectories of different particle-fluid systems. The robustness of cluster identification in terms of statistical properties of clusters in the steady state is demonstrated.