3D Streamline Visualization Method Based on Clustering Fusion

doi:10.16182/j.issn1004731x.joss.22-1257

Abstract

Abstract:

In order to solve the problems of incomplete feature extraction, continuity destruction of flow field by visual results, and poor representation of streamline caused by unstable clustering division when the clustering method is used to realize 3D streamline visualization. A 3D streamline visualization method based on clustering fusion is proposed. It consists ofadistance measurement method between features and a clustering fusion method, which takes the inter-feature distance and spatial distance as the similarity between streamlines for clustering and then performs weighted merging and subdivision of the obtained clustering result. The method has been tested on data sets with different features and compared qualitatively and quantitatively with the existing methods. The results show that compared with the existing methods, the proposed method can better balance the relationship between feature extraction and streamline distribution, and the stability of clustering division is improved by 2%~5%. The accuracy of vector filed reconstruction is improved by 3%~5%.

Key words: flow filed visualization, streamline visualization, clustering fusion, feature extraction, streamline selection

CLC Number:

TP391.41

Shao Xuqiang, Cheng Ya, Jin Yizhong. 3D Streamline Visualization Method Based on Clustering Fusion[J]. Journal of System Simulation, 2024, 36(3): 625-635.

Figures/Tables 11

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数据集	图5(a)	图5(b)	图5(c)	图5(d)	图5(e)
1	0.004 7	0.004 8	0.005 2	0.005 0	0.014 1
2	0.004 4	0.004 6	0.005 2	0.005 1	0.014 5
3	0.003 4	0.003 6	0.004 5	0.004 3	0.013 7
4	0.004 5	0.0048	0.005 3	0.004 6	0.012 9

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