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A parallel high-precision critical point detection and location for large-scale 3D flow field on the GPU

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Abstract

Automatic analysis of large-scale 3D flow field promotes the combination of topological analysis and integration curve analysis methods. Critical point detection and location are one of the key core algorithms of topological analysis. Dealing with large-scale flow fields in parallel, eliminating memory constraints, and obtaining high-precision position of critical points robustly are still primary challenges. Therefore, a novel parallel detection and location method on the GPU is proposed and denoted by HybridDualCP. It uses the duality property of the cartesian coordinate system and the velocity vector space inside each grid cell. It achieves high locality, has the ability of large-scale parallelization, and can quickly detect and locate critical points in large-scale 3D flow fields. Furthermore, based on the Inverse Distance Weighting (IDW) interpolation algorithm in the dual space, it eliminates the computation of solving systems of high-order or linear equations and can support any dimensional simplex mesh type. HybridDualCP provides the position classification of critical points relative to the grid cell by explicitly considering the inside/facet/edge/vertex. By detecting critical points in 29 typical 3D flow fields, HybridDualCP is faster, more robust and comprehensive than currently widely used methods. This paper also shows the effect of streamline visualization guided by critical points and the necessity of distinguishing critical points at different locations relative to the grid cell.

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Acknowledgements

We thank Bhatia Harsh for his patient help by email communication. We appreciate the anonymous reviewers for their comments and suggestions on the paper. This work was supported by the National Science Foundation of China (91752111).

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Authors and Affiliations

  1. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications (BUPT), Beijing, 100876, CO, China

    Zhi-Bin Huang, Guang-Tao Fu & Ling-jing Cao

  2. China Academy of Aerospace Aerodynamics, Beijing, 100074, CO, China

    Min Yu & Wu-Bing Yang

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  1. Zhi-Bin Huang
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  2. Guang-Tao Fu
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  3. Ling-jing Cao
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  4. Min Yu
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Correspondence to Zhi-Bin Huang.

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Huang, ZB., Fu, GT., Cao, Lj. et al. A parallel high-precision critical point detection and location for large-scale 3D flow field on the GPU. J Supercomput 78, 9642–9667 (2022). https://doi.org/10.1007/s11227-021-04220-6

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