Informed component label algorithm for robust identification of connected components with volume-of-fluid method computers and fluids
Informed component label algorithm for robust identification of connected components with volume-of-fluid method computers and fluids
The connected component labeling technique (CCL), which labels regions of connected Eulerian field data, will inaccurately identify closely spaced components when applied to the volume-of-fluid function. We present two modifications to the CCL that improve its robustness and accuracy. This Informed Component Labeling algorithm (ICL) incorporates the normal and uses multilevel thresholding to improve and refine connectivity decisions for components with spacing just larger than the grid size. Through detailed verification and validation using synthetic volume fraction data, we show that the ICL algorithm removes the bias to larger components, provide guidelines for its use, and estimate its error bounds for the smallest components. The ICL produces zero standard deviation in the number of components identified for those with radius larger than twice the grid size and can reduce it by ~38% for smaller components. The modifications that comprise the ICL can be applied to any existing CCL algorithm with a known increase in computational cost. It enables robust identification of connected components for accurate transfer of information in mixed Eulerian-Lagrangian methods and statistical analysis that use the volume-of-fluid function.
Connected component labeling, Eulerian–Lagrangian data, Volume-of-fluid method
Hendrickson, Kelli
358d7339-2b4c-4b62-820f-a9be37434251
Weymouth, Gabriel
b0c85fda-dfed-44da-8cc4-9e0cc88e2ca0
Yue, Dick K.-P.
efbe9a39-3cdc-4381-8a65-e72b4a590935
30 January 2020
Hendrickson, Kelli
358d7339-2b4c-4b62-820f-a9be37434251
Weymouth, Gabriel
b0c85fda-dfed-44da-8cc4-9e0cc88e2ca0
Yue, Dick K.-P.
efbe9a39-3cdc-4381-8a65-e72b4a590935
Hendrickson, Kelli, Weymouth, Gabriel and Yue, Dick K.-P.
(2020)
Informed component label algorithm for robust identification of connected components with volume-of-fluid method computers and fluids.
Computers and Fluids, 197, [104373].
(doi:10.1016/j.compfluid.2019.104373).
Abstract
The connected component labeling technique (CCL), which labels regions of connected Eulerian field data, will inaccurately identify closely spaced components when applied to the volume-of-fluid function. We present two modifications to the CCL that improve its robustness and accuracy. This Informed Component Labeling algorithm (ICL) incorporates the normal and uses multilevel thresholding to improve and refine connectivity decisions for components with spacing just larger than the grid size. Through detailed verification and validation using synthetic volume fraction data, we show that the ICL algorithm removes the bias to larger components, provide guidelines for its use, and estimate its error bounds for the smallest components. The ICL produces zero standard deviation in the number of components identified for those with radius larger than twice the grid size and can reduce it by ~38% for smaller components. The modifications that comprise the ICL can be applied to any existing CCL algorithm with a known increase in computational cost. It enables robust identification of connected components for accurate transfer of information in mixed Eulerian-Lagrangian methods and statistical analysis that use the volume-of-fluid function.
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Accepted/In Press date: 7 November 2019
e-pub ahead of print date: 9 November 2019
Published date: 30 January 2020
Additional Information:
Funding Information:
This work was funded by the Office of Naval Research N00014-10-1-0630 under the guidance of Dr. Ki-Han Kim and Dr. Thomas C. Fu. Appendix A
Publisher Copyright:
© 2019
Keywords:
Connected component labeling, Eulerian–Lagrangian data, Volume-of-fluid method
Identifiers
Local EPrints ID: 435793
URI: http://eprints.soton.ac.uk/id/eprint/435793
ISSN: 0045-7930
PURE UUID: c8b5c028-4b94-4b19-94b9-6bf940ed04c2
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Date deposited: 20 Nov 2019 17:30
Last modified: 06 Jun 2024 04:08
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Contributors
Author:
Kelli Hendrickson
Author:
Dick K.-P. Yue
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