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An Enhanced Particle Reseeding Algorithm for the Hybrid Particle Level Set Method in Compressible Flows

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Abstract

The hybrid particle level set (HPLS) method improves the mass conservation of level set method by using Lagrangian marker particles in under-resolved regions. In the HPLS method, the value of Lagrangian particles is calculated prior to the level set equation and used to rebuild the embedded interface. Therefore, it is essential to compute Lagrangian particles with reliable means to sustain accuracy of the level-set based interfaces. The particle method preserves exactly the characteristics line, but it cannot automatically handle the union or deletion of characteristics. So reseeding Lagrangian particles (or regulation) are required to be calculated precisely despite the particle deletion near the interface where loss of characteristics information and unintended error or mass loss may occur. Reseeding procedure of the HPLS method is not suitable for compressible flows in which characteristics are often merged or destroyed, or one material is divided into two. In this paper, reseeding procedure of the original HPLS is evaluated and modified based on the geometrical and characteristics information. The numerical results of various complex interface problems involving solids, liquids and gases demonstrate the capability of the new reseeding procedure in the HPLS method.

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Acknowledgments

The financial support of this work came from the grants provided by the Agency for Defense Development and the National Research Foundation of Korea (2012R1A6A3A01017982) contracted through the IAAT at Seoul National University.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, 151-742, South Korea

    Ki-Hong Kim, Min-cheol Gwak & Jack J. Yoh

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  1. Ki-Hong Kim
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  2. Min-cheol Gwak
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  3. Jack J. Yoh
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Correspondence to Jack J. Yoh.

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Kim, KH., Gwak, Mc. & Yoh, J.J. An Enhanced Particle Reseeding Algorithm for the Hybrid Particle Level Set Method in Compressible Flows. J Sci Comput 65, 431–453 (2015). https://doi.org/10.1007/s10915-014-9970-6

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  • DOI: https://doi.org/10.1007/s10915-014-9970-6

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