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Hybridization of front tracking and level set for multiphase flow simulations: a machine learning approach

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Abstract

A machine learning (ML) based approach is proposed to hybridize two well-established methods for multiphase flow simulations: the front tracking (FT) and the level set (LS) methods. Based on the geometric information of the Lagrangian marker elements which represents the phase interface in FT simulations, the distance function field, which is the key feature for describing the interface in LS simulations, is predicted using an ML model. The trained ML model is implemented in our conventional numerical framework, and we finally demonstrate that the FT-based interface representation can easily and immediately be switched to an LS-based representation whenever needed during the simulation period.

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Abbreviations

G :

Geometric information vector in FT simulation

ϕ :

Distance function in LS simulation

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00244322) and support through computing time at the Institut du Developpement et des Ressources en Informatique Scientifique (IDRIS) of the Centre National de la Recherche Scientifique (CNRS), coordinated by GENCI (Grand Equipement National de Calcul Intensif) Grant 2022 A0142B06721.

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Authors and Affiliations

  1. Korea Institute of Marine Science and Technology Promotion (KIMST), Seoul, 06775, Korea

    Ikroh Yoon

  2. Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Université Paris Saclay, Orsay, 91400, France

    Jalel Chergui & Damir Juric

  3. Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, CB3 0WA, UK

    Damir Juric

  4. Department of Mechanical and System Design Engineering, Hongik University, Seoul, 04066, Korea

    Seungwon Shin

Authors
  1. Ikroh Yoon
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  2. Jalel Chergui
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  3. Damir Juric
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  4. Seungwon Shin
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Corresponding author

Correspondence to Seungwon Shin.

Additional information

Ikroh Yoon received his Ph.D. from Hongik University in Seoul, Korea, in 2020. Dr. Yoon is currently a Principal Researcher and a programme specialist at Korea Institute of Marine Science and Technology Promotion (KIMST) and United Nations Educational, Scientific and Cultural Organization, respectively.

Jalel Chergui received his Ph.D. from Paris-Sorbonne University at Paris VI, France. Dr. Chergui is currently a Senior Research Engineer in Computer Methods and Applied Fluid Mechanics at Centre National de la Recherche Scientifique (LISN/CNRS).

Damir Juric, who received his Ph.D. from the University of Michigan in 1996, is currently a Senior Researcher at the Centre National de la Recherche Scientifique (CNRS) in France as well as a Visiting Fellow at Churchill College, University of Cambridge in the Department of Applied Mathematics and Theoretical Physics (DAMTP).

Seungwon Shin received his Ph.D. from Georgia Tech in 2002. Dr. Shin is currently a Professor at the School of Mechanical and System Design Engineering at Hongik University in Seoul, Korea.

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Yoon, I., Chergui, J., Juric, D. et al. Hybridization of front tracking and level set for multiphase flow simulations: a machine learning approach. J Mech Sci Technol 37, 4749–4756 (2023). https://doi.org/10.1007/s12206-023-0829-3

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  • DOI: https://doi.org/10.1007/s12206-023-0829-3

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