Hybridization of front tracking and level set for multiphase flow simulations: a machine learning approachHybridization of front tracking and level set for multiphase flow simulations: a machine learning approach
- Other Titles
- Hybridization of front tracking and level set for multiphase flow simulations: a machine learning approach
- Authors
- Yoon, Ikroh; Chergui, Jalel; Juric, Damir; Shin, Seungwon
- Issue Date
- Sep-2023
- Publisher
- Korean Society of Mechanical Engineers
- Keywords
- Artificial intelligence; Front tracking; Level set; Machine learning; Multiphase flow; Numerical simulation
- Citation
- Journal of Mechanical Science and Technology, v.37, no.9, pp 4749 - 4756
- Pages
- 8
- Journal Title
- Journal of Mechanical Science and Technology
- Volume
- 37
- Number
- 9
- Start Page
- 4749
- End Page
- 4756
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/31674
- DOI
- 10.1007/s12206-023-0829-3
- ISSN
- 1738-494X
1976-3824
- 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. © 2023, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.
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Collections - College of Engineering > Department of Mechanical and System Design Engineering > 1. Journal Articles
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