A consistent adaptive level set framework for incompressible two-phase flows with high density ratios and high Reynolds numbers

Yadong Zeng, Han Liu, Qiang Gao, Ann Almgren, Amneet Pal Singh Bhalla, Lian Shen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We develop a consistent adaptive framework in a multilevel collocated grid layout for simulating two-phase flows with adaptive mesh refinement (AMR). The conservative momentum equations and the mass equation are solved in the present consistent framework. This consistent mass and momentum transport treatment greatly improves the accuracy and robustness for simulating two-phase flows with a high density ratio and high Reynolds number. The interface capturing level set method is coupled with the conservative form of the Navier–Stokes equations, and the multilevel reinitialization technique is applied for mass conservation. This adaptive framework allows us to advance all variables level by level using either the subcycling or the non-subcycling method to decouple the data advancement on each level. The accuracy and robustness of the framework are validated by a variety of canonical two-phase flow problems. We demonstrate that the consistent scheme results in a numerically stable solution in flows with high density ratios (up to 106) and high Reynolds numbers (up to 106), while the inconsistent scheme exhibits nonphysical fluid behaviors in these tests. Furthermore, it is shown that the subcycling and non-subcycling methods provide consistent results and that both of them can accurately resolve the interfaces of the two-phase flows with surface tension effects. Finally, a 3D breaking wave problem is simulated to show the efficiency and significant speedup of the proposed framework using AMR.

Original languageEnglish (US)
Article number111971
JournalJournal of Computational Physics
Volume478
DOIs
StatePublished - Apr 1 2023

Bibliographical note

Funding Information:
Y. Z. H. L. Q. G. and L. S. gratefully acknowledge the support of this work by the Office of Naval Research (N00014-17-1-2658, N00014-19-1-2139, and N00014-22-1-2481) and the National Science Foundation (OCE-1924799). A. P. S. B. acknowledges support from NSF award OAC-1931368. Y. Z. and H. L. extend their special thanks to Dr. Nishant Nangia and Dr. Anqing Xuan for discussing the numerical algorithms. Y. Z. also thanks the Center for Computational Sciences and Engineering (CCSE) and the Applied Numerical Algorithms Group (ANAG) in the Berkeley Lab for their help with this study.

Funding Information:
Y. Z., H. L., Q. G., and L. S. gratefully acknowledge the support of this work by the Office of Naval Research ( N00014-17-1-2658 , N00014-19-1-2139 , and N00014-22-1-2481 ) and the National Science Foundation ( OCE-1924799 ). A. P. S. B. acknowledges support from NSF award OAC-1931368 . Y. Z. and H. L. extend their special thanks to Dr. Nishant Nangia and Dr. Anqing Xuan for discussing the numerical algorithms. Y. Z. also thanks the Center for Computational Sciences and Engineering (CCSE) and the Applied Numerical Algorithms Group (ANAG) in the Berkeley Lab for their help with this study.

Publisher Copyright:
© 2023 Elsevier Inc.

Keywords

  • Adaptive mesh refinement (AMR)
  • Consistent transport
  • High density ratio/High Reynolds number
  • Level set
  • Subcycling/Non-subcycling
  • Two-phase flow

Fingerprint

Dive into the research topics of 'A consistent adaptive level set framework for incompressible two-phase flows with high density ratios and high Reynolds numbers'. Together they form a unique fingerprint.

Cite this