Skip to main content
Log in

A Liutex-based subgrid stress model for large-eddy simulation

  • Letters
  • Published:
Journal of Hydrodynamics Aims and scope Submit manuscript

Abstract

The concept of vortex is crucial in both understanding and modeling of turbulence. For large eddy simulation (LES), the effect of small-scale eddies onto the large scales or the resolved flow field is modeled by subgrid stress models. Even though the rotating motions of fluids, i.e., vortices or eddies are central in developing turbulent models, vortex identification methods are seldom used in these models. In this study, we develop a new subgrid model based on the Liutex vector, a new quantity introduced to decompose fluid motions into rigid rotation, pure shear and stretching, and thus identify vortices. The new model is then applied in a decaying homogeneous isotropic turbulence (DHIT) and a turbulent channel flow at Reynolds number Reτ = 180. It is shown that the new model can predict accurate energy spectra compared with experiments in DHIT and give a well-matched velocity profile in turbulent channel flow without changing the form of the model. Future directions include improvement of the Liutex based model, for example developing anisotropic subgrid models, and its applications in various turbulent flows.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Liu C., Gao Y., Tian S. et al. Rortex—A new vortex vector definition and vorticity tensor and vector decompositions [J]. Physics of Fluids, 2018, 30(3): 035103.

    Article  Google Scholar 

  2. Liu C., Gao Y. S., Dong X. R. et al. Third generation of vortex identification methods: Omega and Liutex/Rortex based systems [J]. Journal of Hydrodynamics, 2019, 31(2): 205–223.

    Article  Google Scholar 

  3. Wang Y. Q., Gao Y. S., Xu H. et al. Liutex theoretical system and six core elements of vortex identification [J]. Journal of Hydrodynamics, 2020, 32(2): 197–211.

    Article  Google Scholar 

  4. Wang Y. Q., Gao Y. S., Liu J. M. et al. Explicit formula for the Liutex vector and physical meaning of vorticity based on the Liutex-Shear decomposition [J]. Journal of Hydrodynamics, 2019, 31(3): 464–474.

    Article  Google Scholar 

  5. Kolář V., Šístek J. Stretching response of Rortex and other vortex-identification schemes [J]. AIP Advances, 2019, 9: 105025.

    Article  Google Scholar 

  6. Liu J., Gao Y., Liu C. An objective version of the Rortex vector for vortex identification [J]. Physics of Fluids, 2019, 31(6): 065112.

    Article  Google Scholar 

  7. Dong X., Gao Y., Liu C. New normalized Rortex/vortex identification method [J]. Physics of Fluids, 2019, 31(1): 011701.

    Article  Google Scholar 

  8. Liu J., Liu C. Modified normalized Rortex/vortex identification method [J]. Physics of Fluids, 2019, 31(6): 061704.

    Article  Google Scholar 

  9. Gao Y. S., Liu J. M., Yu Y. et al. A Liutex based definition and identification of vortex core center lines [J]. Journal of Hydrodynamics, 2019, 31(3): 445–454.

    Article  Google Scholar 

  10. Xu H., Cai X. S., Liu C. Liutex (vortex) core definition and automatic identification for turbulence vortex strucutres [J]. Journal of Hydrodynamics, 2019, 31(5): 857–863.

    Article  Google Scholar 

  11. Gui N., Ge L., Cheng P. X. et al. Comparative assessment and analysis of Rortex vortex in swirling jets [J]. Journal of Hydrodynamics, 2019, 31(3): 495–503.

    Article  Google Scholar 

  12. Zhang Y., Liu K. H., Li J. W. et al. Analysis of the vortices in the inner flow of reversible pump turbine with the new omega vortex identification method [J]. Journal of Hydrodynamics, 2018, 30(3): 463–469.

    Article  Google Scholar 

  13. Liu C. New ideas on governing equations of fliud dynamics [J]. Journal of Hydrodynamics, 2021, 33(4): 861–866.

    Article  Google Scholar 

  14. Liu C., Liu Z. New governing equations for fluid dynamics [J]. AIP Advances, 2021, 11: 115025.

    Article  Google Scholar 

  15. Xu W. Q., Wang Y. Q., Gao Y. S. et al. Liutex similarity in turbulent boundary layer [J]. Journal of Hydrodynamics, 2019, 31(6): 1259–1262.

    Article  Google Scholar 

  16. Xu W., Wang Y., Gao Y. et al. Observation on Liutex similarity in the dissipation subrange of turbulent boundary layer [J]. Computers and Fluids, 2022, 246(4): 105613.

    Article  MathSciNet  MATH  Google Scholar 

  17. Smagorinsky J. General circulation experiments with the primitive equations: I. The basic equations [J]. Monthly Weather Review, 1963, 91: 99–164.

    Article  Google Scholar 

  18. Moin P., Kim J. Numerical investigation of turbuelnt channel flow [J]. Journal of Fluid Mechanics, 1981, 118: 341–377.

    Article  Google Scholar 

  19. Nicoud F., Ducros F. Subgrid-scale stress modelling based on the square of the velocity gradient tensor [J]. Flow, Turbulence and Combustion, 1999, 62: 183–200.

    Article  MATH  Google Scholar 

  20. Comte-Bellot G., Corrsin S. Simple Eulerian time correlation of full- and narrow-band velocity signals in grid-generated, ‘isotropic’ turbulence [J]. Journal of Fluid Mechanics, 1971, 48(2): 273–337.

    Article  Google Scholar 

  21. Laizet S., Lamballais E. High-order compact schemes for incompressible flows: A simple and efficient method with the quasi-spectral accuracy [J]. Journal of Computational Physics, 2009, 228(15): 5989–6015.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yi-qian Wang.

Additional information

Compliance with Ethical Standards

Conflict of Interest: The authors declare that they have no conflict of interest.

Informed consent: Informed consent was obtained from all individual participants included in the study.

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Project supported by the National Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 22KJB130011), the Supercomputing Center in Yancheng (Grant No. FW(W)20221001).

Biography: Yuan Ding (1997-), Female, Master Candidate, E-mail: 1664242988@qq.com

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, Y., Pang, By., Yan, Bw. et al. A Liutex-based subgrid stress model for large-eddy simulation. J Hydrodyn 34, 1145–1150 (2022). https://doi.org/10.1007/s42241-023-0085-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42241-023-0085-0

Key words

Navigation