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A novel Omega-driven dynamic PANS model

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Abstract

A novel Omega (Ω) -driven dynamic partially-averaged Navier-Stokes (PANS) model is proposed in this paper. The ratio of the modeled-to-total turbulent kinetic energies fk is dynamically adjusted by the rigid vorticity ratio (the ratio of the rigid vorticity to the total vorticity), the key parameter of the Ω vortex identification method. Three classical flow cases with rotation and curvature are used to test the model. The results show that the turbulent viscosity is effectively adjusted by the new dynamic fk and the LES-like mode is activated, which can help the revelation of more turbulence information and improve the prediction accuracy. The new PANS model does not contain any explicit dependency on the grid size and enjoys good adaptability to the flow fields, and can be used for efficient engineering computations of the turbulent flows in the hydraulic machinery.

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References

  1. Girimaji S. S., Srinivasan R., Jeong E. PANS turbulence model for seamless transition between RANS and LES: fixed-point analysis and preliminary results [C]. 4th ASME-JSME Joint Fluids Engineering Conference, Honolulu, Hawaii, USA, 2003.

    Google Scholar 

  2. Fröhlich J., Terzi D. V. Hybrid LES/RANS methods for the simulation of turbulent flows [J]. Progress in Aerospace Sciences, 2008, 44(5): 349–377.

    Article  Google Scholar 

  3. Chaouat B. The state of the art of hybrid RANS/LES modeling for the simulation of turbulent flows [J]. Flow, Turbulence and Combustion, 2017, 99(2): 279–327.

    Article  Google Scholar 

  4. Frendi A., Tosh A., Girimaji S. S. Flow past a backwardfacing step: Comparison of PANS, DES and URANS results with experiments [J]. International Journal for Computational Methods in Engineering Science and Mechanics, 2006, 8(1): 23–38.

    Article  Google Scholar 

  5. Jeong E., Girimaji S. S. Partially averaged Navier-Stokes (PANS) method for turbulence simulations-flow past a square cylinder [J]. Journal of Fluids Engineering, 2010, 132(12): 121203.

    Article  Google Scholar 

  6. Pereira F. S., Eça L., Vaz G. Investigating the effect of the closure in partially-averaged Navier-Stokes equations [J]. Journal of Fluids Engineering, 2019, 141(12): 121402.

    Article  Google Scholar 

  7. Ji B., Luo X., Wu Y. et al. Partially-averaged Navier- Stokes method with modified k-? model for cavitating flow around a marine propeller in a non-uniform wake [J]. International Journal of Heat and Mass Transfer, 2012, 55(23-24): 6582–6588.

    Article  Google Scholar 

  8. Liu H., Wang J., Wang Y. et al. Partially-averaged Navier- Stokes model for predicting cavitating flow in centrifugal pump [J]. Engineering Applications of Computational Fluid Mechanics, 2014, 8(2): 319–329.

    Article  Google Scholar 

  9. Liu J. T., Wu Y. L., Wang L. Q. Instability analysis of a model pump-turbine with MGV based on nonlinear partially averaged Navier-Stokes methods [J]. Advances in Mechanical Engineering, 2015, 5: 710769.

    Article  Google Scholar 

  10. Elmiligui A., Abdol-Hamid K. S., Massey S. J. et al. Numerical study of flow past a circular cylinder using RANS, hybrid RANS/LES and PANS formulations [C]. 22nd Applied Aerodynamics Conference and Exhibit, Providence, Rhode Island, USA, 2004.

    Google Scholar 

  11. Girimaji S. S., Abdol-Hamid K. S. Partially-averaged Navier-Stokes model for turbulence: implementation and validation [C]. 43rd AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA, 2005.

    Google Scholar 

  12. Foroutan H., Yavuzkurt S. A partially-averaged Navier- Stokes model for the simulation of turbulent swirling flow with vortex breakdown [J]. International Journal of Heat and Fluid Flow, 2014, 50: 402–416.

    Article  Google Scholar 

  13. Schiestel R., Dejoan A. Towards a new partially integrated transport model for coarse grid and unsteady turbulent flow simulations [J]. Theoretical and Computational Fluid Dynamics, 2005, 18(6): 443–468.

    Article  Google Scholar 

  14. Luo D. H., Yan C., Zheng W. L. et al. A new PANS model for unsteady separated flow simulations [J]. Applied Mechanics and Materials, 2014, 721: 182–186.

    Article  Google Scholar 

  15. Wilcox D. C. Turbulence modeling for CFD [M]. La Canada Flintridge, California, USA: DCW Industries, 2006.

    Google Scholar 

  16. Baglietto E., Lenci G., Concu D. STRUCT: A secondgeneration URANS approach for effective design of advanced systems [C]. ASME 2017 Fluids Engineering Division Summer Meeting, Waikoloa, Hawaii, USA, 2017.

    Google Scholar 

  17. Liu Y. W., Yan H., Fang L. et al. Modified k-ω model using kinematic vorticity for corner separation in compressor cascades [J]. Science China Technological Sciences, 2016, 59(5): 795–806.

    Article  Google Scholar 

  18. Liu C., Wang Y. Q., Yang Y. et al. New omega vortex identification method[J]. Science China Physics, Mechanics and Astronomy, 2016, 59(8): 684711.

    Article  Google Scholar 

  19. 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 

  20. Zhang Y. N., Qiu X., Chen F. P. et al. A selected review of vortex identification methods with applications [J]. Journal of Hydrodynamics, 2018, 30(5): 767–779.

    Article  Google Scholar 

  21. Liu J. M., Wang Y. Q., Gao Y. S. et al. Galilean invariance of Omega vortex identification method [J]. Journal of Hydrodynamics, 2019, 31(2): 249–255.

    Article  Google Scholar 

  22. Kolár V. Vortex identification: New requirements and limitations [J]. International Journal of Heat and Fluid Flow, 2007, 28(4): 638–652.

    Article  Google Scholar 

  23. Spalart P. R., Shur M. On the sensitization of turbulence models to rotation and curvature [J]. Aerospace Science and Technology, 1997, 1(5): 297–302.

    Article  Google Scholar 

  24. Belian A., Chkhetiani O., Golbraikh E. et al. Helical turbulence: Turbulent viscosity and instability of the second moments [J]. Physica A, 1998, 258: 55–68.

    Article  Google Scholar 

  25. Mockett C., Fuchs M., Garbaruk A. et al. Two non-zonal approaches to accelerate RANS to LES transition of free shear layers in DES [J]. Progress in Hybrid RANS-LES Modelling, 2015, 130: 187–201.

    Article  Google Scholar 

  26. Grossmann S., Lohse D., Sun C. High-Reynolds number Taylor-Couette turbulence [J]. Annual Review of Fluid Mechanics, 2016, 48: 53–80.

    Article  MathSciNet  Google Scholar 

  27. Dong S. Direct numerical simulation of turbulent Taylor- Couette flow [J]. Journal of Fluid Mechanics, 2007, 587: 373–393.

    Article  MathSciNet  Google Scholar 

  28. Bazilevs Y., Akkerman I. Large eddy simulation of turbulent Taylor-Couette flow using isogeometric analysis and the residual-based variational multiscale method [J]. Journal of Computational Physics, 2010, 229(9): 3402–3414.

    Article  MathSciNet  Google Scholar 

  29. 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 

  30. 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 

  31. Gao Y., Liu C. Rortex and comparison with eigenvaluebased vortex identification criteria [J]. Physics of Fluids, 2018, 30(8): 085107.

    Article  Google Scholar 

  32. Guo S. H. Partially-averaged Navier-Stokes (PANS) approach for study of rotating turbulence [D]. Beijing, China: China Agricultural University, 2018(in Chinese).

    Google Scholar 

  33. Javadi A., Nilsson H. LES and DES of strongly swirling turbulent flow through a suddenly expanding circular pipe [J]. Computers and Fluids, 2015, 107: 301–313.

    Article  Google Scholar 

  34. Dellenback P. A., Metzger D. E., Neitzel G. P. Measurements in turbulent swirling flow through an abrupt axisymmetric expansion [J]. AIAA Journal, 1988, 26(6): 669–681.

    Article  Google Scholar 

  35. Byskov R. K., Jacobsen C. B., Pedersen N. Flow in a centrifugal pump impeller at design and off-design donditions-part I: Particle image velocimetry (PIV) and laser doppler velocimetry (LDV) measurements [J]. Journal of Fluids Engineering, 2003, 125(1): 61–72.

    Article  Google Scholar 

  36. Byskov R. K., Jacobsen C. B., Pedersen N. Flow in a centrifugal pump impeller at design and off-design donditions-part II: large eddy simulations [J]. Journal of Fluids Engineering, 2003, 125(1): 73–83.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China

    Chao-yue Wang, Fu-jun Wang, Ben-hong Wang, Yuan Tang & Hao-ru Zhao

  2. Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, Beijing, 100083, China

    Fu-jun Wang

Authors
  1. Chao-yue Wang
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  2. Fu-jun Wang
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  3. Ben-hong Wang
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  4. Yuan Tang
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  5. Hao-ru Zhao
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Corresponding author

Correspondence to Fu-jun Wang.

Additional information

Project supported by the National Natural Science Foundation of China (Grant Nos. 51836010, 51779258 and 51839001), the National Key Research and Development Program of China (Grant No. 2018YFB0606103) and the Nature Science Foundation of Beijing (Grnat No. 3182018).

Biography: Chao-yue Wang (1993-), Male, Ph. D. Candidate

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Wang, Cy., Wang, Fj., Wang, Bh. et al. A novel Omega-driven dynamic PANS model. J Hydrodyn 32, 710–716 (2020). https://doi.org/10.1007/s42241-020-0052-y

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  • DOI: https://doi.org/10.1007/s42241-020-0052-y

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