Hostname: page-component-745bb68f8f-cphqk Total loading time: 0 Render date: 2025-01-24T21:48:27.606Z Has data issue: false hasContentIssue false
  • English
  • Français

Wall-resolved wavelet-based adaptive large-eddy simulation of bluff-body flows with variable thresholding

Published online by Cambridge University Press:  05 January 2016

Giuliano De Stefano ,
Alireza Nejadmalayeri  and
Oleg V. Vasilyev
Giuliano De Stefano
Affiliation:
Dipartimento di Ingegneria Industriale e dell’Informazione, Seconda Università di Napoli, I 81031 Aversa, Italy
Alireza Nejadmalayeri
Affiliation:
FortiVenti Inc., Suite 404, 999 Canada Place, Vancouver, BC, V6C 3E2, Canada
Oleg V. Vasilyev*
Affiliation:
Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
*
†Email address for correspondence: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The wavelet-based eddy-capturing approach with variable thresholding is extended to bluff-body flows, where the obstacle geometry is enforced through Brinkman volume penalization. The use of a spatio-temporally varying threshold allows one to perform adaptive large-eddy simulations with the prescribed fidelity on a near optimal computational mesh. The space–time evolution of the threshold variable is achieved by solving a transport equation based on the Lagrangian path-line diffusive averaging methodology. The coupled wavelet-collocation/volume-penalization approach with variable thresholding is illustrated for a turbulent incompressible flow around an isolated stationary prism with square cross-section. Wavelet-based adaptive large-eddy simulations supplied with the one-equation localized dynamic kinetic energy-based model are successfully performed at moderately high Reynolds number. The present study demonstrates that the proposed variable thresholding methodology for wavelet-based modelling of turbulent flows around solid obstacles is feasible, accurate and efficient.

JFM classification

Mathematical Foundations: Computational methods Turbulent Flows: Turbulence modelling Turbulent Flows: Turbulence simulation
Type
Papers
Information
Journal of Fluid Mechanics , Volume 788 , 10 February 2016 , pp. 303 - 336
Check for updates
Copyright
© 2016 Cambridge University Press 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Angot, P., Bruneau, C.-H. & Fabrie, P. 1999 A penalization method to take into account obstacles in incompressible viscous flows. Numer. Math. 81, 497–520.Google Scholar
Bardina, J., Ferziger, J. H. & Reynolds, W. C.1980 Improved subgrid-scale models for large-eddy simulation. AIAA Paper 80-1357.Google Scholar
Brun, C., Aubrun, S., Goossens, T. & Ravier, Ph. 2008 Coherent structures and their frequency signature in the separated shear layer on the sides of a square cylinder. Flow Turbul. Combust. 81, 97–114.CrossRefGoogle Scholar
Carbou, G. & Fabrie, P. 2003 Boundary layer for a penalization method for viscous incompressible flow. Adv. Differ. Equ. 8 (12), 1453–1480.Google Scholar
De Stefano, G. & Vasilyev, O. V. 2010 Stochastic coherent adaptive large eddy simulation of forced isotropic turbulence. J. Fluid Mech. 646, 453–470.Google Scholar
De Stefano, G. & Vasilyev, O. V. 2012 A fully adaptive wavelet-based approach to homogeneous turbulence simulation. J. Fluid Mech. 695, 149–172.CrossRefGoogle Scholar
De Stefano, G. & Vasilyev, O. V. 2013 Wavelet-based adaptive large eddy simulation with explicit filtering. J. Comput. Phys. 238, 240–254.CrossRefGoogle Scholar
De Stefano, G. & Vasilyev, O. V. 2014 Wavelet-based adaptive simulations of three-dimensional flow past a square cylinder. J. Fluid Mech. 748, 433–456.CrossRefGoogle Scholar
De Stefano, G., Vasilyev, O. V. & Goldstein, D. E. 2008 Localized kinetic-energy-based models for stochastic coherent adaptive large eddy simulation of turbulent flows. Phys. Fluids 20, 045102,1–14.Google Scholar
Durao, D. F. G., Heitor, M. V. & Pereira, J. C. F. 1988 Measurements of turbulent and periodic flows around a square cross-section cylinder. Exp. Fluids 6, 298–304.Google Scholar
Farge, M. 1992 Wavelet transforms and their applications to turbulence. Annu. Rev. Fluid Mech. 24 (1), 395–458.CrossRefGoogle Scholar
Farge, M., Schneider, K. & Kevlahan, N. 1999 Non-Gaussianity and coherent vortex simulation for two-dimensional turbulence using an adaptive orthogonal wavelet basis. Phys. Fluids 11 (8), 2187–2201.CrossRefGoogle Scholar
Fröhlich, J. & von Terzi, D. 2008 Hybrid LES/RANS methods for the simulation of turbulent flows. Prog. Aerosp. Sci. 44 (5), 349–377.Google Scholar
Fureby, C., Alin, N., Wikström, N., Menon, S., Svanstedt, N. & Persson, L. 2004 Large-eddy simulation of high-Reynolds-number wall-bounded flows. AIAA J. 42 (3), 457–468.Google Scholar
Germano, M., Piomelli, U., Moin, P. & Cabot, W. H. 1991 A dynamic subgrid-scale eddy viscosity model. Phys. Fluids A 3 (7), 1760–1765.Google Scholar
Ghosal, S., Lund, T. S., Moin, P. & Akselvoll, K. 1995 A dynamic localization model for large-eddy simulation of turbulent flows. J. Fluid Mech. 286, 229–255.Google Scholar
Goldstein, D. E., Kevlahan, N. K.-R. & Vasilyev, O. V. 2005 CVS and SCALES simulation of 3-D isotropic turbulence. J. Turbul. 6, 1–20.Google Scholar
Goldstein, D. E. & Vasilyev, O. V. 2004 Stochastic coherent adaptive large eddy simulation method. Phys. Fluids 16 (7), 2497–2513.CrossRefGoogle Scholar
Hunt, J. C. R., Wray, A. & Moin, P.1988 Eddies, stream, and convergence zones in turbulent flows. Center for Turbulence Research Report CTR-S88.Google Scholar
Kevlahan, N. K.-R. & Vasilyev, O. V. 2005 An adaptive wavelet collocation method for fluid–structure interaction at high Reynolds numbers. SIAM J. Sci. Comput. 26 (6), 1894–1915.Google Scholar
Krajnović, S. & Davidson, L. 2002 Large-eddy simulation of the flow around a bluff body. AIAA J. 40 (5), 927–936.Google Scholar
Lilly, D. K. 1992 A proposed modification of the Germano subgrid-scale closure method. Phys. Fluids A 4 (3), 633–635.CrossRefGoogle Scholar
Liu, S., Meneveau, C. & Katz, J. 1994 On the properties of similarity subgrid-scale models as deduced from measurements in a turbulent jet. J. Fluid Mech. 275, 83–119.Google Scholar
Lyn, D. A., Einav, S., Rodi, W. & Park, J. H. 1995 A laser-Doppler velocimetry study of ensemble-averaged characteristics of the turbulent flow near wake of a square cylinder. J. Fluid Mech. 304, 285–319.Google Scholar
Lyn, D. A. & Rodi, W. 1994 The flapping shear layer formed by flow separation from the forward of a square cylinder. J. Fluid Mech. 267, 353–376.CrossRefGoogle Scholar
Meneveau, C. 1991 Analysis of turbulence in the orthonormal wavelet representation. J. Fluid Mech. 232, 469–520.Google Scholar
Menon, S. & Kim, W.-W.1996 High-Reynolds-number flow simulations using the localized dynamic subgrid-scale model. AIAA Paper 96-0425.Google Scholar
Mimeau, C., Gallizio, F., Cottet, G.-H. & Mortazavi, I. 2015 Vortex penalization method for bluff body flows. Intl J. Numer. Meth. Fluids 79 (2), 55–83.Google Scholar
Nejadmalayeri, A., Vezolainen, A., Brown-Dymkoski, E. & Vasilyev, O. V. 2015 Parallel adaptive wavelet collocation method for PDEs. J. Comput. Phys. 298, 237–253.Google Scholar
Nejadmalayeri, A., Vezolainen, A., De Stefano, G. & Vasilyev, O. V. 2014 Fully adaptive turbulence simulations based on Lagrangian spatio-temporally varying wavelet thresholding. J. Fluid Mech. 749, 794–817.Google Scholar
Nejadmalayeri, A., Vezolainen, A. & Vasilyev, O. V. 2013 Reynolds number scaling of coherent vortex simulation and stochastic coherent adaptive large eddy simulation. Phys. Fluids 25, 110823,1–15.CrossRefGoogle Scholar
Pope, S. B. 2000 Turbulent Flows. Cambridge University Press.Google Scholar
Schneider, K. & Vasilyev, O. V. 2010 Wavelet methods in computational fluid dynamics. Annu. Rev. Fluid Mech. 42, 473–503.Google Scholar
Shirokoff, D. & Nave, J.-C. 2015 A sharp-interface active penalty method for the incompressible Navier–Stokes equations. J. Sci. Comput. 62 (1), 53–77.Google Scholar
Smagorinsky, J. 1963 General circulation experiments with the primitive equations, I. The basic experiment. Mon. Weath. Rev. 91 (3), 99–164.Google Scholar
Sohankar, A., Davidson, L. & Norberg, C. 2000 Large eddy simulation of flow past a square cylinder: comparison of different subgrid scale models. Trans. ASME J. Fluids Engng 122, 39–47.Google Scholar
Spalart, P. R. 2009 Detached-eddy simulation. Annu. Rev. Fluid Mech. 41, 181–202.CrossRefGoogle Scholar
Sweldens, W. 1998 The lifting scheme: a construction of second generation wavelets. SIAM J. Math. Anal. 29 (2), 511–546.CrossRefGoogle Scholar
Vanella, M., Rabenold, P. & Balaras, E. 2010 A direct-forcing embedded-method with adaptive mesh refinement for fluid–structure interaction problems. J. Comput. Phys. 229 (18), 6427–6449.Google Scholar
Vasilyev, O. V. & Bowman, C. 2000 Second generation wavelet collocation method for the solution of partial differential equations. J. Comput. Phys. 165, 660–693.CrossRefGoogle Scholar
Vasilyev, O. V., De Stefano, G., Goldstein, D. E. & Kevlahan, N. K.-R. 2008 Lagrangian dynamic SGS model for stochastic coherent adaptive large eddy simulation. J. Turbul. 9 (11), 1–14.Google Scholar
Vasilyev, O. V. & Kevlahan, N. K.-R. 2005 An adaptive multilevel wavelet collocation method for elliptic problems. J. Comput. Phys. 206, 412–431.Google Scholar