On Flux-Limiting-Based Implicit Large Eddy Simulation

[+] Author and Article Information
Fernando F. Grinstein

Applied Physics Division, Los Alamos National Laboratory, MS B259, Los Alamos, NM 87545fgrinstein@lanl.gov

Christer Fureby

 The Swedish Defence Research Agency, FOI, SE-172 90 Stockholm, Swedenfureby@foi.se

J. Fluids Eng 129(12), 1483-1492 (Jul 19, 2007) (10 pages) doi:10.1115/1.2801684 History: Received February 20, 2007; Revised July 19, 2007

Recent progress in understanding the theoretical basis and effectiveness of implicit large eddy simulation (ILES) is reviewed in both incompressible and compressible flow regimes. We use a modified equation analysis to show that the leading-order truncation error terms introduced by certain hybrid high resolution methods provide implicit subgrid scale (SGS) models similar in form to those of conventional mixed SGS models. Major properties of the implicit SGS model are related to the choice of high-order and low-order scheme components, the choice of a flux limiter, which determines how these schemes are blended locally depending on the flow, and the designed balance of the dissipation and dispersion contributions to the numerical solution. Comparative tests of ILES and classical LES in the Taylor–Green vortex case show robustness in capturing established theoretical findings for transition and turbulence decay.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 5

Volumetrically averaged kinetic energy decay in time. (a) Comparative power-law behaviors versus various ILES methods and the MM LES; (b) kinetic energy decay versus grid resolution based on the fourth-order FCT (1283 and 2563) and LR (1283) simulation data; all scales are logarithmic.

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Figure 2

(a) Flow visualizations of the TGV flow using volume renderings of λ2—the second-largest eigenvalue of the velocity gradient tensor on the 1283 grid; (b) volume visualizations of the vorticity magnitude are shown on the right at t*=62 for 1283 (top left) and 2563 resolutions. The results shown here, being representative of all methods discussed, are from the fourth-order 3D monotone FCT.

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Figure 3

Temporal evolution of the kinetic energy dissipation rate −dK∕dt from (a) the DNS (14,38), (b) the 1283 ILES and MM LES simulations, and (c) comparative representative conventional LES; (d) evolution of the mean enstrophy on 643 and 1283 simulations for second-order FCT ILES and LES (from 11); (e) −dK∕dt predictions from 643, 1283, and 2563 3D-FCT, and DNS data; (f) −dK∕dt predictions from DNS data, second-order FCT ILES, and various LES.

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Figure 4

(a) Evolution of the 3D velocity spectra E(k); (b) compensated spectra, k+5∕3E(k)




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