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Research Papers: Flows in Complex Systems

Assessment Measures for Engineering LES Applications

[+] Author and Article Information
I. Celik

Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26505ismail.celik@mail.wvu.edu

M. Klein

Energy and Power-Plant Technology (EKT), Department of Mechanical Engineering, Technical University of Darmstadt, Darmstadt 64287, Germanykleinm@ekt.tu-darmstadt.de

J. Janicka

Energy and Power-Plant Technology (EKT), Department of Mechanical Engineering, Technical University of Darmstadt, Darmstadt 64287, Germanyjanicka@ekt.tu-darmstadt.de

J. Fluids Eng 131(3), 031102 (Feb 09, 2009) (10 pages) doi:10.1115/1.3059703 History: Received January 31, 2007; Revised October 17, 2008; Published February 09, 2009

Anticipating that large eddy simulations will increasingly become the future engineering tool for research, development, and design, it is deemed necessary to formulate some quality assessment measures that can be used to judge the resolution of turbulent scales and the accuracy of predictions. In this context some new and refined measures are proposed and compared with those already published by the authors in the common literature. These measures involve (a) fraction of the total turbulent kinetic energy, (b) relative grid size with respect to Kolmogorov or Taylor scales, and (c) relative effective subgrid/numerical viscosity with respect to molecular viscosity. In addition, an attempt is made to segregate the contributions from numerical and modeling errors. Proposed measures are applied to various test cases and validated against fully resolved large eddy simulation and/or direct numerical simulation whenever possible.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Effect of mesh size on simulation results: (a) channel flow mean velocity profiles on different meshes, (b) energy spectra for the channel flow calculations, (c) turbulent kinetic energy from DNS and LES of a plane jet plotted at different Reynolds numbers along the centerline of the jet (Klein (6) and Klein (13)). Note that the channel flow simulations by Klein were performed using a second order central differencing scheme on a staggered grid with a grid stretching factor of approximately 1.05 in the wall-normal direction without any local coordinate transformation.

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Figure 2

Resolved turbulent kinetic energy profiles along the centerline of a jet; Re=4000 (based on inlet velocity=1.0 m/s and nozzle diameter=1.0 m): (a) SSM and (b) DSM

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Figure 3

Sgs-viscosity obtained from plane jet LES data

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Figure 4

Estimated Kolmogorov length scales for the plane jet data: (a) SSM and (b) DSM

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Figure 5

Apparent order of modeled contributions for vsgs and ksgs: (a) SSM and (b) DSM

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Figure 6

Estimated LES_IQ for the plane jet LES data: (a) SSM and (b) DSM

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Figure 7

Estimated LES_IQ using veff and ηh,eff for the plane jet LES data: (a) SSM and (b) DSM

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Figure 8

Estimated numerical viscosity normalized by laminar viscosity for plane jet LES data: (a) SSM and (b) DSM

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Figure 9

Estimated uncertainty in tke for the plane jet data: (a) SSM and (b) DSM. The curves labeled as DNS are those obtained by subtracting kres from kDNS.

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Figure 10

Estimated uncertainty and error with respect to DNS for a plane jet. Velocity (left) and turbulent kinetic energy (right).

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Figure 11

Resolved tke profiles: (a) without combustion and (b) with combustion. Symbols in (a) denote the same as in (b).

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Figure 12

Comparison of sgs-viscosity for cases (a) without and (b) with combustion

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Figure 13

Estimated LES_IQ based on turbulent kinetic energy: (a) without combustion and (b) with combustion

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Figure 14

Estimated Kolmogorov scales: (a) without combustion and (b) with combustion

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