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Research Papers: Fundamental Issues and Canonical Flows

Comparison of Experimental and RANS-Based Numerical Studies of the Decay of Grid-Generated Turbulence

[+] Author and Article Information
Ivan Torrano

Mechanical and Industrial
Manufacturing Department,
Mondragon Unibertsitatea,
Arrasate-Mondragon 20500, Spain
e-mail: itorrano@mondragon.edu

Mustafa Tutar

Basque Foundation for Science,
IKERBASQUE,
Bilbao 48011, Spain
e-mail: mtutar@mondragon.edu

Manex Martinez-Agirre

Mechanical and Industrial
Manufacturing Department,
Mondragon Unibertsitatea,
Arrasate-Mondragon 20500, Spain
e-mail: mmartinez@mondragon.edu

Anthony Rouquier, Nicolas Mordant

L.E.G.I.—U.M.R.5519 BP53,
Grenoble Cedex 9 38041, France

Mickael Bourgoin

L.E.G.I.—U.M.R.5519 BP53,
Grenoble Cedex 9 38041, France
e-mail: mickael.bourgoin@legi.grenoble-inp.fr

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received July 28, 2014; final manuscript received January 21, 2015; published online March 9, 2015. Assoc. Editor: Alfredo Soldati.

J. Fluids Eng 137(6), 061203 (Jun 01, 2015) (12 pages) Paper No: FE-14-1408; doi: 10.1115/1.4029726 History: Received July 28, 2014; Revised January 21, 2015; Online March 09, 2015

This paper presents both experimental and numerical studies of the statistical properties of turbulent flows at moderate Reynolds number (Reλ = 100) in the context of grid-generated turbulence. In spite of the popularity of passive grids as turbulence generators, their design relies essentially on empirical laws. Here, we propose to test the ability of simple numerical simulations to capture the large scale properties (root-mean-square (rms) velocity, turbulence decay, pressure drop, etc.) of the turbulence downstream a passive grid. With this purpose, experimental measurements are compared with the three-dimensional (3D) Reynolds-Averaged Navier–Stokes (RANS) equations based turbulence model simulations. To better modeling of energy cascade of turbulence, different turbulence models, mesh resolutions, and turbulence model constants, which are determined in accordance with the experimentally measured corresponding values, are used. Both qualitative and quantitative comparisons are made with the experimental data to further assess the accuracy and capability of present numerical techniques for their use in different aerodynamic applications at moderate Re number.

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Copyright © 2015 by ASME
Topics: Turbulence
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Figures

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Fig. 1

Low-speed wind tunnel

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Fig. 2

Central line for extracting experimental data (units in mm)

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Fig. 3

The downstream evolution of turbulence flow parameters. (a) Downstream evolution of mean velocity U. (b) Downstream evolution of the fluctuation value. (c) Downstream evolution of the turbulence intensity. For all plots, the evolution over the accessible well developed region (x/M > 20) is emphasized.

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Fig. 4

The downstream evolution of turbulence flow parameters. (a) Downstream evolution of the turbulence kinetic energy. The dashed lines show the best fit by a power law over the points corresponding to x/M > 20 (these points are emphasized in the plot). The solid lines represent a cubic spline fit of all the experimental data range, which is used to estimate the local slopes. (b) Plot of the local slopes (d log(k/U2))/(d log((x − x0)/M)).

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Fig. 5

The downstream evolution of turbulence flow parameters. (a) Decay of energy dissipation rate. (b) Downstream evolution of the dissipation scale. (c) Downstream evolution of the Re number based on Taylor microscale. For all plots, the evolution over the accessible well developed region (x/M > 20) is emphasized.

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Fig. 6

The downstream evolution of turbulence flow parameters. (a) Example of spatial correlation function Ruu(x) (normalized by the variance u 2) for the data acquired at the position x/M = 30. (b) Cumulative integral of Ruu.

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Fig. 7

The downstream evolution of turbulence flow parameters. (a) Growth of the integral scale L (normalized by the mesh size) of the turbulence downstream of the grid. Solid line shows the best fit with a power law with no virtual origin, leading to an exponent of 0.51. (b) Energy dissipation rate normalized by u3rms/L.

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Fig. 8

Turbulence kinetic energy decay for mesh sensitivity analysis

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Fig. 9

Turbulence kinetic energy decay for turbulence modeling effect

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Fig. 10

The downstream evolution of turbulence flow parameters. (a) Turbulence kinetic energy decay for modeling effect of the constants modification. (b) Relative error.

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Fig. 11

Pressure coefficient evolution for the modeling effect of the constants modification

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Fig. 12

Turbulence kinetic energy (m2/s2) contour plots obtained for different turbulence models

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