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Research Papers: Techniques and Procedures

A Method of Measuring Turbulent Flow Structures With Particle Image Velocimetry and Incorporating Into Boundary Conditions of Large Eddy Simulations

[+] Author and Article Information
Puxuan Li

Institute for Environmental Research,
Mechanical and Nuclear Engineering
Department,
Kansas State University,
Manhattan, KS 66506
e-mail: puxuanli@ksu.edu

Steve J. Eckels

Institute for Environmental Research,
Mechanical and Nuclear Engineering
Department,
Kansas State University,
Manhattan, KS 66506
e-mail: eckels@ksu.edu

Garrett W. Mann

Institute for Environmental Research,
Mechanical and Nuclear
Engineering Department,
Kansas State University,
Manhattan, KS 66506
e-mail: gmann@ksu.edu

Ning Zhang

Department of Chemical, Civil
and Mechanical Engineering,
McNeese State University,
Lake Charles, LA 70609
e-mail: nzhang@mcneese.edu

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received January 23, 2017; final manuscript received November 22, 2017; published online March 16, 2018. Assoc. Editor: Daniel Livescu.

J. Fluids Eng 140(7), 071401 (Mar 16, 2018) (11 pages) Paper No: FE-17-1051; doi: 10.1115/1.4039256 History: Received January 23, 2017; Revised November 22, 2017

The setup of inlet conditions for a large eddy simulation (LES) is a complex and important problem. Normally, there are two methods to generate the inlet conditions for LES, i.e., synthesized turbulence methods and precursor simulation methods. This study presents a new method for determining inlet boundary conditions of LES using particle image velocimetry (PIV). LES shows sensitivity to inlet boundary conditions in the developing region, and this effect can even extend into the fully developed region of the flow. Two kinds of boundary conditions generated from PIV data, i.e., steady spatial distributed inlet (SSDI) and unsteady spatial distributed inlet (USDI), are studied. PIV provides valuable field measurement, but special care is needed to estimate turbulent kinetic energy and turbulent dissipation rate for SSDI. Correlation coefficients are used to analyze the autocorrelation of the PIV data. Different boundary conditions have different influences on LES, and their advantages and disadvantages for turbulence prediction and static pressure prediction are discussed in the paper. Two kinds of LES with different subgrid turbulence models are evaluated: namely dynamic Smagorinsky–Lilly model (Lilly model) and wall modeled large eddy simulation (WMLES model). The performances of these models for flow prediction in a square duct are presented. Furthermore, the LES results are compared with PIV measurement results and Reynolds-stress model (RSM) results at a downstream location for validation.

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Figures

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Fig. 4

PIV image with vector field

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Fig. 3

PIV instrumentations

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Fig. 2

Schematic for experimental test section

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Fig. 1

Schematic for water flow loop

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Fig. 6

Boundary conditions for case 2 with PIV measurement data: (a) velocity magnitude, (b) turbulent kinetic energy, and (c)turbulent dissipation rate

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Fig. 5

Geometry with meshes: (a) whole domain, (b) zoom-in view at inlet for RSM, and (c)zoom-in view at inlet for LES

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Fig. 12

Time histories of static pressure simulated by the two LES at point 1 with USDI for case 2

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Fig. 7

Grid-independence study for RSM with SSDI

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Fig. 8

Experimental results and numerical results with SSDI on line 1: (a) velocity profiles for case 1, (b) turbulent kinetic energy for case 1, (c) velocity profiles for case 2, and (d) turbulent kinetic energy for case 2

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Fig. 9

Contours for the RSM results in cross sections: (a) velocity magnitude, (b) turbulent kinetic energy, and (c) turbulent dissipation rate

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Fig. 10

Time histories of static pressure simulated by WMLES model with SSDI at point 1 for case 2

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Fig. 11

Experimental results and numerical results with USDI on line 1: (a) velocity profiles for case 1, (b) turbulent kinetic energy for case 1, (c) velocity profiles for case 2, and (d) turbulent kinetic energy for case 2

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