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Research Papers: Flows in Complex Systems

Evaluation of Large Eddy Simulation and RANS for Determining Hydraulic Performance of Disinfection Systems for Water Treatment

[+] Author and Article Information
Jie Zhang

Department of Civil
and Environmental Engineering,
University of South Florida, Tampa,
4202 E. Fowler Avenue, ENB 118,
Tampa, FL 33620
e-mail: jiez@mail.usf.edu

Andrés E. Tejada-Martínez

Department of Civil
and Environmental Engineering,
University of South Florida, Tampa,
4202 E. Fowler Avenue, ENB 118,
Tampa, FL 33620
e-mail: aetejada@usf.edu

Qiong Zhang

Department of Civil
and Environmental Engineering,
University of South Florida, Tampa,
4202 E. Fowler Avenue, ENB 118,
Tampa, FL 33620
e-mail: qiongzhang@usf.edu

1Jie Zhang is a doctoral candidate in Civil and Environmental Engineering at University of South Florida.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received August 8, 2013; final manuscript received May 7, 2014; published online September 10, 2014. Assoc. Editor: Sharath S. Girimaji.

J. Fluids Eng 136(12), 121102 (Sep 10, 2014) (9 pages) Paper No: FE-13-1483; doi: 10.1115/1.4027652 History: Received August 08, 2013; Revised May 07, 2014

Reynolds-Averaged Navier–Stokes (RANS) simulation has been demonstrated to be a powerful and efficient approach for conducting numerical assessment of the hydraulic performance of disinfection systems for water treatment at a much lower cost than physical experiments. Recently, large eddy simulation (LES) has been introduced for the first time as a potentially more accurate alternative to RANS for predicting hydraulic performance of disinfection systems such as baffled contactors (Kim et al., 2010, “Large Eddy Simulation of Flow and Tracer Transport in Multichamber Ozone Contactors,” J. Environ. Eng., 136, pp. 22–31). This gives rise to the need to carefully assess RANS and LES in order to understand under which flow characteristics LES should be recommended instead of the less computationally intensive RANS for predicting hydraulic performance of a disinfection system. To that extent, this manuscript presents results from RANS and LES simulations of flow and tracer transport in a laboratory-scale column contactor and a laboratory-scale baffled contactor. Flow fields, residence time distributions, and characteristic residence times are analyzed. LES is shown to be a more reliable strategy than RANS in simulating tracer transport in column contactors due to its ability to better predict the spatial transition to turbulence characterizing the flow. However, in baffled contactors where such transition does not occur and the flow is characterized by a quasi-steady short circuiting jet and dead zones, RANS performs on par with LES.

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Figures

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Fig. 1

Layout and computational grid of column contactor following physical experiments of Chen [37]

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Fig. 2

Layout (a) and grid (b) of baffled ozone contactor [9]

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Fig. 11

Normalized tracer concentration versus normalized time predicted by present LES and RANS (the figure on the right is the same as the figure on the left but with different range in x axis)

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Fig. 12

Cumulative normalized tracer concentration versus normalized time predicted by present LES and RANS of flow in the baffled contactor

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Fig. 4

(a) Instantaneous streamwise velocity contours from LES and (b) streamwise velocity contours from RANS in the column contactor at Re = 6900 (scenario AI). Note that, for ease of presentation, the column contactor has been split into six streamwise segments. For example, segment 1 extends from the inlet cross section up through 1 m away from the inlet. Segment 2 extends from 1 m away from the inlet, 1–2 ms away from the inlet, and so forth.

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Fig. 5

Variation of streamwise velocity over various cross sections of the column contactor for scenario AI

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Fig. 6

Normalized tracer concentration (i.e., RTD) versus normalized time for different scenarios in the column contactor

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Fig. 7

(a) Instantaneous streamwise velocity contours from LES and (b) streamwise velocity contours from RANS in the column contactor at Re = 1380 (scenario AII). See caption of Fig. 4 for explanation of the different segments.

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Fig. 8

Cumulative normalized tracer concentration versus normalized time for different scenarios in the column contactor

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Fig. 9

Comparison of cumulative residence time distributions from the present RANS and LES, the LES of Kim et al. [9], and physical experiment of Kim et al. [36] all at Re = 2740 (scenario BI)

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Fig. 10

Speed contours and streamlines from (a) RANS, (b) LES instantaneous result, and (c) LES time-averaged result at Re = 2740 (scenario BI)

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Fig. 3

Comparison of normalized tracer concentration (i.e., RTD) measured at the outlet versus normalized time from the present numerical simulation and the physical experiment of Chen [37] at Re = 6900 (scenario AI)

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