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RESEARCH PAPERS

Large Scale Urban Contaminant Transport Simulations With Miles

[+] Author and Article Information
Gopal Patnaik

Laboratory for Computational Physics and Fluid Dynamics, Naval Research Laboratory, Washington, DC 20375patnaik@lcp.nrl.navy.mil

Jay P. Boris

Laboratory for Computational Physics and Fluid Dynamics, Naval Research Laboratory, Washington, DC 20375boris@lcp.nrl.navy.mil

Theodore R. Young

Laboratory for Computational Physics and Fluid Dynamics, Naval Research Laboratory, Washington, DC 20375young@lcp.nrl.navy.mil

Fernando F. Grinstein

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

J. Fluids Eng 129(12), 1524-1532 (Apr 27, 2007) (9 pages) doi:10.1115/1.2801368 History: Received February 13, 2007; Revised April 27, 2007

Airborne contaminant transport in cities presents challenging new requirements for computational fluid dynamics. The unsteady flow involves very complex geometry and insufficiently characterized boundary conditions, and yet the challenging and timely nature of the overall problem demands that the turbulence be included efficiently with an absolute minimum of extra memory and computing time requirements. This paper describes the monotone integrated large eddy simulation methodology used in NRL’s FAST3D-CT (CT is contaminant transport) simulation model for urban CT and focuses on critical validation issues that need to be addressed to achieve practical predictability. Progress in validation studies benchmarking with flow data from wind-tunnel urban model simulations and actual urban field studies are reported. Despite inherent physical uncertainties and current model tradeoffs, it is clearly possible to achieve some degree of reliable prediction.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 3

Instantaneous distributions of the tracer concentration at the 2cm height plane. Release occurred at a location behind the first cube in the vicinity of the centerline plane. Flow direction is from bottom to top.

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Figure 4

Average rms streamwise velocity fluctuation profiles upstream of the first cube at the matching location (y=−0.225). Note that the cubes enhance fluctuation levels upstream.

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Figure 5

Average velocity in the canyon between the second and the third cube (second canyon). Symbols are experimental results at the corresponding profile location.

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Figure 6

Average rms streamwise velocity fluctuations in the canyon between the second and the third cube (second canyon). Absence of inflow fluctuations causes a significant underprediction of fluctuation levels.

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Figure 7

Average concentration profiles are shown at selected stations located in the first three canyons. Corresponding experimental results are shown by symbols.

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Figure 8

Four of the eight FAST3D-CT realizations of a single event with different actual release times are compared with Los Angeles experimental sampler data

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Figure 9

Ensemble concentration distribution functions from six FAST3D-CT simulated realizations at the location of Sampler 25 in run LA 8. The vertical lines denoted with “E” indicate the experimental measurement and the short crosshatched bar is the threshold value of 20ppt used for this comparison.

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Figure 10

Geometry of spires and surface roughness elements used to generate the turbulent urban boundary layer in the University of Hamburg Wind Tunnel. (Figure courtesy of B. Leitl, U. Hamburg.)

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Figure 11

Comparison of mean velocities (left) and turbulent kinetic energy (right). The figure shows the sensitivity of the TKE to the amount of roughness modeled on the lower surface. The intensity of the TKE increases with the amount of roughness.

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Figure 1

Contaminant dispersion from an instantaneous release in Times Square, New York City as predicted by the FAST3D-CT MILES model. Concentrations shown at 3min, 5min, 7min, and 15min after release.

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Figure 2

USEPA Meteorological Wind Tunnel: 3D Array of Buildings (courtesy of Michael Brown, LANL). Previous reported studies used the USEPA wind-tunnel data to test flow simulation models but did not address their effects on CT.

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