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TECHNICAL PAPERS

Computational Study of the Wake and Contaminant Transport of a Walking Human

[+] Author and Article Information
Brian A. Edge1

Computational Mechanics Division, Applied Research Laboratory and Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, State College, PA 16804bae127@psu.edu

Eric G. Paterson

Computational Mechanics Division, Applied Research Laboratory and Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, State College, PA 16804eric-paterson@psu.edu

Gary S. Settles

Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA 16802gss2@psu.edu

1

Corresponding author.

J. Fluids Eng 127(5), 967-977 (Apr 25, 2005) (11 pages) doi:10.1115/1.2013291 History: Received November 11, 2004; Revised April 20, 2005; Accepted April 25, 2005

The unsteady aerodynamic wake of a human is studied using a time-accurate computational fluid dynamics simulation. Transport of a scalar contaminant, which originates on the body, is also considered. An existing Reynolds-averaged Navier-Stokes solver is modified to include energy, scalar-transport, and thermal buoyancy effects. Structured overset grids are used to discretize the geometry and the flow field. Results indicate two distinct wake regions: an unsteady bluff-body wake behind the torso which is characterized by a mean recirculation zone, and a region of unsteady vortex shedding behind the legs which is dominated by a “jet” of air formed between the legs. A significant downwash occurs behind the body which has the effect of laterally spreading the lower portions of the wake. The magnitude of the scalar contaminant is shown to decay exponentially within the wake and is found to be highly dependent upon the source location.

Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

Computational geometry shown with the nondimensional units (1.0unit=0.58m)

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Figure 2

Front (a) and side (b) details of the overset-grid system

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Figure 3

Overset-grid layout: horizontal slices at various vertical elevations

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Figure 4

Layout of the surface grids on the computational geometry

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Figure 5

Top (a) and side (b) views of the full overset-grid domain

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Figure 6

a View of the fine (a), intermediate (b), and coarse (c) surface grids which were used in the grid refinement study

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Figure 7

Coefficient of pressure in the lateral direction versus nondimensional time as computed on the medium grid

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Figure 8

Computed-mean U velocity for three different sample sizes versus downstream distance X. The line types define the sample set: — 150 time units, – – – 100 time units, and – - – 50 time units. The symbols define the location of the sample: ◻ head, ▵ chest, ◇ stomach, and 엯 feet. Uavg is the nondimensional average velocity and X is the nondimensional distance downstream of the body.

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Figure 9

Computed-mean U velocity of the three grid resolutions versus downstream distance at nondimensional time 50. The line types define the grid resolution: — fine grid, – – – medium grid, and – - – coarse grid. The symbols define the location of the sample: ◻ head, ▵ chest, ◇ stomach, 엯 feet.

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Figure 10

Computed-mean scalar quantity for the three grid resolutions versus downstream distance at nondimensional time 50. The line types define the grid resolution: — fine grid, – – – medium grid, and – - – coarse grid. The symbols define the location of the sample: ◻ head ▵ chest, ◇ stomach, and 엯 feet. The values of the computed-mean scalar quantities go to a limit of 1 at the body source X=0.

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Figure 11

Computed-mean U velocity at nondimensional time 50 for two different time step sizes. The line types represent the nondimensional time step size: — Δt=0.005, – – – Δt=0.01. The symbols represent the location of the sample: ◻ head, ▵ chest, ◇ stomach, and 엯 feet.

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Figure 12

Computed-mean scalar quantity at nondimensional time 50 for two different time step sizes. The line types represent the nondimensional time step size: — 0.005, and – – – Δt=0.01. The symbols represent the location of the sample: ◻ head, ▵ chest, ◇ stomach, and 엯 feet.

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Figure 13

Computed-mean (a) and instantaneous (b) total velocity magnitude with tangential streamlines in a vertical slice through the body centerline

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Figure 14

Computed-mean (a) and instantaneous (b) total velocity magnitude with tangential streamlines in a horizontal slice through the stomach

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Figure 15

Computed-mean (a) and instantaneous (b) total velocity magnitude with tangential streamlines in a horizontal slice through the ankles

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Figure 16

Instantaneous vorticity for a vertical slice through the body centerline. Positive vorticity is bounded by solid lines and negative vorticity is bounded by dashed lines.

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Figure 17

Instantaneous vorticity for a horizontal slice through the stomach. Positive vorticity is bounded by solid lines and negative vorticity is bounded by dashed lines.

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Figure 18

Instantaneous vorticity for a horizontal slice through the ankles. Positive vorticity is bounded by solid lines and negative vorticity is bounded by dashed lines.

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Figure 19

Computed-mean (a) and instantaneous (b) scalar magnitude contours from a vertical slice through the centerline of the body

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Figure 20

Computed-mean (a) and instantaneous (b) scalar magnitude contours from a horizontal slice through the stomach

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Figure 21

Computed-mean (a) and instantaneous (b) scalar magnitude contours from a horizontal slice through the ankles

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Figure 22

Sketches of the significant physics observed in the flow visualization experiments performed by the Penn State Gas Dynamics Laboratory. (a) Vertical plane and (b) horizontal plane.

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Figure 23

Computed-mean (a) and instantaneous (b) scalar magnitude versus non-dimensional downstream distance X for the uniform contaminant source. Symbols define sample location: ◻ head, ▵ chest, ◇ stomach, and 엯 feet.

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Figure 24

Computed-mean (a) and instantaneous (b) scalar magnitude versus X for the localized contaminant source. Symbols define sample location: ◻ head, ▵ chest, ◇ stomach, and 엯 feet.

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