physical modelling of concentration fluctuations in simple obstacle arrays robert macdonald and...

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Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University of Waterloo, Ontario, Canada Eric Savory Department of Mechanical and Materials Engineering University of Western Ontario, Canada Miho Horie and Shiki Okamoto Shibaura Institute of Technology, Tokyo, Japan Presented at NATO ASI, May 200

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Page 1: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays

Robert Macdonald and Brian Kim Department of Mechanical EngineeringUniversity of Waterloo, Ontario, Canada

Eric Savory Department of Mechanical and Materials Engineering

University of Western Ontario, Canada

Miho Horie and Shiki OkamotoShibaura Institute of Technology, Tokyo, Japan

Presented at NATO ASI, May 2004

Page 2: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Content• Background • Description of the physical modelling facility

- Hydraulic flume- Atmospheric boundary layer simulation- Obstacle arrays

• Planar Laser Induced Fluorescence (PLIF) technique for concentration measurements

• Discussion of results for- Mean concentrations- RMS concentration fluctuations

• Conclusions

Page 3: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Air Pollutants• Local stack plumes• Exhausts from

automobiles• Accidental Releases

Page 4: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Street Canyon

• The canyon flow is affected by the arrangement and spacing between the buildings

• Geometry created by a narrow street with buildings lined up continuously along both sides (Nicholson, 1975)

Page 5: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Background

Concentration fluctuations can be important in assessing toxic risk.

Experimental data on concentration fluctuations in obstacle arrays is quite sparse.

It has been suggested that Crms is O(Cmean) but estimates vary greatly.

The present study seeks to quantify Crms / Cmean

for different obstacle arrays and downwind locations.

Page 6: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Objectives

• Obtain concentration profiles using PLIF technique in a water flume

• Determine the effects of obstacle configuration on mean dispersion parameters (max. concentration, plume height, etc.)

• Obtain relative concentration fluctuation intensity profiles

• Validation of PLIF application

Page 7: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Scale Modeling• Full-scale (field) and Small-scale Studies

• 10 m : 5 cm = 200 : 1

Page 8: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Experimental Facilities (I)

• Hydraulic Flume– Fully developed, turbulent approach flow

(10cm/sec)

- Flume dimensions: 12.6 m x 1.2 m x 0.8 m

2.4 m

Page 9: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Experimental Facilities (II)

• Light Source System

– Argon ion Laser (Maximum, 5W)

– Fixed frequency resonant optical scanner

– 1 mm Light sheet

Page 10: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

0

10

20

30

40

50

60

70

0 5 10

U (cm/ s)

Z (cm

)

X=0cmX=50cmX=100cmX=147cm

Approach Flow Characterization• Acoustic Doppler

Velocimeter (ADV: 20 Hz)

Reference Height (5cm)Zo = 0.20 mm (0.4 m FS)UH = 5.74 m/sU* / UH = 0.13 = 0.29 Suburban terrain

Page 11: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

0

2

4

6

8

10

12

14

0 0.2 0.4

σ i/UH

Z/H

σ u/uHσ v/uHσ w/uH

0

2

4

6

8

10

12

14

0 0.2 0.4σ i/UH

Z/H

σ u/uHσ v/uHσ w/uH

• Non-dimensional Turbulence Intensity

X = 0 cm X = 50 cm

Page 12: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

0

2

4

6

8

10

12

14

0 0.2 0.4σ i/UH

Z/H

σ u/uHσ v/uHσ w/uH

0

2

4

6

8

10

12

14

0 0.2 0.4σ i/UH

Z/H

σ u/uHσ v/uHσ w/uH

X = 100 cm X = 147 cm

• Longitudinal > Lateral > Vertical turbulence Intensity

Page 13: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Dispersion Parameter (I)

• Non-Dimensional Concentration (Kc)

Q

H UC K

2H N

C

Where CN = C/Cs

Q = Volume flow rate

UH = Velocity at H (5cm)

Cs = Source conc.

Can be directlycompared with

non-dimensional datafrom

field-scale experiments or

dispersion data fromother wind tunnel

facilities

Page 14: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

DispersionParameter (II)

• Net vertical plume variance

Vertical rise of the plumeis a combination of two factors;

The centre of the distribution Zc and the standard deviation of the distribution Z

Reference : Lecture Not (Air Pollution)

C (z)

Z/Hz/H

222

ZCZZ

Page 15: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Experimental Configuration (I)• Square and Staggered Building Array

Page 16: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Experimental Configuration (II)• Unobstructed plume

- Less dilution than in the obstacle arrays higher concentrations - A baseline case for comparison to the obstacle (building) array results

“Lego”roughness Nuts

Page 17: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• 2-Dimensional with different Area Density

Experimental Configuration (III)

xyT

Ff S LS W

WH

A

A

H2 / (2.5Hx2.5H)= 16%H2 / (1.5Hx1.5H)= 44%

1.5 H 0.5 H

AF = Frontal area AT = Total plan area

Page 18: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Previous work with these arrays

• ADV measurements of mean velocity and turbulence quantities, Carter (2000), Macdonald et al (2002).

• Correlation of turbulence quantities above obstacles with: u / U* = 2.10, v / U* = 1.65 and w

/ U* = 1.20.

• Peak TKE about 30% greater above staggered array when compared to square array.

• Value of about 50% larger for flat plate arrays compared to cube arrays.

uw

Page 19: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• 3 different heights (0.3 H / 0.5 H / 1.0 H)

Source Release System

Page 20: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Upstream source (spacings from f = 16%)

Source Types and Downstream-Scale

1 row 2 row 6 row4 row

2.25H4.75H

9.75H14.75H

Inside source

U

U

Page 21: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Summary of Experiments

Source Types Upstream Inside

Array Types

Square

Staggered

TwoDimensional

With NUTS and LEGO

16 %

16 %

16 %

44 %

33 % 44 %

Unobstructed

16 %(Source Height 0.3H,0.5H,1.0H)

16 %

16 %

Page 22: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Traditional Point Measurement Techniques

• Allow measurement of

- Transport processes- Spatial distribution of concentration and velocity

Page 23: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Optical Measurement using Planar Laser Induced Fluorescence (PLIF)

38 33 27 27

58 38 30 33 23 15

57 47 41 35 25 5

56 46 40 35 25 13

58 48 40 33 23 11

55 45 37 28 18 17

57

77 71 65 67 55

76 76 70 65 65 53

78 78 70 63 63 61

75 75 77 68 58 57

95 95 83

99 99 93 81

97 98 98 87

• Digital CCD camera- Whole field measurement

• Indirect measurement - Using dye (as tracer)

• Non-intrusive- Optical technique

Page 24: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

PLIF Components

• The basic PLIF system

1. Planar laser light source

2. Fluorescent tracer release system

3. Digital image acquisition and storage system

4. Digital image analysis software

1

2 3

4

Page 25: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Inside test section of water flume

PLIF Principle• Allows measurement of the spatial distribution

of tracer concentrations• The higher the

concentration (C) of dye, the greater the intensity of emitted light (E) for a given intensity of incident light (I):

= calibration const.(e.g. Crimaldi & Koseff, 2001)

CIE

Page 26: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Low-Pass Filter• In the experiments, only the fluorescent

colour (555 nm) of dye is visible to the CCD camera – the use of a filter removes background

argon-ion laser light (514nm)

Wavelength Characteristic

0

20

40

60

80

100

200 300 400 500 600 700 800

um

%T

Page 27: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Calibration

0.5 0.25 0.1 0.05 (ppm) KNOWN Concentration

• To obtain the actual concentration, a calibration box was used with known concentrations of dye to form a calibration curve

• PLIF technique requires a careful calibration to convert image intensity to concentration.

Page 28: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Data Analysis Procedure

Set-UpExperiment

Configuration

ImageRecording

In each ROI

CalibrationBox

Image Record

Experimental Work Image processing

ImageGrabbing

CalculatingConcentration

WithCalibration

CollectingConcentration

Profile Data

Data Analysis

MeanConcentration

UsingGaussian C.Fit

ExtractDispersionParameter

AnalysisOf

RelativeConcentration

Page 29: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Instantaneous Images : 1st ~ 20th ( 15 sec interval )

Image Gathering

Average Image

20

1N

N/20I

Page 30: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Fluctuating concentrations:

= -

FluctuatingConcentration

( C )

InstantaneousConcentration

( C )

MeanConcentration

( C )= -

Average ImageInstantaneous ImageTurbulence Image

Turbulent Image Manipulation

Page 31: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Turbulence Variance( )

• Turbulence RMS ( )

2)'(1

CN

N

1

Fluctuating concentration Image

2)'(C

Page 32: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Variance ( )

RMS ( ) Mean (C)

Concentration Data Analysis

6.5 6.5 10.9 15.1 11.1 15.1 5.3 5.3 9.3 8.4 9.4 14.7 5.9 5.9 9.4 15.1 11.1 11.1 1.4 1.4 7.4 10.9 10.9 11.7 4.4 4.4 9.3 9.1 9.1 11.1 5.8 5.8 8.4 9.4 9.4 11.7

78 78 83 87 87 88 78 78 80 83 83 85 77 77 81 85 85 85 76 76 80 85 85 83 78 78 80 83 83 81 75 75 77 78 78 77

Concentration

ImageIntensity

Page 33: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Number of Images for Analysis

Z/H = 1

• No significant influence of image sample size on the Average image for Cmean

Present Study

Page 34: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Appropriate sampling time = 5 minutes to ensure

reliable data for Concentration fluctuations Crms

Z/H = 1

Page 35: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

1st Canyon Profiles for different image samples

Gaussian Mean Concentration Profiles

Cmean / Cs

0 2x10-3 4x10-3 6x10-3 8x10-3 10x10-3 12x10-3

Z/H

0

1

2

3

4

5

50 images (5min)20 images (2min)10 images (1min)20 images (5min)

Page 36: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

RMS Concentration Profiles

Crms / Cs

0 1x10-3 2x10-3 3x10-3 4x10-3 5x10-3

Z/H

0

1

2

3

4

5

50 images (5min)20 images (2min)10 images (1min)20 images (5min)

Page 37: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Relative Concentration Profiles

Crms / Cmean

0 2 4 6 8

Z/H

0

1

2

3

4

5

50 images (5min)20 images (2min)10 images (1min)20 images (5min)

Page 38: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Results

• Mean Concentration Profiles• Non-Dimensional Concentration• Analysis of characteristics for the various area

densities and configurations• Concentration fluctuation profiles

Page 39: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

AveragingPartitioning

• Mean concentration profiles

- Each canyon was divided into 5 sections

Mean Concentration Image

Page 40: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Spatial Averaged Concentration

- Upstream Staggered 1st Canyon Example

0.0006 0.0006 0.0006 0.0006 0.00060.0005 0.0006 0.0006 0.0005 0.00060.0006 0.0005 0.0005 0.0005 0.00050.0005 0.0005 0.0005 0.0005 0.00050.0006 0.0006 0.0005 0.0005 0.00050.0005 0.0004 0.0005 0.0004 0.00040.0005 0.0005 0.0005 0.0005 0.00050.0005 0.0005 0.0005 0.0005 0.00050.0005 0.0005 0.0005 0.0005 0.00050.0005 0.0004 0.0005 0.0005 0.00040.0005 0.0005 0.0005 0.0005 0.00050.0005 0.0005 0.0006 0.0006 0.00070.0008 0.0009 0.0013 0.0007 0.00100.0021 0.0018 0.0023 0.0018 0.00200.0037 0.0037 0.0038 0.0031 0.00350.0080 0.0063 0.0051 0.0052 0.00500.0093 0.0081 0.0076 0.0071 0.00660.0099 0.0097 0.0101 0.0096 0.00740.0099 0.0107 0.0115 0.0105 0.00790.0108 0.0114 0.0100 0.0098 0.0089

Spartial Averaging

0

0.002

0.004

0.006

0.008

0.01

0.012

0 20 40 60 80 100 120

Inside Canyon(pixel)C

/Cs

Average_P 100_Point

100 pixels 20 pixels 20 pixels

0.0077(at Z/H=0.7)Average

Page 41: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Mean concentration profile fitted with Gaussian curve

0 0.05 0.1 0.15 0.2 0.25 0.3

0

1

2

3

4

1

2

4

6

G 1

G 2

G 4

G 6

2

2

2

2

2

2

2

)(exp

2

)(exp

2exp

2),(

z

c

z

c

yzyp

zzzzy

U

QzyC

C(z)

Z / H

(ppm)

Page 42: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Saturation, Attenuation, Non-linear regression , Distortion, Reflection, Images for analyzing

Control Factors

– Source concentration (24.5 ppm)

– Small aperture (narrow field of view)

– Weak dye ( C <= max ~ 0.5 ppm)

– Gaussian curve fitting parameters

– Maximum length of camera position (3.8 m)

– Painting all blocks black

– 5 minute sampling with 20 images is optimal

Summary of considerations

0.5 ppm

Page 43: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

I. Comparison of dispersion parameters (Kc, Zbar)with

Wind tunnel and Point measurement data

• Different configuration for two experiments

1. Wind T : Ground level release Point measurement at centre of canyon

2. Flume : 0.5 H release, at centre of canyon

Page 44: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Upstream Square Array f = 16%

Nondimensional Concentration (K)

X / H

0 2 4 6 8 10 12 14 16

Kmax(Z=0.5H)

0.01

0.1

1

10

S.Carter (2000)Present Study (2004)Macdonald (1997)

Point Measurement

Wind Tunnel Measurement

Page 45: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Upstream Square Array f = 16%

Mean Height of Plume (Zbar)

X / H

0 2 4 6 8 10 12 14 16

Zbar

(Zc2+z2)1/2

0.4

0.6

0.8

1.0

1.2

1.4 S.Carter (2000)Present Study (2004)Macdonald (1997)

Point Measurement

Wind Tunnel Measurement

Page 46: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

II. Analysis of dispersion parameters Kc, Zbar

With

MEAN CONCENTRATION

Page 47: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Upstream Source (f = 16%)

Mean Height of Plume (Zbar)

X / H

0 2 4 6 8 10 12 14 16

Zbar

(Zc2+z2)1/2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

UnobstructedSQUARESTAGGERRED2-Dimensional

Initial plume disperses most rapidly for 2D array and least for square array.

Similar trends for inside source.

Page 48: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Upstream Source (f = 16%)

Nondimensional Concentration (K)

X / H

0 2 4 6 8 10 12 14 16

Kmax(Z=0.5H)

0.1

1

10UnobstructedSQUARESTAGERRED2-Dimensional

Resulting concentrations lower for 2D canyon compared to others.

Page 49: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

III. Analysis of fluctuating concentrations

With

Relative CONCENTRATION (Crms/Cmean)

Page 50: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Crms / Cmean

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Z/H

0

1

2

3

4

5

1st Canyon2nd Canyon4st Canyon6st Canyon

• The peak of the relative concentration fluctuation intensity (Crms/Cmean) occurs in the mixing layer immediately above the obstacles

• The ratio

(Crms/Cmean) decreases rapidly below rooftop height.

Relative Concentration Profiles

UpstreamSquare 16 %

Page 51: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Effect of Array Types Crms/Cmean

1 st Canyon

Crms

/ Cmean

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Z/H

0

1

2

3

4

5

Square 16%Staggered 16%

Staggered array shows greater relative concentration fluctuations than the square array both inside and above the canyon.

Page 52: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Effect of Array Types Crms/Cmean

6 th Canyon

Crms

/ Cmean

0.2 0.4 0.6 0.8 1.0 1.2

Z/H

0

1

2

3

4

5

Square 16%Staggered 16%

These differences also seem to occur further downstream, except within the canyon.

Page 53: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

2-D Relative Concentration Crms/Cmean

1 st Canyon

Crms

/ Cmean

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

Z/H

0

1

2

3

4

5

2D 16%2D 44%Inside 2D 16%

Caton et al (2003)Single cavity, W/H = 1

0.44

W/H=1.5W/H=0.5

Page 54: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

• Within the lowest 0.8H of the canyon Crms / Cmean = 0.25 to 0.45 and does not decrease in the downwind direction for the canyons studied. Magnitudes are consistent with Caton et al (2003) and Pavageau and Schatzmann (1999) for 2-D canyons.

• Peaks up to Crms / Cmean = 1.7 occur in shear layer above 1st canyon, decreasing to 0.9 further downstream.

• Further analysis of relative concentration profiles is required (develop model to predict the shape).

Summary

Page 55: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Acknowledgments

Natural Sciences and Engineering Research Council, NSERC (Canada)

Zhiyong Duan

Dr Dubravka Pokrajac for lending me her notebook PC …… I hope it still works !!

Page 56: Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University

Presented in fond memory of Robert Macdonald

1961 - 2004