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

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Presentation on theme: "Physical Modelling of Concentration Fluctuations in Simple Obstacle Arrays Robert Macdonald and Brian Kim Department of Mechanical Engineering University."— Presentation transcript:

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2 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 2004

3 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

4 Air Pollutants Local stack plumes Exhausts from automobiles Accidental Releases

5 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)

6 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 C rms is O(C mean ) but estimates vary greatly. The present study seeks to quantify C rms / C mean for different obstacle arrays and downwind locations.

7 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

8 Scale Modeling Full-scale (field) and Small-scale Studies 10 m : 5 cm = 200 : 1

9 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

10 Experimental Facilities (II) Light Source System –Argon ion Laser (Maximum, 5W) –Fixed frequency resonant optical scanner –1 mm Light sheet

11 Approach Flow Characterization Acoustic Doppler Velocimeter (ADV: 20 Hz) Reference Height (5cm) Z o = 0.20 mm (0.4 m FS) U H = 5.74 m/s U * / U H = 0.13 = 0.29 Suburban terrain

12 Non-dimensional Turbulence Intensity X = 0 cm X = 50 cm

13 X = 100 cm X = 147 cm Longitudinal > Lateral > Vertical turbulence Intensity

14 Dispersion Parameter (I) Non-Dimensional Concentration (Kc) Where C N = C/Cs Q = Volume flow rate U H = Velocity at H (5cm) Cs = Source conc. Can be directly compared with non-dimensional data from field-scale experiments or dispersion data from other wind tunnel facilities

15 Dispersion Parameter (II) Net vertical plume variance Vertical rise of the plume is 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/H z /H

16 Experimental Configuration (I) Square and Staggered Building Array

17 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

18 2-Dimensional with different Area Density Experimental Configuration (III) H 2 / (2.5Hx2.5H) = 16% H 2 / (1.5Hx1.5H) = 44% 1.5 H 0.5 H A F = Frontal area A T = Total plan area

19 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.

20 3 different heights (0.3 H / 0.5 H / 1.0 H) Source Release System

21 Upstream source (spacings from f = 16%) Source Types and Downstream-Scale 1 row2 row6 row4 row 2.25H 4.75H 9.75H 14.75H Inside source U U

22 Summary of Experiments Source TypesUpstreamInside Array Types Square Staggered Two Dimensional With NUTS and LEGO 16 % 44 % 33 %44 % Unobstructed 16 % (Source Height 0.3H,0.5H,1.0H) 16 %

23 Traditional Point Measurement Techniques Allow measurement of - Transport processes - Spatial distribution of concentration and velocity

24 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

25 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

26 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)

27 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)

28 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.

29 Data Analysis Procedure Set-Up Experiment Configuration Image Recording In each ROI Calibration Box Image Record Experimental WorkImage processing Image Grabbing Calculating Concentration With Calibration Collecting Concentration Profile Data Data Analysis Mean Concentration Using Gaussian C.Fit Extract Dispersion Parameter Analysis Of Relative Concentration

30 Instantaneous Images : 1 st ~ 20 th ( 15 sec interval ) Image Gathering Average Image

31 Fluctuating concentrations: = - Fluctuating Concentration ( C ) Instantaneous Concentration ( C ) Mean Concentration ( C ) = - Average ImageInstantaneous ImageTurbulence Image Turbulent Image Manipulation

32 Turbulence Variance ( ) Turbulence RMS ( ) Fluctuating concentration Image

33 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 Image Intensity

34 Number of Images for Analysis Z/H = 1 No significant influence of image sample size on the Average image for C mean Present Study

35 Appropriate sampling time = 5 minutes to ensure reliable data for Concentration fluctuations C rms Z/H = 1

36 1 st Canyon Profiles for different image samples

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39 Results Mean Concentration Profiles Non-Dimensional Concentration Analysis of characteristics for the various area densities and configurations Concentration fluctuation profiles

40 Averaging Partitioning Mean concentration profiles - Each canyon was divided into 5 sections Mean Concentration Image

41 Spatial Averaged Concentration - Upstream Staggered 1 st Canyon Example 100 pixels 20 pixels 0.0077(at Z/H=0.7) Average

42 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 C(z) Z / H (ppm)

43 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

44 I. Comparison of dispersion parameters (Kc, Z bar ) 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

45 Upstream Square Array f = 16% Point Measurement Wind Tunnel Measurement

46 Upstream Square Array f = 16% Point Measurement Wind Tunnel Measurement

47 II. Analysis of dispersion parameters Kc, Z bar With MEAN CONCENTRATION

48 Upstream Source ( f = 16%) Initial plume disperses most rapidly for 2D array and least for square array. Similar trends for inside source.

49 Upstream Source ( f = 16%) Resulting concentrations lower for 2D canyon compared to others.

50 III. Analysis of fluctuating concentrations With Relative CONCENTRATION (C rms /C mean )

51 The peak of the relative concentration fluctuation intensity (C rms /C mean ) occurs in the mixing layer immediately above the obstacles The ratio (C rms /C mean ) decreases rapidly below rooftop height. Relative Concentration Profiles Upstream Square 16 %

52 Effect of Array Types C rms /C mean Staggered array shows greater relative concentration fluctuations than the square array both inside and above the canyon.

53 Effect of Array Types C rms /C mean These differences also seem to occur further downstream, except within the canyon.

54 2-D Relative Concentration C rms /C mean Caton et al (2003) Single cavity, W/H = 1 0.44 W/H=1.5 W/H=0.5

55 Within the lowest 0.8H of the canyon C rms / C mean = 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 C rms / C mean = 1.7 occur in shear layer above 1 st canyon, decreasing to 0.9 further downstream. Further analysis of relative concentration profiles is required (develop model to predict the shape). Summary

56 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 !!

57 Presented in fond memory of Robert Macdonald 1961 - 2004


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