MEASURING AND MODELLING THE DISPERSION OF AEROSOLS FROM POINT SOURCES - PowerPoint PPT Presentation

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MEASURING AND MODELLING THE DISPERSION OF AEROSOLS FROM POINT SOURCES

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  1. Royal Military College of Canada MEASURING AND MODELLING THE DISPERSION OF AEROSOLS FROM POINT SOURCES William S. Andrews1, Xiaoying Cao2 Gilles Roy3 and Luc Forand3 1. Chemistry and Chemical Engineering, RMC 2. Chemical Engineering, Queen’s University 3. DRDC-RDDC Valcartier Department of Chemistry and Chemical Engineering

  2. OUTLINE • Introduction • Experiment setup and lidar inversion • Cloud selection and Data processing • Measuremental data analysis • ANN Modelling • Model results Department of Chemistry and Chemical Engineering, RMC

  3. INTRODUCTION • Plumes better characterized than puffs • Basic puff model is Gaussian (usually use Slade’s dispersion coefficients - 1968) • Weak empirical basis • Poor predictions, especially close to source • Experimental program conducted 2002-2003 in Valcartier Quebec • This paper will report on initial attempts to analyze (model) data collected Department of Chemistry and Chemical Engineering, RMC

  4. PUFF MEASUREMENT • release 10 or 20 g of aerosol from generator (150 - 200 m from lidar) • scan cloud (raster pattern) during evolution using scanning lidar @ 532 nm • sampling frequency 125 MHz (every 1.2 m) • beam “width” (elliptical) 2 mrad • 1 shot per 0.8 º azimuth (up to 82 º FOV) • 0.5 º or 1º steps in elevation Department of Chemistry and Chemical Engineering, RMC

  5. FIELD DATA COLLECTION • 4 periods • 5-8 Nov 02, • 11-14 Mar 03, • 27-30 May 03, • 12-15 Aug 03 • 2 x 24 h cycles per period • 10 releases per material every 3 h (30) • Three separate aerosols • hollow microspheres (~ 27 μm, 0.21 g/cc) • solid glass spheres (~ 6.5 μm, 2.7g/cc) • talc (~ 22 μm) Department of Chemistry and Chemical Engineering, RMC

  6. MICROSPHERES Department of Chemistry and Chemical Engineering, RMC

  7. GLASS SPHERES Department of Chemistry and Chemical Engineering, RMC

  8. TALC Department of Chemistry and Chemical Engineering, RMC

  9. LIDAR INVERSION (Bissonnette and Roy, 2003). Department of Chemistry and Chemical Engineering, RMC

  10. 2 1 3 6 5 4 7 8 9 Department of Chemistry and Chemical Engineering, RMC

  11. Department of Chemistry and Chemical Engineering, RMC

  12. Statistics of useful clouds Department of Chemistry and Chemical Engineering, RMC

  13. Range of meteorological conditions Department of Chemistry and Chemical Engineering, RMC

  14. Data Processing • Lidar Inversion • Mass Center of cloud • Transport wind speed and direction • Coordinate system rotation • Center of Mass modification Department of Chemistry and Chemical Engineering, RMC

  15. (a) (b) (c) (d) Sample ms cloud 0730 13 August 2003 Department of Chemistry and Chemical Engineering, RMC

  16. Sample talc cloud 2230 14 August 2003 Department of Chemistry and Chemical Engineering, RMC

  17. Sample sg cloud– 0500 15 August 2003 (a) (b) Department of Chemistry and Chemical Engineering, RMC

  18. Concentration Distributions Department of Chemistry and Chemical Engineering, RMC

  19. Comparison of models Department of Chemistry and Chemical Engineering, RMC

  20. ANN Model Prediction Department of Chemistry and Chemical Engineering, RMC

  21. Conclusions • Concentration is insensitive to particle shape, size, air pressure • Insolation, T, time after release are very important • Important turbulence parameters: CN2, CTQ, CVPT2, U_var, W_var, UV_cov, VTs_cov, WTs_cov • ANNs predict concentration distribution to be Gaussian in shape • ANN prediction closer to observations than standard Gaussian puff (Slade parameters) Department of Chemistry and Chemical Engineering, RMC

  22. Acknowledgements • Dr. Andrews and Dr. Roy (supervisors) • Luc Forand, Guy Potvin and Luc Bissonnette • Personnel at DRDC Valcartier • NSERC, DGNS, ARP Department of Chemistry and Chemical Engineering, RMC