Characterizing the lower log region of the atmospheric surface layer via large-scale particle tracking velocimetry
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As a first step toward characterizing coherent structures within the atmospheric surface layer (ASL), measurements obtained via a large-scale particle tracking velocimetry (LS-PTV) system were validated against wind-measurement station data as well as canonical turbulent boundary layer studies. The LS-PTV system resolves three-dimensional, Lagrangian tracks over a 16 m3 volume. Mean-velocity measurements, as well as vertical and shear Reynolds-stress measurements, generally agreed with wind-measurement station data and Reynolds-stress profiles referenced from literature. The probability distributions for streamwise, spanwise and vertical velocity-fluctuation components appear normally distributed about zero. Furthermore, the probability distributions for all three components of Lagrangian acceleration were exponential and followed the parametrization curve from LaPorta et al. (Lett Nat 409:1017–1019, 2001). Lastly, the vorticity probability distributions were exponential and symmetric about zero, which matches findings from Balint et al. (Fluid Mech 228:53–86, 1991). The vorticity intensity measured by the LS-PTV system was less than values from Priyadarshana et al. (Fluid Mech 570:307–346, 2007), which is attributed to the low spatial resolution. However, the average spacing of 0.5 m between tracer particles is deemed sufficient for the future characterization of vortical structures within the ASL.
The authors wish to thank Alberta Innovates Technology Futures for their financial backing. Thanks also go to Genivar Wind for their generous support in the development and operation of the wind mast. The authors also wish to thank Tyler Christensen and Mohamed Arif Mohamed for their assistance during data acquisition, and Dr. Masaki Hayashi for providing access to his eddy-covariance system.
- Balint J-L, Wallace JM, Vukoslavcevic P (1991) The velocity and vorticity vector fields of a turbulent boundary layer. part 2. statistical properties. Fluid Mech 228:53–86Google Scholar
- Kaimal JC, Finnigan JJ (1994) Atmospheric boundary layer flows-their structure and measurement. Oxford University Press, New York, NYGoogle Scholar
- Machacek M (2003) A quantitative visualization tool for large wind tunnel experiments. Ph.D. thesis, Swiss Federal Institute of TechnologyGoogle Scholar
- Marusic I, McKeon BJ, Monkewitz PA, Nagib HM, Smits AJ, Sreenivasan KR (2010) Wall-bounded turbulent flows at high reynolds numbers: recent advances and key issues. Phys Fluids 25:431–442Google Scholar
- Mockett CR (1998) Dispersion and reconstruction. Astrophyical and geophysical flows as dynamical systems. WHOI Tech Report 166:166–188Google Scholar
- Pasquill F (1961) The estimation of dispersion of windborne material. Meteorol Mag 90:33–49Google Scholar
- Raffel M, Willert CE, Wereley ST, Kompenhans J (2007) Particle image velocimetry: a practical guide, 2nd edn. Springer, New YorkGoogle Scholar
- Sugiyama G, Nasstrom JS (1999) Methods for determining the height of the atmospheric boundary layer. Lawrence Livermore National Laboratory, LivermoreGoogle Scholar
- Sun Y, Zhang Y (2003) Development of a stereoscopic particle image velocimetry system for full-scale room airflow studies: part ii: Experimental setup. Am Soc Heat Refridgeration Air-Conditioning Eng 09:8–540Google Scholar