Stochastic modeling of rough wall turbulent boundary layers: a wind tunnel and atmospheric scale study
ORAL
Abstract
The statistical properties of uniform momentum zones (UMZ) in rough wall turbulent boundary layers are investigated at the laboratory and field scales to identify potential similarities in their probability distribution functions in the logarithmic region. In particular, we focus on the thickness Hm and the modal velocity Um , as a function of the wall distance. These are critical to reproduce the attached eddy scaling laws and the mean velocity profile, while providing a significant contribution to the observed variability of the streamwise velocity component. We will show that building and inverting the Hm, Um cumulative density functions, allow us to generate synthetic velocity profiles, from the ground up, that mimic the vertical distribution of UMZs while retaining some of the statistical properties of turbulent boundary layers, such as the mean velocity and the variance profiles. Spatio-temporally resolved PIV velocity fields, acquired as part of the Grand-scale Atmospheric Imaging Apparatus (GAIA) collaboration and covering a field of view of 8 x 9 m 2 in the atmospheric surface layer, allow us to derive and test our semi-stochastic algorithm at high Reynolds numbers. Preliminary results show the generalizability of our framework and the potential to develop a computationally affordable, low dimensional, dynamic near wall model.
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Presenters
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Roozbeh Ehsani
University of Minnesota, University of Minnesota, Twin Cities
Authors
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Roozbeh Ehsani
University of Minnesota, University of Minnesota, Twin Cities
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Michael Heisel
University of California in Los Angeles, University of California at Los Angeles
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Nathaniel Bristow
University of Minnesota
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Peter W Hartford
University of Minnesota, University of Minnesota, Twin Cities
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Jiaqi Li
University of Minnesota
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Jiarong Hong
University of Minnesota
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Vaughan Voller
University of Minnesota
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Michele Guala
University of Minnesota