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Title: Data-Driven Characterization of Coherent Turbulence Structures in Atmospheric Boundary Layers
Turbulence is a major source of momentum, heat, moisture, and aerosol transport in the atmosphere. Hence, it is crucial to understand and accurately characterize turbulence mechanisms in atmospheric flows. Many complex factors in the atmosphere influence the turbulence structures including stratification and background shear. However, our understanding of the interacting effects of these factors on coherent turbulence structure evolutions is still limited. In this talk, we aim to bridge this knowledge gap by using mode decomposition techniques and a wide range of large-eddy simulation (LES) data. By developing a data-driven technique, we will characterize unique features of atmospheric boundary layer (ABL) turbulence under different forcing scenarios. We will present 3D LES wind speed snapshots of different ABL flows that will be used as dynamic mode decomposition (DMD) input data. Then, the obtained modes and eigenvalues will be employed to gain insights into coherent turbulence structures in ABLs. We will explain the physical meaning of dominant modes and how each mode relates to the physical cause of turbulence structures. The dominant modes, which are selected based on the mode amplitude, contain the most important spatial and temporal characteristics of the flow. We will evaluate the accuracy of the performance of this method by reconstructing the flow field with only a small number of modes, and then calculate the mean average error between the real flow and the reconstructed flow fields. We will present different data frequencies, wind speeds, and surface heat fluxes. This allows us to elucidate the modes and determine the conditions in which the mode decomposition provides more accurate results for the ABL flows. Our findings can be used to identify the major causes of turbulence in real atmospheric flows and could provide a deeper insight into the dynamics of turbulence in ABLs. Our results will also be useful for developing reduced-order models that can rapidly predict the turbulent ABL flow fields.  more » « less
Award ID(s):
2228299
NSF-PAR ID:
10466629
Author(s) / Creator(s):
;
Publisher / Repository:
https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/416487
Date Published:
Format(s):
Medium: X
Location:
Denver
Sponsoring Org:
National Science Foundation
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