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The atmospheric boundary layer (ABL) is a highly turbulent geophysical flow, which has chaotic and often too complex dynamics to unravel from limited data. Characterizing coherent turbulence structures in complex ABL flows under various atmospheric regimes is not systematically well established yet. This study aims to bridge this gap using large eddy simulations (LESs), Koopman theory, and unsupervised classification techniques. To this end, eight LESs of different convective, neutral, and unsteady ABLs are conducted. As the ratio of buoyancy to shear production increases, the turbulence structures change from roll vortices to convective cells. The quadrant analysis indicated that as this ratio increases, the sweep and ejection events decrease, and inward/outward interactions increase. The Koopman mode decomposition (KMD) is then used to characterize their turbulence structures. Our results showed that KMD can reveal non-trivial modes of highly turbulent ABL flows (e.g., transverse to the mean flow direction) and can reconstruct the primary dynamics of ABLs even under unsteady conditions with only ∼5% of the modes. We attributed the detected modes to the imposed pressure gradient (shear), Coriolis (inertial oscillations), and buoyancy (convection) forces by conducting novel timescale and quadrant analyses. We then applied the convolutional neural network combined with the K-means clustering to group the Koopman modes. This approach is displacement and rotation invariant, which allows efficiently reducing the number of modes that describe the overall ABL dynamics. Our results provide new insights into the dynamics of ABLs and present a systematic data-driven method to characterize their complex spatiotemporal patterns.
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Classification of spatial-temporal flow patterns in a low Re wake based on the recurrent trajectory clustering
Coherent structures are ubiquitous in unsteady flows. They can be regarded as certain kinds of spatial-temporal patterns that interact with the neighboring field. Although they play a key role in convection and mixing, there is no consensus on how to define them, and their dynamics are complicated. In the past decades, many methods are developed to identify coherent structures based on instantaneous velocity fields (e.g., vortex identification) or long-time statistics (e.g., proper orthogonal decomposition), but the evolution process of individual structures is not well considered in the identification. In this paper, we propose a new method to classify coherent motions according to their evolution dynamics. Specifically, the evolutions are represented by trajectories in the phase space. We define a distance between two trajectories and use it to construct a network that characterizes all evolution patterns. Using spectrum clustering, we categorize these patterns into various groups. This method is applied to a low Reynolds number wake flow downstream of two cylinders-in-tandem, where one of the cylinders oscillates in the transverse direction. The flow is quasi-periodic, and four types of recurrent spatial-temporal patterns can be identified. It is a useful tool to investigate low Reynolds number unsteady flows.
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- Award ID(s):
- 1944187
- PAR ID:
- 10393940
- Date Published:
- Journal Name:
- Physics of Fluids
- Volume:
- 34
- Issue:
- 11
- ISSN:
- 1070-6631
- Page Range / eLocation ID:
- 113607
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0123627
