An official website of the United States government
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.
more »
« less
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
- PAR ID:
- 10466629
- 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
More Like this
-
-
Baroclinicity adds another layer of complexity to the much-studied barotropic atmospheric boundary layers (ABLs) by modulating the pressure gradient in height. Despite the prevalence of baroclinicity in real-world ABLs, our knowledge of the interacting effects of baroclinicity and atmospheric stability is limited. In this talk, we aim to address this knowledge gap by systematically varying baroclinicity and stability using the large-eddy simulation (LES) technique. We will present how baroclinicity alters the friction velocity, Obukhov length, shear production, ABL height, and low-level jet (LLJ) in diabatic baroclinic atmospheric flows. It will be shown that while baroclinicity significantly impacts unstable, neutral, and weakly stable ABLs, its effects reduce with increased stratification in the ABL. In strongly stratified ABLs, the LLJ height, friction velocity, and Obukhov length converge to a constant asymptote independent of the baroclinicity regime. We will demonstrate that this behavior is attributed to the strong turbulence destruction in very stable ABLs that decouples the surface from higher elevations where baroclinicity is more important. Finally, two rescaling methods in the inner and outer layers of stable baroclinic ABLs will be presented to non-dimensionalize and collapse the wind profiles in baroclinic environments. The developed reduced model for different baroclinic wind profiles will be shown against the LES results. The findings of this research elucidate the underlying physics of baroclinic diabatic ABLs and are useful for characterizing the wind profiles in weather/climate models, field measurements, and various industrial applications.more » « less
-
Large scale coherent structures in the atmospheric boundary layer (ABL) are known to contribute to the power generation in wind farms. In order to understand the dynamics of large scale structures, we perform proper orthogonal decomposition (POD) analysis of a finite sized wind turbine array canopy in the current paper. The POD analysis sheds light on the dynamics of large scale coherent modes as well as on the scaling of the eigenspectra in the heterogeneous wind farm. We also propose adapting a novel Fourier-POD (FPOD) modal decomposition which performs POD analysis of spanwise Fourier-transformed velocity. The FPOD methodology helps us in decoupling the length scales in the spanwise and streamwise direction when studying the 3D energetic coherent modes. Additionally, the FPOD eigenspectra also provide deeper insights for understanding the scaling trends of the three-dimensional POD eigenspectra and its convergence, which is inherently tied to turbulent dynamics. Understanding the behaviour of large scale structures in wind farm flows would not only help better assess reduced order models (ROM) for forecasting the flow and power generation but would also play a vital role in improving the decision making abilities in wind farm optimization algorithms in future. Additionally, this study also provides guidance for better understanding of the POD analysis in the turbulence and wind farm community.more » « less
-
Rotating and swirling turbulence comprises an important class of flows, not only due to the complex physics that occur, but also due to their relevance to many engineering applications, such as combustion, cyclone separation, mixing, etc. In these types of flows, rotation strongly affects the characteristics and structure of turbulence. However, the underlying turbulent flow phenomena are complex and currently not well understood. The axially rotating pipe is an exemplary prototypical model problem that exhibits these complex turbulent flow physics. By examining the complex interaction of turbulent structures within rotating turbulent pipe flow, insight can be gained into the behavior of rotating flows relevant to engineering applications. Direct numerical simulations are conducted at a bulk Reynolds number up to Re_D = 19,000 with rotation numbers ranging from N = 0 to 3. Coherence analysis, including Proper Orthogonal Decomposition and Dynamic Mode Decomposition, are used to identify the relevant (highest energy) modes of the flow. Studying the influence of these modes on turbulent statistics (i.e. mean statistics, Reynolds stresses, turbulent kinetic energy, and turbulent kinetic energy budgets) allow for a deeper understanding of the effects of coherent turbulent flow structures in rotating flows.more » « less
-
Abstract The marine atmospheric boundary layer (ABL) and oceanic boundary layer (OBL) are a two-way coupled system. At the ocean surface, the ABL and OBL share surface fluxes of momentum and buoyancy that incorporate variations in sea surface temperature (SST) and currents. To investigate the interactions, a coupled ABL–OBL large-eddy simulation (LES) code is developed and exercised over a range of atmospheric stability. At each time step, the coupling algorithm passes oceanic currents and SST to the atmospheric LES, which in turn computes surface momentum, temperature, and humidity fluxes driving the oceanic LES. Equations for each medium are time advanced using the same time step but utilize different grid resolutions: the horizontal grid resolution in the ocean is approximately four times finer, e.g., (Δxo, Δxa) = (1.22, 4.88) m. Interpolation and anterpolation (its adjoint) routines connect the atmosphere and ocean surface layers. In the simplest setup of a statistically horizontally homogeneous flow, the largest scale ABL turbulent shear-convective rolls leave an imprint on the OBL currents in the upper layers. This result is shown by comparing simulations that use coupling rules that are applied either instantaneously at everyx–ygrid point or averaged across anx–yplane. The spanwise scale of the ABL turbulence is ∼1000 m, while the depth of the OBL is ∼20 m. In these homogeneous, fully coupled cases, the large-scale spatially intermittent turbulent structures in the ABL modulate SST, currents, and the connecting momentum and buoyancy fluxes, but the mean profiles in each medium are only slightly different.more » « less
