The characteristics of the meandering effect in a stratified wake
ORAL
Abstract
In a stratified wake, the near-wake flow is similar to its non-stratified equivalent, and the buoyancy effect grows stronger as the flow develops downstream. The flow starts with initial vortex shedding. Then, the vertical motions get suppressed and large horizontal structures are observed in the late wake. The evolving flow structures bring in meandering behavior in the wake regime. We aim at understanding how the meandering impacts the flow behavior, especially the scaling of the deficit velocity.
In this work, we study the meandering effect by the decomposition of the stationary behavior and the pure meandering behavior. A large direct numerical simulation (DNS) database is set up for a wide range of Reynolds numbers and Froude numbers. The meandering effect can be characterized by extracting the center location from a large number of instantaneous snapshots. We find similar horizontal expansion in the meandering of the center location as in the size expansion of flow structures. We established self-similarity theory in the stratified wake and obtain the conservation law. We find that the meandering has little effect on changing the scaling of the mean velocity, but it is one of the main reasons for possible deviation from the Gaussian assumptions.
In this work, we study the meandering effect by the decomposition of the stationary behavior and the pure meandering behavior. A large direct numerical simulation (DNS) database is set up for a wide range of Reynolds numbers and Froude numbers. The meandering effect can be characterized by extracting the center location from a large number of instantaneous snapshots. We find similar horizontal expansion in the meandering of the center location as in the size expansion of flow structures. We established self-similarity theory in the stratified wake and obtain the conservation law. We find that the meandering has little effect on changing the scaling of the mean velocity, but it is one of the main reasons for possible deviation from the Gaussian assumptions.
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Presenters
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Xinyi Huang
The California Institute of Technology
Authors
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Xinyi Huang
The California Institute of Technology
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Jiaqi Li
The Pennsylvania State University
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Xiang Yang
Pennsylvania State University, The Penn State Department of Mechanical Engineering, Penn State Department of Mechanical Engineering