Vortical structures are regarded as the dominant organized patterns in wall
turbulence. They play a key role in physical phenomena of practical importance such as
energy and momentum transport, combustion, mixing, and noise and drag production.
Considerable investigations have been performed in drag and noise phenomena studies,
with a main purpose of controlling and reducing them. Various techniques to control the
drag reduction have been studied for over last five decades; however, the detailed
understanding of the drag reduction mechanism is still lacking. Vortices play an
important role in turbulence structure. Nevertheless, the identification of vortices is still
unclear, not even a universal definition of a vortex is accepted.
In the present study, several vortex feature extraction schemes are implemented.
The methods are applied to analyze instantaneous two-dimensional velocity fields
obtained by particle tracking Velocimetry (PTV) measurements of a turbulent channel
flow with and without microbubble injection within the boundary layer. Microbubble
injection is one of the drag reduction techniques, first studied in early 1970s, that has
undergone extensive research in past years, and the generated information has aided into drag reduction understanding.
As a general rule, vortex extraction methods can be either a simple visualization
scheme or more sophisticated identification tools. The Reynolds decomposition and its
variants are suitable due to their capacity to mark vortices advecting at different
velocities. In the case of identification techniques, which yield a scalar field calculated
from either the velocity vector field or the velocity gradient tensor, both the modified
swirling strength Λci or the λ2 criteria were found to be well suited for vortex
identification.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/3757 |
| Date | 16 August 2006 |
| Creators | Maroni Veiga, Adrian Gaston |
| Contributors | Annamalai, Kalyan, Hassan, Yassin A. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 2581660 bytes, electronic, application/pdf, born digital |
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