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Turbulent superstructure statistics in a turbulent boundary layer with pressure gradients

Bross, Matt and Schanz, Daniel and Novara, Matteo and Eich, Felix and Schröder, Andreas and Kähler, Christian (2023) Turbulent superstructure statistics in a turbulent boundary layer with pressure gradients. European Journal of Mechanics B-Fluids, 101 (09/10), 209 - 218. Elsevier. doi: 10.1016/j.euromechflu.2023.05.010. ISSN 0997-7546.

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Official URL: https://doi.org/10.1016/j.euromechflu.2023.05.010


In turbulent boundary layers, streamwise elongated regions of high- and low-momentum in the log-law layer that can extent up to several boundary layer thicknesses are often referred to as turbulent superstructures. These structures contain a relatively large portion of the layer’s turbulent kinetic energy and have been shown to interact with the near-wall flow structures. In the last few decades extensive research on zero-pressure gradient (ZPG) turbulent boundary layers has been done, however by comparison, the structural characteristics for adverse pressure gradient turbulent (APG) boundary layer flows are much less studied despite their strong significance aero-hydrodynamic vehicle design. Therefore, the three-dimensional dynamics of turbulent superstructures in a turbulent boundary layer flow are investigated in the Atmospheric Wind Tunnel Munich (AWM) using a multi-camera 3D time-resolved Lagrangian particle tracking approach. In this study, Lagrangian and Eulerian statistics will be used to characterize the dynamics and interaction of turbulent superstructures within a zero pressure gradient (ZPG) turbulent boundary layer at Re = 5000 or Re = 14 000 that then flows over a curved plate subjected to a favorable (FPG) and strong adverse (APG) pressure gradient, which eventually separates. An Eulerian analysis, using multi-point correlations of 3D velocity fields, found that the average superstructure topology is modulated by decelerating flow in the APG region when compared to the ZPG region, however the basic shape and spanwise pattern is preserved. Looking into the behavior of individual trajectories, it was found that the dispersion of single particles along trajectories in the log-law layer are capable of moving more than the average Eulerian superstructure spacing in the spanwise direction. Furthermore, the mean square of the single particle dispersion indicates that the maximum dispersion in the spanwise direction comes from particles released at the wall-normal location corresponding to the so-called “second-peak/plateau” region in the streamwise normal Reynolds stress.

Item URL in elib:https://elib.dlr.de/196255/
Document Type:Article
Additional Information:Online ISSN: 1873-7390; https://www.sciencedirect.com/journal/european-journal-of-mechanics-b-fluids; Turbulent superstructure statistics in a turbulent boundary layer with pressure gradients - ScienceDirect; online; Available online 20 June 2023
Title:Turbulent superstructure statistics in a turbulent boundary layer with pressure gradients
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Schanz, DanielUNSPECIFIEDhttps://orcid.org/0000-0003-1400-4224UNSPECIFIED
Novara, MatteoUNSPECIFIEDhttps://orcid.org/0000-0002-8975-0419UNSPECIFIED
Schröder, AndreasUNSPECIFIEDhttps://orcid.org/0000-0002-6971-9262140150914
Date:20 June 2023
Journal or Publication Title:European Journal of Mechanics B-Fluids
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:209 - 218
EditorsEmailEditor's ORCID iDORCID Put Code
Dias, FredericUni College DublinUNSPECIFIEDUNSPECIFIED
Series Name:ScienceDirect
Keywords:turbulent boundary layer, superstructures, Lagrangian particle tracking
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Efficient Vehicle
DLR - Research area:Aeronautics
DLR - Program:L EV - Efficient Vehicle
DLR - Research theme (Project):L - Virtual Aircraft and  Validation
Location: Göttingen
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > Experimental Methods, GO
Deposited By: Micknaus, Ilka
Deposited On:09 Aug 2023 14:27
Last Modified:09 Aug 2023 14:27

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