Acceleration feature points of unsteady shear flows Journal Article

Author(s): Kasten, Jens; Reininghaus, Jan; Hotz, Ingrid; Hege, Hans C; Noack, Bernd R; Daviller, Guillaume; Morzyński, Marek
Article Title: Acceleration feature points of unsteady shear flows
Affiliation IST Austria
Abstract: A framework fo r extracting features in 2D transient flows, based on the acceleration field to ensure Galilean invariance is proposed in this paper. The minima of the acceleration magnitude (a superset of acceleration zeros) are extracted and discriminated into vortices and saddle points, based on the spectral properties of the velocity Jacobian. The extraction of topological features is performed with purely combinatorial algorithms from discrete computational topology. The feature points are prioritized with persistence, as a physically meaningful importance measure. These feature points are tracked in time with a robust algorithm for tracking features. Thus, a space-time hierarchy of the minima is built and vortex merging events are detected. We apply the acceleration feature extraction strategy to three two-dimensional shear flows: (1) an incompressible periodic cylinder wake, (2) an incompressible planar mixing layer and (3) a weakly compressible planar jet. The vortex-like acceleration feature points are shown to be well aligned with acceleration zeros, maxima of the vorticity magnitude, minima of the pressure field and minima of λ2.
Journal Title: Archives of Mechanics
Volume: 68
Issue 1
ISSN: 0373-2029
Publisher: Polish Academy of Sciences Publishing House  
Date Published: 2016-01-01
Start Page: 55
End Page: 80
Notes: The authors acknowledge funding of the German Re- search Foundation (DFG) via the Collaborative Re- search Center (SFB 557) \Control of Complex Turbu- lent Shear Flows" and the Emmy Noether Program. Further funding was provided by the Zuse Institute Berlin (ZIB), the DFG-CNRS research group \Noise Generation in Turbulent Flows" (2003{2010), the Chaire d'Excellence 'Closed-loop control of turbulent shear ows using reduced-order models' (TUCOROM) of the French Agence Nationale de la Recherche (ANR), and the Eu- ropean Social Fund (ESF App. No. 100098251). We thank the Ambrosys Ltd. Society for Complex Sys- tems Management and the Bernd R. Noack Cybernet- ics Foundation for additional support. A part of this work was performed using HPC resources from GENCI-[CCRT/CINES/IDRIS] supported by the Grant 2011- [x2011020912
Open access: yes (repository)