Identifying Locally Turbulent Vortices within Instabilities
This work addresses a domain-specific problem in fluid dynamics for researchers analyzing turbulent flows, but it appears incremental as it builds on existing methods with new indicators.
The paper tackles the problem of automatically detecting locally turbulent vortices in 2D turbulent flows like instabilities by using Topological Data Analysis to extract vortex geometry and introducing indicators based on kinetic energy power spectra to estimate turbulence correlation. Preliminary experiments show these indicators can distinguish turbulent from laminar vortices.
This work presents an approach for the automatic detection of locally turbulent vortices within turbulent 2D flows such as instabilites. First, given a time step of the flow, methods from Topological Data Analysis (TDA) are leveraged to extract the geometry of the vortices. Specifically, the enstrophy of the flow is simplified by topological persistence, and the vortices are extracted by collecting the basins of the simplified enstrophy's Morse complex. Next, the local kinetic energy power spectrum is computed for each vortex. We introduce a set of indicators based on the kinetic energy power spectrum to estimate the correlation between the vortex's behavior and that of an idealized turbulent vortex. Our preliminary experiments show the relevance of these indicators for distinguishing vortices which are turbulent from those which have not yet reached a turbulent state and thus known as laminar.