A new statistical tool to study the geometry of intense vorticity clusters in turbulence

Alberto Vela-Martin, Takashi Ishihara

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


Recent large-scale direct numerical simulations (DNS) of high-Reynolds number (high-Re) turbulence, suggest that strong micro-scale tube-like vortices form clusters in localized thin regions of space. However, to this date no thorough quantitative and statistical analysis of the geometry of such vortical clusters has been conducted. This study is intended to generate new statistical tools to study the shape and dynamics of these intense vorticity and strain regions. We first propose a new method for locating and analysing the geometrical properties of thresholded vortical clusters contained inside boxes of a given size. Second, we use this new tool to investigate the natural presence of intense shear layers and their relevance as geometrical features of high-Re homogeneous turbulence. This new method is applied to the DNS of homogeneous incompressible turbulence with up to 40963 grid points, showing that the geometry of high vorticity regions varies strongly depending on the threshold and on the size of the clusters. In particular for sizes in the inertial range of scales and high thresholds, approximately layer-like structures of vortices are extracted and visualized. Agreement of results with previous observations and known features of turbulence supports the validity of the proposed method to characterize the geometry of intense vorticity and strain regions in high-Re turbulence.

Original languageEnglish
Article number012004
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - Apr 29 2016
Externally publishedYes
Event2nd Multiflow Summer School on Turbulence - Madrid, Spain
Duration: May 25 2015Jun 26 2015

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'A new statistical tool to study the geometry of intense vorticity clusters in turbulence'. Together they form a unique fingerprint.

Cite this