Open AccessA network-based perspective on coherent structure detection from very-sparse Lagrangian data
Author(s) -
Giovanni Iacobello,
David E. Rival
Publication year - 2021
Publication title -
international symposium on particle image velocimetry.
Language(s) - English
Resource type - Journals
ISSN - 2769-7576
DOI - 10.18409/ispiv.v1i1.130
Subject(s) - lagrangian coherent structures , lagrangian , eulerian path , representation (politics) , perspective (graphical) , flow (mathematics) , trajectory , fluid dynamics , fluid mechanics , motion (physics) , computer science , mathematics , classical mechanics , statistical physics , physics , mechanics , artificial intelligence , turbulence , politics , political science , law , astronomy
Coherent structure detection (CSD) is a long-lasting issue in fluid mechanics research as the presence of spatio-temporal coherent motion enables simpler ways to characterize the flow dynamics. Such reducedorder representation, in fact, has significant implications for the understanding of the dynamics of flows, as well as their modeling and control (Hussain, 1986). While the Eulerian framework has been extensively adopted for CSD, Lagrangian coherent structures have recently received increasing attention, mainly driven by advancements in Lagrangian flow measurement techniques (Haller, 2015; Hadjighasem et al., 2017). Lagrangian particle tracking (LPT), in particular, is widely used nowadays due to its ability to quantity fluid-parcel trajectories in three-dimensional volumes (Schanz et al., 2016).