A Relative Liutex Method for Vortex Identification

TL;DR

This paper introduces a new relative Liutex vortex identification method that is Galilean invariant, more selective, and effective at capturing both strong and weak vortical structures based solely on local flow information.

Contribution

The study proposes a novel relative Liutex method with explicit formulation, enhancing vortex detection accuracy and physical coherence over traditional methods.

Findings

Successfully captures both strong and weak vortices

Provides more selective vortex identification in complex flows

Outperforms conventional methods in boundary-layer transition cases

Abstract

A relative Liutex vortex identification method is proposed in this study, together with its explicit mathematical formulation. The method is designed to identify vortical structures based solely on local flow-field information and is inherently Galilean invariant, ensuring robustness under different reference frames. To validate the proposed approach, a three-dimensional flat-plate boundary-layer transition case is investigated, in which the relative Liutex is systematically compared with conventional vortex identification methods, including the Q-criterion and the original Liutex method. The results show that the relative Liutex is capable of simultaneously capturing both strong and weak vortical structures. Importantly, its behavior cannot be interpreted as a simple superposition of Liutex iso-surfaces obtained using different threshold values. Instead, the relative Liutex provides a more selective and physically coherent identification of weak vortices, particularly in regions above the $\Lambda$-vortex and in the downstream hairpin-vortex structures, while effectively suppressing spurious and noise-induced features. These advantages arise from its formulation based on local velocity-gradient strength rather than a global vortical-intensity measure. Owing to its ability to consistently identify vortices across a wide range of intensities, the relative Liutex demonstrates strong potential for revealing complex vortex structures and underlying flow mechanisms in vortex-dominated flows.