Extracting waves and vortices from Lagrangian trajectories

TL;DR

This paper introduces a wavelet-based method to extract and distinguish vortex and wave motions from Lagrangian trajectories in fluid flows, enabling detailed analysis of oscillatory behaviors in turbulent systems.

Contribution

The paper presents a novel, parameter-efficient technique for separating vortex and wave motions in Lagrangian data without operator intervention, using complex wavelet transforms.

Findings

Method accurately extracts vortex orbiting motions from trajectories.

Vortex motions are high frequency and nearly circular, distinguishable from low-frequency wave meandering.

Eccentricity of signals serves as a new diagnostic for motion characterization.

Abstract

A method for extracting time-varying oscillatory motions from time series records is applied to Lagrangian trajectories from a numerical model of eddies generated by an unstable equivalent barotropic jet on a beta plane. An oscillation in a Lagrangian trajectory is represented mathematically as the signal traced out as a particle orbits a time-varying ellipse, a model which captures wavelike motions as well as the displacement signal of a particle trapped in an evolving vortex. Such oscillatory features can be separated from the turbulent background flow through an analysis founded upon a complex-valued wavelet transform of the trajectory. Application of the method to a set of one hundred modeled trajectories shows that the oscillatory motions of Lagrangian particles orbiting vortex cores appear to be extracted very well by the method, which depends upon only a handful of free parameters and which requires no operator intervention. Furthermore, vortex motions are clearly distinguished from wavelike meandering of the jet---the former are high frequency, nearly circular signals, while the latter are linear in polarization and at much lower frequencies. This suggests that the proposed method can be useful for identifying and studying vortex and wave properties in large Lagrangian datasets. In particular, the eccentricity of the oscillatory displacement signals, a quantity which is not normally considered in Lagrangian studies, emerges as an informative diagnostic for characterizing qualitatively different types of motion.