Spatiotemporal spectral transfers in fluid dynamics

TL;DR

This paper introduces a spatio-temporal spectral transfer method for analyzing variability scales in fluid dynamics, applicable to ocean and atmospheric data, and demonstrates its robustness and utility through turbulence simulation analysis.

Contribution

The paper develops a generalized spatio-temporal spectral transfer tool using time-frequency methods, extending frequency transfer analysis to practical fluid dynamics applications.

Findings

The method effectively analyzes energy transfers in turbulence simulations.

Spatio-temporal transfers are robust to low sampling and nonstationarity.

Application to 2D turbulence demonstrates the method's utility.

Abstract

Motivated by previous work on kinetic energy cascades in the ocean and atmosphere, we develop a spatio-temporal spectral transfer tool that can be used to study scales of variability in generalized dynamical systems. In particular, we use generalized time-frequency methods from signal analysis to broaden the applicability of frequency transfers from theoretical to practical applications such as the study of ocean or atmosphere data or simulation output. We also show that triad interactions in wavenumber used to study kinetic energy and enstrophy cascades can be generalized to study triad interactions in frequency or wavenumber-frequency. We study the effects of sweeping on the locality of frequency transfers and frequency triad interactions to better understand the locality of spatio-temporal frequency transfers. As an illustrative example, we use the spatio-temporal spectral transfer tool to study the results of a simulation of two-dimensional homogeneous isotropic turbulence. This simulated fluid is forced at a well-defined wavenumber and frequency with dissipation occurring at both large and small scales, making this one of the first studies of "modulated turbulence" in two dimensions. Our results show that the spatio-temporal transfers we develop in this paper are robust to potential practical problems such as low sampling rates or nonstationarity in time series of interest. We anticipate that this method will be a useful tool in studying scales of spatio-temporal variability in a wide range of fluids applications as higher resolution data and simulations become more widely available.