Current State of Atmospheric Turbulence Cascades

TL;DR

This paper evaluates the accuracy of DNS and LES simulations in replicating multifractal turbulence cascade models, aiming to improve atmospheric turbulence understanding and remote sensing techniques.

Contribution

It introduces a method to analyze DNS and LES data using multifractal models, moving beyond Kolmogorov assumptions for better atmospheric turbulence representation.

Findings

LES and DNS data exhibit multifractal characteristics consistent with experimental models

The analysis improves understanding of anisotropic turbulence cascades

Enhanced modeling can lead to better remote sensing metrics

Abstract

Turbulence cascade has been modeled using various methods; the one we have used applies to a more exact representation of turbulence where people use the multifractal representation. The nature of the energy dissipation is usually governed by partial differential equations that have been described, such as Navier-Stokes Equations, although usually in climate modeling, the Kolmogorov turbulence cascading approximation leads towards an isotropic representation. In recent years, Meneveau et al. have proposed to go away from Kolmogorov assumptions and propose multifractal models where we can account for a new anisotropic representation. Our research aims to use Direct Numerical Simulations (DNS) from the JHU Turbulence Database and Large Eddy Simulations (LES) we simulated using OpenFOAM to predict how accurate these simulations are in replicating Meneveau experimental procedures with numerical simulations using the same rigorous mathematical approaches. Modeling turbulence cascading using higher fidelity data will advance the field and produce faster and better remote sensing metrics. We have written computer code to analyze DNS and LES data and study the multifractal nature of energy dissipation. The box-counting method is used to identify the multifractal dimension spectrum of the DNS and LES data in every direction to follow Meneveau work to represent turbulence-cascading effects in the atmosphere better.