Implications of inertial subrange scaling for stably stratified mixing

Abstract

We investigate the effects of the turbulent dynamic range on active scalar mixing in stably stratified turbulence by adapting the theoretical passive scalar modelling arguments of Beguier, Dekeyser & Launder (1978) (Phys. Fluids, vol. 21 (3), pp. 307–310) and demonstrating their usefulness through consideration of the results of direct numerical simulations of statistically stationary homogeneous stratified and sheared turbulence. By analysis of inertial and inertial–convective subrange scalings, we show that the relationship between the active scalar and turbulence time scales is predicted by the ratio of the Kolmogorov and Obukhov–Corrsin constants, provided mean flow parameters permit the two subrange scalings to be appropriate approximations. We use the resulting relationship between time scales to parameterise an appropriate turbulent mixing coefficient $\varGamma \equiv \chi /\epsilon$ , defined here as the ratio of available potential energy ( $E_p$ ) and turbulent kinetic energy ( $E_k$ ) dissipation rates. With the analysis presented here, we show that $\varGamma$ can be estimated by $E_p,E_k$ and a universal constant provided an appropriate Reynolds number is sufficiently high. This large Reynolds number regime appears here to occur at $ {{Re_b}} \equiv \epsilon / u N^{2} \gtrapprox 300$ where $ u$ is the kinematic viscosity and $N$ is the characteristic buoyancy frequency. We propose a model framework for irreversible diapycnal mixing with robust theoretical parametrisation and asymptotic behaviour in this high- $ {{Re_b}}$ limit.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 24, 2022
Source ID
10.1017/jfm.2022.160

Entities

People

  • Colm-Cille Caulfield
  • Gavin Portwood
  • Stephen M. de Bruyn Kops

Organizations

  • Office of Naval Research

Tags

Fields of Study

  • Physics

Readers

  • Calculus or Mathematical Analysis
  • Fluid Mechanics and Fluid Dynamics.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers