most citedMachine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models

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physics.flu-dyn2026

Impact of alignments between fluctuating and mean density gradients on the scale-dependent energetics of stably stratified turbulence

Soumak Bhattacharjee, Stephen M. de Bruyn Kops, Andrew D. Bragg

Non-trivial alignments between vorticity and the strain-rate tensor play an important role in the evolution of velocity gradients and the energy cascade in isotropic turbulence. He…

physics.flu-dyn2026

Evolution of passive scalar mixing layers in stratified and unstratified homogeneous turbulence

Stephen M. de Bruyn Kops, Peter N. Blossey, James J. Riley

High-resolution large-eddy simulations of decaying stratified and unstratified homogeneous turbulence are used to understand the mixing of passive scalars in stably stratified flow…

physics.flu-dyn20241 cited

Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models

Muralikrishnan Gopalakrishnan Meena, Demetri Liousas, Andrew D. Simin +4

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence…

physics.flu-dyn2024

Asymptotic analysis of mixing in stratified turbulent flows, and the conditions for an inertial sub-range

Andrew D. Bragg, Stephen M. de Bruyn Kops

In an important study, Maffioli et al. (J. Fluid Mech., Vol. 794 , 2016) used a scaling analysis to predict that in the weakly stratified flow regime ( is the hori…

physics.flu-dyn20231 cited

Prandtl number effects on extreme mixing events in forced stratified turbulence

Nicolaos Petropoulos, Miles M. P. Couchman, Ali Mashayek +2

Relatively strongly stratified turbulent flows tend to self-organise into a 'layered anisotropic stratified turbulence' (LAST) regime, characterised by relatively deep and well-mix…

physics.flu-dyn2023

Understanding the effect of Prandtl number on momentum and scalar mixing rates in neutral and stably stratified flows using gradient field dynamics

Andrew D. Bragg, Stephen M. de Bruyn Kops

Recently, direct numerical simulations (DNS) of stably stratified turbulence have shown that as the Prandtl number () is increased from 1 to 7, the mean turbulent potential ene…