1 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows
Sam F. Lewin, Stephen M. de Bruyn Kops, Gavin D. Portwood +1
Motivated by oceanographic observational datasets, we propose a probabilistic neural network (PNN) model for calculating turbulent energy dissipation rates from vertical columns of…