Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Kops, Stephen M. de Bruyn"'
Autor:
Meena, Muralikrishnan Gopalakrishnan, Liousas, Demetri, Simin, Andrew D., Kashi, Aditya, Brewer, Wesley H., Riley, James J., Kops, Stephen M. de Bruyn
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 (SST). SST is strongly affected by fine balances between forces and be
Externí odkaz:
http://arxiv.org/abs/2404.16141
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 $Fr_h\gg1$ ($Fr_h$ is the horizontal Froude number), the mixing coefficient $\Gamma$ (defined as the
Externí odkaz:
http://arxiv.org/abs/2402.10704
Autor:
Petropoulos, Nicolaos, Couchman, Miles M. P., Mashayek, Ali, Kops, Stephen M. de Bruyn, Caulfield, Colm-cille P.
Relatively strongly stratified turbulent flows tend to self-organise into a 'layered anisotropic stratified turbulence' (LAST) regime, characterised by relatively deep and well-mixed density 'layers' separated by relatively thin 'interfaces' of enhan
Externí odkaz:
http://arxiv.org/abs/2310.15365
Publikováno v:
J. Fluid Mech. 992 (2024) A10
Recently, direct numerical simulations (DNS) of stably stratified turbulence have shown that as the Prandtl number ($Pr$) is increased from 1 to 7, the mean turbulent potential energy dissipation rate (TPE-DR) drops dramatically, while the mean turbu
Externí odkaz:
http://arxiv.org/abs/2308.00518
Publikováno v:
Journal of Fluid Mechanics, 961, A20 (2023)
Understanding how turbulence enhances irreversible scalar mixing in density-stratified fluids is a central problem in geophysical fluid dynamics. While isotropic overturning regions are commonly the focus of mixing analyses, we here investigate wheth
Externí odkaz:
http://arxiv.org/abs/2210.16148
Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows
Motivated by oceanographic observational datasets, we propose a probabilistic neural network (PNN) model for calculating turbulent energy dissipation rates from vertical columns of velocity and density gradients in density stratified turbulent flows.
Externí odkaz:
http://arxiv.org/abs/2112.01113
Autor:
Zhang, Xiaolong, Dhariwal, Rohit, Portwood, Gavin, Kops, Stephen M. de Bruyn, Bragg, Andrew D.
Budgets of turbulent kinetic energy (TKE) and turbulent potential energy (TPE) at different scales $\ell$ in sheared, stably stratified turbulence are analyzed using a filtering approach. Competing effects in the flow are considered, along with the p
Externí odkaz:
http://arxiv.org/abs/2110.10115
A fundamental effect of fluid turbulence is turbulent mixing, which results in the stretching and wrinkling of scalar isosurfaces. Thus, the area of isosurfaces is of interest in understanding turbulence in general with specific applications in, e.g.
Externí odkaz:
http://arxiv.org/abs/1910.03116
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.