1 code implementation • 10 Jan 2022 • Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg
In this study, we apply a physics-informed Deep Learning technique based on vector quantization to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows.
1 code implementation • 7 Dec 2021 • Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques.
no code implementations • 12 Jan 2021 • Andrew D. Bragg, David H. Richter, Guiquan Wang
While it may be thought that settling can be ignored when the settling parameter $Sv\equiv v_s/u_\tau$ is small ($v_s$ - Stokes settling velocity, $u_\tau$ - fluid friction velocity), we show that even in this regime the settling may make a leading order contribution to the concentration profiles.
Fluid Dynamics Atmospheric and Oceanic Physics Geophysics