3 citations · 3 across the 2 of their papers we have counts for
3 papers
Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers
Alexis-Tzianni G. Charalampopoulos, Themistoklis P. Sapsis
We formulate a data-driven, physics-constrained closure method for coarse-scale numerical simulations of turbulent fluid flows. Our approach involves a closure scheme that is non-l…
A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations
Spencer H. Bryngelson, Alexis Charalampopoulos, Themistoklis P. Sapsis +1
Phase-averaged dilute bubbly flow models require high-order statistical moments of the bubble population. The method of classes, which directly evolve bins of bubbles in the probab…
Implementation of a fully nonlinear Hamiltonian Coupled-Mode Theory, and application to solitary wave problems over bathymetry
Ch. E. Papoutsellis, A. G. Charalampopoulos, G. A. Athanassoulis
This paper deals with the implementation of a new, efficient, non-perturbative, Hamiltonian coupled-mode theory (HCMT) for the fully nonlinear, potential flow (NLPF) model of water…