Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Ephrati, Sagy"'
In this paper, we study geostrophic turbulence without external forcing or dissipation, using a Casimir-preserving numerical method. The research examines the formation of large zonal jets, common in geophysical flows, especially in giant gas planets
Externí odkaz:
http://arxiv.org/abs/2409.05432
Accurate long-term predictions of large-scale flow features on planets are crucial for understanding global atmospheric and oceanic systems, necessitating the development of numerical methods that can preserve essential physical structures over exten
Externí odkaz:
http://arxiv.org/abs/2409.05410
An integrator for a class of stochastic Lie-Poisson systems driven by Stratonovich noise is developed. The integrator is suited for Lie-Poisson systems that also admit an isospectral formulation, which enables scalability to high-dimensional systems.
Externí odkaz:
http://arxiv.org/abs/2408.16701
Autor:
Ephrati, Sagy
A framework for deriving probabilistic data-driven closure models is proposed for coarse-grained numerical simulations of turbulence in statistically stationary state. The approach unites the ideal large-eddy simulation model and data assimilation me
Externí odkaz:
http://arxiv.org/abs/2408.14838
We present a geometric derivation of the quasi-geostrophic equations on the sphere, starting from the rotating shallow water equations. We utilise perturbation series methods in vorticity and divergence variables. The derivation employs asymptotic an
Externí odkaz:
http://arxiv.org/abs/2402.13707
A closure model is presented for large-eddy simulation (LES) based on the three-dimensional variational data assimilation algorithm. The approach aims at reconstructing high-fidelity kinetic energy spectra in coarse numerical simulations by including
Externí odkaz:
http://arxiv.org/abs/2312.12858
Publikováno v:
Journal of Fluid Mechanics. 2023;975:A35
A data-driven turbulence model for coarse-grained numerical simulations of two-dimensional Rayleigh-B\'enard convection is proposed. The model starts from high-fidelity data and is based on adjusting the Fourier coefficients of the numerical solution
Externí odkaz:
http://arxiv.org/abs/2305.10043
A resolution-independent data-driven stochastic parametrization method for subgrid-scale processes in coarsened fluid descriptions is proposed. The method enables the inclusion of high-fidelity data into the coarsened flow model, thereby enabling acc
Externí odkaz:
http://arxiv.org/abs/2304.12007
The 2D Euler equations are a simple but rich set of non-linear PDEs that describe the evolution of an ideal inviscid fluid, for which one dimension is negligible. Solving numerically these equations can be extremely demanding. Several techniques to o
Externí odkaz:
http://arxiv.org/abs/2301.06326
In this paper, we propose and assess several stochastic parametrizations for data-driven modelling of the two-dimensional Euler equations using coarse-grid SPDEs. The framework of Stochastic Advection by Lie Transport (SALT) [Cotter et al., 2019] is
Externí odkaz:
http://arxiv.org/abs/2204.02193