A consistent stochastic large-scale representation of the Navier-Stokes equations
Arnaud Debussche, Berenger Hug, Etienne Memin

TL;DR
This paper provides a theoretical analysis of a stochastic large-scale representation of the Navier-Stokes equations within the framework of modeling under location uncertainty, establishing existence, uniqueness, and convergence properties.
Contribution
It proves the existence and uniqueness of solutions for the stochastic Navier-Stokes equations in LU form and shows their convergence to deterministic solutions as noise diminishes.
Findings
Existence of martingale solutions for stochastic Navier-Stokes in LU form.
Pathwise and unique solutions in 2D flows.
Solutions converge to deterministic Navier-Stokes solutions as noise intensity approaches zero.
Abstract
In this paper we analyze the theoretical properties of a stochastic representation of the incompressible Navier-Stokes equations defined in the framework of the modeling under location uncertainty (LU). This setup built from a stochastic version of the Reynolds transport theorem incorporates a so-called transport noise and involves several specific additional features such as a large scale diffusion term, akin to classical subgrid models, and a modified advection term arising from the spatial inhomogeneity of the small-scale velocity components. This formalism has been numerically evaluated in a series of studies with a particular interest on geophysical flows approximations and data assimilation. In this work we focus more specifically on its theoretical analysis. We demonstrate, through classical arguments, the existence of martingale solutions for the stochastic Navier-Stokes…
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Taxonomy
TopicsReservoir Engineering and Simulation Methods · Stochastic processes and financial applications · Probabilistic and Robust Engineering Design
