This work aims at introducing modelling, theoretical and numerical studies related to a new downscaling technique applied to computational fluid dynamics.
Our method consists in building a local model, forced by large scale information computed thanks to a classical numerical weather predictor.
The local model, compatible with the Navier-Stokes equations, is used
for the small scale computation (downscaling) of the considered
fluid. It is
inspired by Pope's works on turbulence, and consists in a so-called Langevin system of stochastic differential equations. We introduce
this model and exhibit its links with classical RANS models. Well-posedness, as well as mean-field interacting particle approximations and boundary condition issues are addressed. We present the numerical discretization of the stochastic downscaling method and investigate the accuracy of the proposed algorithm on simplified situations.