Convergence of the stochastic Navier-Stokes-α solutions toward the stochastic Navier-Stokes solutions

Loosely speaking, the Navier-Stokes-⍺ model and the Navier-Stokes equations differ by a spatial filtration parametrized by a scale denoted ⍺. Starting from a strong two-dimensional solution to the Navier-Stokes-α model driven by a multiplicative noise, we demonstrate that it generates a strong solution to the stochastic Navier-Stokes equations under the condition ⍺ goes to 0. The initially introduced probability space and the Wiener process are maintained throughout the investigation, thanks to a local monotonicity property that abolishes the use of Skorokhod’s theorem. High spatial regularity a priori estimates for the fluid velocity vector field are carried out within periodic boundary conditions