Approximate continuous data assimilation of the 2D Navier-Stokes equations via the Voigt-regularization with observable data

Abstract

We propose a data assimilation algorithm for the 2D Navier-Stokes equations, based on the Azouani, Olson, and Titi (AOT) algorithm, but applied to the 2D Navier-Stokes-Voigt equations. Adapting the AOT algorithm to regularized versions of Navier-Stokes has been done before, but the innovation of this work is to drive the assimilation equation with observational data, rather than data from a regularized system. We first prove that this new system is globally well-posed. Moreover, we prove that for any admissible initial data, the $ L^2 $ and $ H^1 $ norms of error are bounded by a constant times a power of the Voigt-regularization parameter $ \alpha>0 $, plus a term which decays exponentially fast in time. In particular, the large-time error goes to zero algebraically as $ \alpha $ goes to zero. Assuming more smoothness on the initial data and forcing, we also prove similar results for the $ H^2 $ norm.