Numerical study of a regularization model for incompressible flow with deconvolution-based adaptive nonlinear filtering

Abstract

We study a trapezoidal-in-time, finite-element-in-space discretization of a new Leray regularization model that locally chooses the filtering radius using a deconvolution based indicator function to identify regions where regularization is needed. Because this indicator function is mathematically based, it allows us to establish a rigorous analysis of the resulting numerical algorithm. We prove well-posedness, unconditional stability, and convergence of the proposed algorithm, and test the model on several benchmark problems.