Theory for the Rotational Deconvolution model of Turbulence with Fractional regularization

TL;DR

This paper introduces a fractional regularization approach to the rotational deconvolution model of turbulence, expanding the mathematical framework and analyzing convergence under weaker conditions.

Contribution

It generalizes the existing deconvolution model to include fractional regularization, providing new theoretical insights into turbulence modeling.

Findings

Convergence of the solution is established under weaker regularization conditions.

The fractional regularization enhances the mathematical robustness of the model.

The model extends the applicability of rotational turbulence models.

Abstract

We introduce a new regularization of the rotational Navier-Stokes equations that we call the Rotational Approximate Deconvolution Model (RADM). We generalize the deconvolution type model, studied by Berselli and Lewandowski [5], to the RADM model with fractional regularization where the convergence of the solution is studied with weaker conditions on the parameter regularization.