# Pressure-robustness in quasi-optimal a priori estimates for the Stokes problem

## TL;DR

This paper establishes pressure-robust a priori error estimates for the Stokes problem that are valid under minimal regularity assumptions, improving the understanding of discretization accuracy in incompressible flow simulations.

## Contribution

It provides the first general a priori estimate for pressure-robust discretizations of the Stokes problem applicable to data with minimal regularity.

## Key findings

- Pressure-robust estimates depend only on the Helmholtz--Hodge projector of data.
- The new estimates are valid without assuming high regularity of the velocity.
- Numerical examples confirm the theoretical results.

## Abstract

Recent analysis of the divergence constraint in the incompressible Stokes/Navier--Stokes problem has stressed the importance of equivalence classes of forces and how it plays a fundamental role for an accurate space discretization. Two forces in the momentum balance are velocity--equivalent if they lead to the same velocity solution, i.e., if and only if the forces differ by only a gradient field. Pressure-robust space discretizations are designed to respect these equivalence classes. One way to achieve pressure-robust schemes is to introduce a non-standard discretization of the right-side forcing term for any inf-sup stable mixed finite element method. This modification leads to pressure-robust and optimal-order discretizations, but a proof was only available for smooth situations and remained open in the case of minimal regularity, where it cannot be assumed that the vector Laplacian of the velocity is at least square-integrable. This contribution closes this gap by delivering a general estimate for the consistency error that depends only on the regularity of the data term. Pressure-robustness of the estimate is achieved by the fact that the new estimate only depends on the $L^2$ norm of the Helmholtz--Hodge projector of the data term and not on the $L^2$ norm of the entire data term. Numerical examples illustrate the theory.