Preconditioning and Reduced-Order Modeling of Navier-Stokes Equations in Complex Porous Microstructures

TL;DR

This paper introduces novel algebraic and geometric preconditioners for efficiently solving the Navier-Stokes equations in complex porous microstructures, significantly improving convergence and computational speed.

Contribution

The authors develop and benchmark new preconditioners based on pore-level multiscale and pore network models for faster, scalable solutions of Navier-Stokes equations in porous media.

Findings

gPLMM preconditioner outperforms existing methods

Preconditioners enable parallel computation

Faster convergence for steady-state and transient flows

Abstract

We aim to solve the incompressible Navier-Stokes equations within the complex microstructure of a porous material. Discretizing the equations on a fine grid using a staggered (e.g., marker-and-cell, mixed FEM) scheme results in a nonlinear residual. Adopting the Newton method, a linear system must be solved at each iteration, which is large, ill-conditioned, and has a saddle-point structure. This demands an iterative (e.g., Krylov) solver, that requires preconditioning to ensure rapid convergence. We propose two monolithic \textit{algebraic} preconditioners, $a\mathrm{PLMM_{NS}}$ and $a\mathrm{PNM_{NS}}$, that are generalizations of previously proposed forms by the authors for the Stokes equations ($a\mathrm{PLMM_{S}}$ and $a\mathrm{PNM_{S}}$). The former is based on the pore-level multiscale method (PLMM) and the latter on the pore network model (PNM), both successful approximate solvers. We also formulate faster-converging geometric preconditioners $g\mathrm{PLMM}$ and $g\mathrm{PNM}$, which impose $\partial_n\boldsymbol{u}\!=\!0$ (zero normal-gradient of velocity) exactly at subdomain interfaces. Finally, we propose an accurate coarse-scale solver for the steady-state Navier-Stokes equations based on $g\mathrm{PLMM}$, capable of computing approximate solutions orders of magnitude faster. We benchmark our preconditioners against state-of-the-art block preconditioners and show $g\mathrm{PLMM}$ is the best-performing one, followed closely by $a\mathrm{PLMM_{S}}$ for steady-state flow and $a\mathrm{PLMM_{NS}}$ for transient flow. All preconditioners can be built and applied on parallel machines.