Generalized multiscale approximation of a multipoint flux mixed finite element method for Darcy-Forchheimer model

TL;DR

This paper develops a generalized multiscale finite element method combined with a multipoint flux mixed finite element approach to efficiently solve the nonlinear Darcy-Forchheimer model in highly heterogeneous porous media, improving accuracy and computational efficiency.

Contribution

It introduces a novel multiscale framework integrating GMsFEM with MFMFE for the Darcy-Forchheimer model, including offline and online basis construction for enhanced accuracy.

Findings

Significant reduction in iteration times using Newton's method.

Online basis functions substantially decrease relative errors.

Numerical results demonstrate improved efficiency and accuracy.

Abstract

In this paper, we propose a multiscale method for the Darcy-Forchheimer model in highly heterogeneous porous media. The problem is solved in the framework of generalized multiscale finite element methods (GMsFEM) combined with a multipoint flux mixed finite element (MFMFE) method. %In the MFMFE methods, appropriate mixed finite element spaces and suitable quadrature rules are employed, which allow for local velocity elimination and lead to a cell-centered system for the pressure. We consider the MFMFE method that utilizes the lowest order Brezzi-Douglas-Marini ($\textrm{BDM}_1$) mixed finite element spaces for the velocity and pressure approximation. The symmetric trapezoidal quadrature rule is employed for the integration of bilinear forms relating to the velocity variables so that the local velocity elimination is allowed and leads to a cell-centered system for the pressure. %on meshes composed of simplices and $h^2$-perturbed parallelograms. We construct multiscale space for the pressure and solve the problem on the coarse grid following the GMsFEM framework. In the offline stage, we construct local snapshot spaces and perform spectral decompositions to get the offline space with a smaller dimension. In the online stage, we use the Newton iterative algorithm to solve the nonlinear problem and obtain the offline solution, which reduces the iteration times greatly comparing to the standard Picard iteration. Based on the offline space and offline solution, we calculate online basis functions which contain important global information to enrich the multiscale space iteratively. The online basis functions are efficient and accurate to reduce relative errors substantially. Numerical examples are provided to highlight the performance of the proposed multiscale method.