Generalized macroscale model for Cosserat elasticity using Generalized Multiscale Finite Element Method

Ammosov, Dmitry; Efendiev, Yalchin; Grekova, Elena F.; Vasilyeva, Maria

doi:10.1016/j.jcp.2022.111011

Cited by 6 publications

(1 citation statement)

References 74 publications

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“…A variety of domain decomposition methods [43,56,55] have leveraged the idea of building local basis functions utilizing spectral problem. Recently, the GMsFEM has been successfully applied to a variety of problems [2,86,23,79,81,1,3,5,18,27,26,59].…”

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confidence: 99%

Constraint Energy Minimizing Generalized Multiscale Finite Element Method for multi-continuum Richards equations

Mai,

Cheung,

Park

2022

Preprint

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In fluid flow simulation, the multi-continuum model is a useful strategy. When the heterogeneity and contrast of coefficients are high, the system becomes multiscale, and some kinds of reduced order methods are demanded. Combining these techniques with nonlinearity, we will consider in this paper a dual-continuum model which is generalized as a multi-continuum model for a coupled system of nonlinear Richards equations as unsaturated flows, in complex heterogeneous fractured porous media; and we will solve it by a novel multiscale approach utilizing the constraint energy minimizing generalized multiscale finite element method (CEM-GMsFEM). In particular, such a nonlinear system will be discretized in time and then linearized by Picard iteration (whose global convergence is proved theoretically). Subsequently, we tackle the resulting linearized equations by the CEM-GMsFEM and obtain proper offline multiscale basis functions to span the multiscale space (which contains the pressure solution). More specifically, we first introduce two new sources of samples, and the GMsFEM is used over each coarse block to build local auxiliary multiscale basis functions via solving local spectral problems, that are crucial for detecting highcontrast channels. Second, per oversampled coarse region, local multiscale basis functions are created through the CEM as constrainedly minimizing an energy functional. Various numerical tests for our approach reveal that the error converges with the coarse-grid size alone and that only few oversampling layers as well as basis functions are needed.

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