Domain Decomposition Preconditioners for Multiscale Flows in High Contrast Media: Reduced Dimension Coarse Spaces

In this paper, robust preconditioners for multiscale flow problems are investigated. We consider elliptic equations with highly varying coefficients. We design and analyze two-level domain decomposition preconditioners that converge independent of the contrast in the media properties. The coarse spaces are constructed using selected eigenvectors of a local spectral problem. Our new construction enriches any given initial coarse space to make it suitable for high-contrast problems. Using the initial coarse space we construct local mass matrices for the local eigenvalue problems. We show that there is a gap in the spectrum of the eigenvalue problem when high-conductivity regions are disconnected. The eigenvectors corresponding to small, asymptotically vanishing eigenvalues are chosen to construct an enrichment of the initial coarse space. Only via a judicious choice of the initial space do we reduce the dimension of the resulting coarse space. Classical coarse basis functions such as multiscale or energy minimizing basis functions can be taken as the basis for the initial coarse space. In particular, if we start with classical multiscale basis, the selected eigenvectors represent only high-conductivity features that cannot be localized within a coarse-grid block, e.g., high-conductivity channels that connect the boundaries of a coarse-grid block. Numerical experiments are presented. The new construction presented here can handle tensor coefficients. The results of this paper substantially extend those presented in J. Galvis and Y. Efendiev, Multiscale Model. Simul., 8 (2010), pp. 1461–1483, where only scalar coefficients are considered and the coarse space dimension can be very large because the coarse space includes all isolated high-conductivity features that are within a coarse block.