Stochastic multiscale flux basis for Stokes-Darcy flows
Ilona Ambartsumyan, Eldar Khattatov, ChangQing Wang, Ivan Yotov

TL;DR
This paper introduces three algorithms for uncertainty quantification in coupled Stokes-Darcy flows, with a focus on reducing computational costs using multiscale flux basis methods in stochastic collocation frameworks.
Contribution
The paper develops novel multiscale flux basis algorithms that significantly decrease computational costs in stochastic simulations of coupled flow problems.
Findings
Multiscale flux basis reduces subdomain solves during interface iterations.
Stochastic basis reuse improves efficiency across multiple realizations.
Numerical results demonstrate substantial computational savings.
Abstract
Three algorithms are developed for uncertainty quantification in modeling coupled Stokes and Darcy flows. The porous media may consist of multiple regions with different properties. The permeability is modeled as a non-stationary stochastic variable, with its log represented as a sum of local Karhunen-Lo\`eve (KL) expansions. The problem is approximated by stochastic collocation on either tensor-product or sparse grids, coupled with a multiscale mortar mixed finite element method for the spatial discretization. A non-overlapping domain decomposition algorithm reduces the global problem to a coarse scale mortar interface problem, which is solved by an iterative solver, for each stochastic realization. In the traditional implementation, each subdomain solves a local Dirichlet or Neumann problem in every interface iteration. To reduce this cost, two additional algorithms based on…
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