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Local-Global Model Reduction for Large-Scale Models Integrating Systems-Theoretical Properties

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Technical Report,15 May 2012,14 May 2016

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Texas Engineering Experiment Station College Station United States

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The main objective of this proposal is to develop efficient and accurate reduced-order models comprised of multiscale and multiphysics characteristics amenable for fast simulation of large-scale problems of flow and assessment of uncertainty in highly heterogeneous porous media. This effort will incorporate multiscale methods and system theory reduced-order modeling for nonlinear systems for a broad spectrum of applications, ranging from single-phase, to multiphase flow and transport phenomena. In our approach, we develop a framework which balances the error from global reduced-order models and local multiscale approximations. Another unique feature of the proposed work involves the development of extensions to nonlinear uncertain parameter-dependent problems in subsurface flow simulation. The project will attempt to achieve the following results1 development of a new local-global multiscale model reduction framework based system theory and multiscale techniques for processes in highly heterogeneous porous media 2 development of multiscale methods for complex nonlinear systems of two-phase flows 3 derivation of error estimators for reduced large-scale discretized models for characterizing model solution accuracy based on system-theoretical properties 4 extensions of the proposed techniques to nonlinear and stochastic parameter-dependent systems

Subject Categories:

  • Numerical Mathematics

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