Journal
ADVANCES IN WATER RESOURCES
Volume 114, Issue -, Pages 19-28Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2018.01.027
Keywords
Algebraic multigrid; Preconditioning; Compositional two-phase flow; Phase transitions; Nonlinear complementarity problem
Categories
Funding
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- U.S. Department of Energy Office of Advanced Scientific Computing Research, Applied Mathematics program [DE-SC0009301]
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Multiphase flow is a critical process in a wide range of applications, including oil and gas recovery, carbon sequestration, and contaminant remediation. Numerical simulation of multiphase flow requires solving of a large, sparse linear system resulting from the discretization of the partial differential equations modeling the flow. In the case of multiphase multicomponent flow with miscible effect, this is a very challenging task. The problem becomes even more difficult if phase transitions are taken into account. A new approach to handle phase transitions is to formulate the system as a nonlinear complementarity problem (NCP). Unlike in the primary variable switching technique, the set of primary variables in this approach is fixed even when there is phase transition. Not only does this improve the robustness of the nonlinear solver, it opens up the possibility to use multigrid methods to solve the resulting linear system. The disadvantage of the complementarity approach, however, is that when a phase disappears, the linear system has the structure of a saddle point problem and becomes indefinite, and current algebraic multigrid (AMG) algorithms cannot be applied directly. In this study, we explore the effectiveness of a new multilevel strategy, based on the multigrid reduction technique, to deal with problems of this type. We demonstrate the effectiveness of the method through numerical results for the case of two-phase, two-component flow with phase appearance/disappearance. We also show that the strategy is efficient and scales optimally with problem size. (C) 2018 Elsevier Ltd. All rights reserved.
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