Abstract
This work presents an overview of techniques that enable the construction of collocated finite volume method for complex multi-physics models in multiple domains. Each domain is characterized by the properties of heterogeneous media and features a distinctive multi-physics model. Coupling together systems of equations, corresponding to multiple unknowns, results in a vector flux. The finite volume method requires continuity of intradomain and interdomain vector fluxes. The continuous flux is derived using an extension of the harmonic averaging point concept. Often, the collocated coupling of the equations results in a saddle-point problem subject to inf-sup stability issues. These issues are addressed by the eigen-splitting of indefinite matrix coefficients encountered in the flux expression. The application of the techniques implemented within INMOST platform to hydraulic fracturing problem is demonstrated.
Acknowledgment
The author would like to thank Igor Konshin for help in the preparation of this article.
Funding: The study was supported by the Russian Science Foundation through the grant 18-71-10111.
References
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Preface
- Methods of variational data assimilation with application to problems of hydrothermodynamics of marine water areas
- Mathematical immunology: from phenomenological to multiphysics modelling
- Iterative solution methods for elliptic boundary value problems
- Multi-physics flux coupling for hydraulic fracturing modelling within INMOST platform
- Tensor decompositions and rank increment conjecture
- Global optimization based on TT-decomposition
Articles in the same Issue
- Frontmatter
- Preface
- Methods of variational data assimilation with application to problems of hydrothermodynamics of marine water areas
- Mathematical immunology: from phenomenological to multiphysics modelling
- Iterative solution methods for elliptic boundary value problems
- Multi-physics flux coupling for hydraulic fracturing modelling within INMOST platform
- Tensor decompositions and rank increment conjecture
- Global optimization based on TT-decomposition