Global well-posedness and numerical justification of an effective micro-macro model for reactive transport in elastic perforated media
In this paper we develop and analyze a variational data assimilation method with efficient decoupled iterative numerical algorithms for the Stokes-Darcy equations with the Beavers-Joseph interface condition. By using Tikhonov regularization and formulating the variational data assimilation into an o...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2025
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| In: |
Multiscale modeling & simulation
Year: 2025, Jahrgang: 23, Heft: 1, Pages: 458-485 |
| ISSN: | 1540-3467 |
| DOI: | 10.1137/24M1644304 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1137/24M1644304 Verlag, lizenzpflichtig, Volltext: https://epubs.siam.org/doi/10.1137/24M1644304 
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| Verfasserangaben: | Jonas Knoch, Markus Gahn, and Maria Neuss-Radu |
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| 245 | 1 | 0 | |a Global well-posedness and numerical justification of an effective micro-macro model for reactive transport in elastic perforated media |c Jonas Knoch, Markus Gahn, and Maria Neuss-Radu |
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| 520 | |a In this paper we develop and analyze a variational data assimilation method with efficient decoupled iterative numerical algorithms for the Stokes-Darcy equations with the Beavers-Joseph interface condition. By using Tikhonov regularization and formulating the variational data assimilation into an optimization problem, we establish the existence, uniqueness, and stability of the optimal solution. Based on the weak formulation of the Stokes-Darcy equations, the Lagrange multiplier rule is utilized to derive the first order optimality system for both the continuous and discrete variational data assimilation problems, where the discrete data assimilation is based on a finite element discretization in space and the backward Euler scheme in time. By rescaling the optimality system and then analyzing its corresponding bilinear forms, we prove the optimal finite element convergence rate with special attention paid to recovering uncertainties missed in the optimality system. To solve the discrete optimality system efficiently, three decoupled iterative algorithms are proposed to address the computational cost for both well-conditioned and ill-conditioned variational data assimilation problems, respectively. Finally, numerical results are provided to validate the proposed methods. | ||
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