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| Titel |
Recent progress in modelling 3D lithospheric deformation |
| VerfasserIn |
B. J. P. Kaus, A. Popov, D. A. May |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250071360
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| Zusammenfassung |
| Modelling 3D lithospheric deformation remains a challenging task, predominantly because
the variations in rock types, as well as nonlinearities due to for example plastic deformation
result in sharp and very large jumps in effective viscosity contrast. As a result, there are only
a limited number of 3D codes available, most of which are using direct solvers which are
computationally and memory-wise very demanding. As a result, the resolutions for
typical model runs are quite modest, despite the use of hundreds of processors
(and using much larger computers is unlikely to bring much improvement in this
situation).
For this reason we recently developed a new 3D deformation code,called LaMEM:
Lithosphere and Mantle Evolution Model. LaMEM is written on top of PETSc, and as a result
it runs on massive parallel machines and we have a large number of iterative solvers available
(including geometric and algebraic multigrid methods). As it remains unclear which solver
combinations work best under which conditions, we have implemented most currently
suggested methods (such as schur complement reduction or Fully coupled iterations). In
addition, we can use either a finite element discretization (with Q1P0, stabilized Q1Q1 or
Q2P-1 elements) or a staggered finite difference discretization for the same input geometry,
which is based on a marker and cell technique). This gives us he flexibility to test various
solver methodologies on the same model setup, in terms of accuracy, speed, memory usage
etc.
Here, we will report on some features of LaMEM, on recent code additions, as well as on
some lessons we learned which are important for modelling 3D lithospheric deformation.
Specifically we will discuss:
1) How we combine a particle-and-cell method to make it work with both a finite
difference and a (lagrangian, eulerian or ALE) finite element formulation, with only minor
code modifications code
2) How finite difference and finite element discretizations compare in terms of accuracy,
speed, and robustness for iterative solvers.
3) How the code scales on massive parallel machines (we have performed simulations on
over 4096 cores with over a billion degrees of freedom).
4) We will give an overview of some recent scientific applications of the code. |
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