5,262 research outputs found
A fully implicit, fully adaptive time and space discretisation method for phase-field simulation of binary alloy solidification
A fully-implicit numerical method based upon adaptively refined meshes for the
simulation of binary alloy solidification in 2D is presented. In addition we combine a
second-order fully-implicit time discretisation scheme with variable steps size control
to obtain an adaptive time and space discretisation method. The superiority of this
method, compared to widely used fully-explicit methods, with respect to CPU time
and accuracy, is shown. Due to the high non-linearity of the governing equations
a robust and fast solver for systems of nonlinear algebraic equations is needed to
solve the intermediate approximations per time step. We use a nonlinear multigrid
solver which shows almost h-independent convergence behaviour
Adaptive control in rollforward recovery for extreme scale multigrid
With the increasing number of compute components, failures in future
exa-scale computer systems are expected to become more frequent. This motivates
the study of novel resilience techniques. Here, we extend a recently proposed
algorithm-based recovery method for multigrid iterations by introducing an
adaptive control. After a fault, the healthy part of the system continues the
iterative solution process, while the solution in the faulty domain is
re-constructed by an asynchronous on-line recovery. The computations in both
the faulty and healthy subdomains must be coordinated in a sensitive way, in
particular, both under and over-solving must be avoided. Both of these waste
computational resources and will therefore increase the overall
time-to-solution. To control the local recovery and guarantee an optimal
re-coupling, we introduce a stopping criterion based on a mathematical error
estimator. It involves hierarchical weighted sums of residuals within the
context of uniformly refined meshes and is well-suited in the context of
parallel high-performance computing. The re-coupling process is steered by
local contributions of the error estimator. We propose and compare two criteria
which differ in their weights. Failure scenarios when solving up to
unknowns on more than 245\,766 parallel processes will be
reported on a state-of-the-art peta-scale supercomputer demonstrating the
robustness of the method
Algorithms and data structures for adaptive multigrid elliptic solvers
Adaptive refinement and the complicated data structures required to support it are discussed. These data structures must be carefully tuned, especially in three dimensions where the time and storage requirements of algorithms are crucial. Another major issue is grid generation. The options available seem to be curvilinear fitted grids, constructed on iterative graphics systems, and unfitted Cartesian grids, which can be constructed automatically. On several grounds, including storage requirements, the second option seems preferrable for the well behaved scalar elliptic problems considered here. A variety of techniques for treatment of boundary conditions on such grids are reviewed. A new approach, which may overcome some of the difficulties encountered with previous approaches, is also presented
Convergence and Optimality of Adaptive Mixed Finite Element Methods
The convergence and optimality of adaptive mixed finite element methods for
the Poisson equation are established in this paper. The main difficulty for
mixed finite element methods is the lack of minimization principle and thus the
failure of orthogonality. A quasi-orthogonality property is proved using the
fact that the error is orthogonal to the divergence free subspace, while the
part of the error that is not divergence free can be bounded by the data
oscillation using a discrete stability result. This discrete stability result
is also used to get a localized discrete upper bound which is crucial for the
proof of the optimality of the adaptive approximation
A robust adaptive algebraic multigrid linear solver for structural mechanics
The numerical simulation of structural mechanics applications via finite
elements usually requires the solution of large-size and ill-conditioned linear
systems, especially when accurate results are sought for derived variables
interpolated with lower order functions, like stress or deformation fields.
Such task represents the most time-consuming kernel in commercial simulators;
thus, it is of significant interest the development of robust and efficient
linear solvers for such applications. In this context, direct solvers, which
are based on LU factorization techniques, are often used due to their
robustness and easy setup; however, they can reach only superlinear complexity,
in the best case, thus, have limited applicability depending on the problem
size. On the other hand, iterative solvers based on algebraic multigrid (AMG)
preconditioners can reach up to linear complexity for sufficiently regular
problems but do not always converge and require more knowledge from the user
for an efficient setup. In this work, we present an adaptive AMG method
specifically designed to improve its usability and efficiency in the solution
of structural problems. We show numerical results for several practical
applications with millions of unknowns and compare our method with two
state-of-the-art linear solvers proving its efficiency and robustness.Comment: 50 pages, 16 figures, submitted to CMAM
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