244 research outputs found
A micro/macro parallel-in-time (parareal) algorithm applied to a climate model with discontinuous non-monotone coefficients and oscillatory forcing
We present the application of a micro/macro parareal algorithm for a 1-D
energy balance climate model with discontinuous and non-monotone coefficients
and forcing terms. The micro/macro parareal method uses a coarse propagator,
based on a (macroscopic) 0-D approximation of the underlying (microscopic) 1-D
model. We compare the performance of the method using different versions of the
macro model, as well as different numerical schemes for the micro propagator,
namely an explicit Euler method with constant stepsize and an adaptive library
routine. We study convergence of the method and the theoretical gain in
computational time in a realization on parallel processors. We show that, in
this example and for all settings, the micro/macro parareal method converges in
fewer iterations than the number of used parareal subintervals, and that a
theoretical gain in performance of up to 10 is possible
Asymptotic-Preserving Monte Carlo methods for transport equations in the diffusive limit
We develop a new Monte Carlo method that solves hyperbolic transport
equations with stiff terms, characterized by a (small) scaling parameter. In
particular, we focus on systems which lead to a reduced problem of parabolic
type in the limit when the scaling parameter tends to zero. Classical Monte
Carlo methods suffer of severe time step limitations in these situations, due
to the fact that the characteristic speeds go to infinity in the diffusion
limit. This makes the problem a real challenge, since the scaling parameter may
differ by several orders of magnitude in the domain. To circumvent these time
step limitations, we construct a new, asymptotic-preserving Monte Carlo method
that is stable independently of the scaling parameter and degenerates to a
standard probabilistic approach for solving the limiting equation in the
diffusion limit. The method uses an implicit time discretization to formulate a
modified equation in which the characteristic speeds do not grow indefinitely
when the scaling factor tends to zero. The resulting modified equation can
readily be discretized by a Monte Carlo scheme, in which the particles combine
a finite propagation speed with a time-step dependent diffusion term. We show
the performance of the method by comparing it with standard (deterministic)
approaches in the literature
A numerical closure approach for kinetic models of polymeric fluids: exploring closure relations for FENE dumbbells
We propose a numerical procedure to study closure approximations for FENE
dumbbells in terms of chosen macroscopic state variables, enabling to test
straightforwardly which macroscopic state variables should be included to build
good closures. The method involves the reconstruction of a polymer distribution
related to the conditional equilibrium of a microscopic Monte Carlo simulation,
conditioned upon the desired macroscopic state. We describe the procedure in
detail, give numerical results for several strategies to define the set of
macroscopic state variables, and show that the resulting closures are related
to those obtained by a so-called quasi-equilibrium approximation
\cite{Ilg:2002p10825}
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