15 research outputs found
Analysis of an asymptotic preserving scheme for linear kinetic equations in the diffusion limit
We present a mathematical analysis of the asymptotic preserving scheme
proposed in [M. Lemou and L. Mieussens, SIAM J. Sci. Comput., 31, pp. 334-368,
2008] for linear transport equations in kinetic and diffusive regimes. We prove
that the scheme is uniformly stable and accurate with respect to the mean free
path of the particles. This property is satisfied under an explicitly given CFL
condition. This condition tends to a parabolic CFL condition for small mean
free paths, and is close to a convection CFL condition for large mean free
paths. Ou r analysis is based on very simple energy estimates
Asymptotic-preserving projective integration schemes for kinetic equations in the diffusion limit
We investigate a projective integration scheme for a kinetic equation in the
limit of vanishing mean free path, in which the kinetic description approaches
a diffusion phenomenon. The scheme first takes a few small steps with a simple,
explicit method, such as a spatial centered flux/forward Euler time
integration, and subsequently projects the results forward in time over a large
time step on the diffusion time scale. We show that, with an appropriate choice
of the inner step size, the time-step restriction on the outer time step is
similar to the stability condition for the diffusion equation, whereas the
required number of inner steps does not depend on the mean free path. We also
provide a consistency result. The presented method is asymptotic-preserving, in
the sense that the method converges to a standard finite volume scheme for the
diffusion equation in the limit of vanishing mean free path. The analysis is
illustrated with numerical results, and we present an application to the
Su-Olson test
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
Two-Scale Macro-Micro decomposition of the Vlasov equation with a strong magnetic field
In this paper, we build a Two-Scale Macro-Micro decomposition of the Vlasov
equation with a strong magnetic field. This consists in writing the solution of
this equation as a sum of two oscillating functions with circonscribed
oscillations. The first of these functions has a shape which is close to the
shape of the Two-Scale limit of the solution and the second one is a correction
built to offset this imposed shape. The aim of such a decomposition is to be
the starting point for the construction of Two-Scale Asymptotic-Preserving
Schemes.Comment: Mathematical Models and Methods in Applied Sciences 00, 00 (2012) 1
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Implicit-Explicit Runge-Kutta schemes for hyperbolic systems and kinetic equations in the diffusion limit
We consider Implicit-Explicit (IMEX) Runge-Kutta (R-K) schemes for hyperbolic
systems with stiff relaxation in the so-called diffusion limit. In such regime
the system relaxes towards a convection-diffusion equation. The first objective
of the paper is to show that traditional partitioned IMEX R-K schemes will
relax to an explicit scheme for the limit equation with no need of modification
of the original system. Of course the explicit scheme obtained in the limit
suffers from the classical parabolic stability restriction on the time step.
The main goal of the paper is to present an approach, based on IMEX R-K
schemes, that in the diffusion limit relaxes to an IMEX R-K scheme for the
convection-diffusion equation, in which the diffusion is treated implicitly.
This is achieved by an original reformulation of the problem, and subsequent
application of IMEX R-K schemes to it. An analysis on such schemes to the
reformulated problem shows that the schemes reduce to IMEX R-K schemes for the
limit equation, under the same conditions derived for hyperbolic relaxation.
Several numerical examples including neutron transport equations confirm the
theoretical analysis
A Multilevel Monte Carlo Asymptotic-Preserving Particle Method for Kinetic Equations in the Diffusion Limit
We propose a multilevel Monte Carlo method for a particle-based
asymptotic-preserving scheme for kinetic equations. Kinetic equations model
transport and collision of particles in a position-velocity phase-space. With a
diffusive scaling, the kinetic equation converges to an advection-diffusion
equation in the limit of zero mean free path. Classical particle-based
techniques suffer from a strict time-step restriction to maintain stability in
this limit. Asymptotic-preserving schemes provide a solution to this time step
restriction, but introduce a first-order error in the time step size. We
demonstrate how the multilevel Monte Carlo method can be used as a bias
reduction technique to perform accurate simulations in the diffusive regime,
while leveraging the reduced simulation cost given by the asymptotic-preserving
scheme. We describe how to achieve the necessary correlation between simulation
paths at different levels and demonstrate the potential of the approach via
numerical experiments.Comment: 20 pages, 7 figures, published in Monte Carlo and Quasi-Monte Carlo
Methods 2018, correction of minor typographical error
Asymptotically complexity diminishing schemes (ACDS) for kinetic equations in the diffusive scaling
In this work, we develop a new class of numerical schemes for collisional kinetic equations in the diffusive regime. The first step consists in reformulating the problem by decomposing the solution in the time evolution of an equilibrium state plus a perturbation. Then, the scheme combines a Monte Carlo solver for the perturbation with an Eulerian method for the equilibrium part, and is designed in such a way to be uniformly stable with respect to the diffusive scaling and to be consistent with the asymptotic diffusion equation. Moreover, since particles are only used to describe the perturbation part of the solution, the scheme becomes computationally less expensive – and is thus an asymptotically complexity diminishing scheme (ACDS) – as the solution approaches the equilibrium state due to the fact that the number of particles diminishes accordingly. This contrasts with standard methods for kinetic equations where the computational cost increases (or at least does not decrease) with the number of interactions. At the same time, the statistical error due to the Monte Carlo part of the solution decreases as the system approaches the equilibrium state: the method automatically degenerates to a solution of the macroscopic diffusion equation in the limit of infinite number of interactions. After a detailed description of the method, we perform several numerical tests and compare this new approach with classical numerical methods on various problems up to the full three dimensional case