27,857 research outputs found
A Multi Level Multi Domain Method for Particle In Cell Plasma Simulations
A novel adaptive technique for electromagnetic Particle In Cell (PIC) plasma
simulations is presented here. Two main issues are identified in designing
adaptive techniques for PIC simulation: first, the choice of the size of the
particle shape function in progressively refined grids, with the need to avoid
the exertion of self-forces on particles, and, second, the necessity to comply
with the strict stability constraints of the explicit PIC algorithm. The
adaptive implementation presented responds to these demands with the
introduction of a Multi Level Multi Domain (MLMD) system (where a cloud of
self-similar domains is fully simulated with both fields and particles) and the
use of an Implicit Moment PIC method as baseline algorithm for the adaptive
evolution. Information is exchanged between the levels with the projection of
the field information from the refined to the coarser levels and the
interpolation of the boundary conditions for the refined levels from the
coarser level fields. Particles are bound to their level of origin and are
prevented from transitioning to coarser levels, but are repopulated at the
refined grid boundaries with a splitting technique. The presented algorithm is
tested against a series of simulation challenges
Multiphysics simulations of collisionless plasmas
Collisionless plasmas, mostly present in astrophysical and space
environments, often require a kinetic treatment as given by the Vlasov
equation. Unfortunately, the six-dimensional Vlasov equation can only be solved
on very small parts of the considered spatial domain. However, in some cases,
e.g. magnetic reconnection, it is sufficient to solve the Vlasov equation in a
localized domain and solve the remaining domain by appropriate fluid models. In
this paper, we describe a hierarchical treatment of collisionless plasmas in
the following way. On the finest level of description, the Vlasov equation is
solved both for ions and electrons. The next courser description treats
electrons with a 10-moment fluid model incorporating a simplified treatment of
Landau damping. At the boundary between the electron kinetic and fluid region,
the central question is how the fluid moments influence the electron
distribution function. On the next coarser level of description the ions are
treated by an 10-moment fluid model as well. It may turn out that in some
spatial regions far away from the reconnection zone the temperature tensor in
the 10-moment description is nearly isotopic. In this case it is even possible
to switch to a 5-moment description. This change can be done separately for
ions and electrons. To test this multiphysics approach, we apply this full
physics-adaptive simulations to the Geospace Environmental Modeling (GEM)
challenge of magnetic reconnection.Comment: 13 pages, 5 figure
Afterlive: A performant code for Vlasov-Hybrid simulations
A parallelized implementation of the Vlasov-Hybrid method [Nunn, 1993] is
presented. This method is a hybrid between a gridded Eulerian description and
Lagrangian meta-particles. Unlike the Particle-in-Cell method [Dawson, 1983]
which simply adds up the contribution of meta-particles, this method does a
reconstruction of the distribution function in every time step for each
species. This interpolation method combines meta-particles with different
weights in such a way that particles with large weight do not drown out
particles that represent small contributions to the phase space density. These
core properties allow the use of a much larger range of macro factors and can
thus represent a much larger dynamic range in phase space density.
The reconstructed phase space density is used to calculate momenta of the
distribution function such as the charge density . The charge density
is also used as input into a spectral solver that calculates the
self-consistent electrostatic field which is used to update the particles for
the next time-step.
Afterlive (A Fourier-based Tool in the Electrostatic limit for the Rapid
Low-noise Integration of the Vlasov Equation) is fully parallelized using MPI
and writes output using parallel HDF5. The input to the simulation is read from
a JSON description that sets the initial particle distributions as well as
domain size and discretization constraints. The implementation presented here
is intentionally limited to one spatial dimension and resolves one or three
dimensions in velocity space. Additional spatial dimensions can be added in a
straight forward way, but make runs computationally even more costly.Comment: Accepted for publication in Computer Physics Communication
Kinetic Solvers with Adaptive Mesh in Phase Space
An Adaptive Mesh in Phase Space (AMPS) methodology has been developed for
solving multi-dimensional kinetic equations by the discrete velocity method. A
Cartesian mesh for both configuration (r) and velocity (v) spaces is produced
using a tree of trees data structure. The mesh in r-space is automatically
generated around embedded boundaries and dynamically adapted to local solution
properties. The mesh in v-space is created on-the-fly for each cell in r-space.
Mappings between neighboring v-space trees implemented for the advection
operator in configuration space. We have developed new algorithms for solving
the full Boltzmann and linear Boltzmann equations with AMPS. Several recent
innovations were used to calculate the discrete Boltzmann collision integral
with dynamically adaptive mesh in velocity space: importance sampling,
multi-point projection method, and the variance reduction method. We have
developed an efficient algorithm for calculating the linear Boltzmann collision
integral for elastic and inelastic collisions in a Lorentz gas. New AMPS
technique has been demonstrated for simulations of hypersonic rarefied gas
flows, ion and electron kinetics in weakly ionized plasma, radiation and light
particle transport through thin films, and electron streaming in
semiconductors. We have shown that AMPS allows minimizing the number of cells
in phase space to reduce computational cost and memory usage for solving
challenging kinetic problems
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