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Register a new userPhoton-Plasma: a modern high-order particle-in-cell code
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Added by
Troels Haugb{\\o}lle
Publication date
2012
fields
Physics
and research's language is
English
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We present the Photon-Plasma code, a modern high order charge conserving particle-in-cell code for simulating relativistic plasmas. The code is using a high order implicit field solver and a novel high order charge conserving interpolation scheme for particle-to-cell interpolation and charge deposition. It includes powerful diagnostics tools with on-the-fly particle tracking, synthetic spectra integration, 2D volume slicing, and a new method to correctly account for radiative cooling in the simulations. A robust technique for imposing (time-dependent) particle and field fluxes on the boundaries is also presented. Using a hybrid OpenMP and MPI approach the code scales efficiently from 8 to more than 250.000 cores with almost linear weak scaling on a range of architectures. The code is tested with the classical benchmarks particle heating, cold beam instability, and two-stream instability. We also present particle-in-cell simulations of the Kelvin-Helmholtz instability, and new results on radiative collisionless shocks.
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We describe Pegasus, a new hybrid-kinetic particle-in-cell code tailored for the study of astrophysical plasma dynamics. The code incorporates an energy-conserving particle integrator into a stable, second-order--accurate, three-stage predictor-predictor-corrector integration algorithm. The constrained transport method is used to enforce the divergence-free constraint on the magnetic field. A delta-f scheme is included to facilitate a reduced-noise study of systems in which only small departures from an initial distribution function are anticipated. The effects of rotation and shear are implemented through the shearing-sheet formalism with orbital advection. These algorithms are embedded within an architecture similar to that used in the popular astrophysical magnetohydrodynamics code Athena, one that is modular, well-documented, easy to use, and efficiently parallelized for use on thousands of processors. We present a series of tests in one, two, and three spatial dimensions that demonstrate the fidelity and versatility of the code.
We design and develop a new Particle-in-Cell (PIC) method for plasma simulations using Deep-Learning (DL) to calculate the electric field from the electron phase space. We train a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) to solve the two-stream instability test. We verify that the DL-based MLP PIC method produces the correct results using the two-stream instability: the DL-based PIC provides the expected growth rate of the two-stream instability. The DL-based PIC does not conserve the total energy and momentum. However, the DL-based PIC method is stable against the cold-beam instability, affecting traditional PIC methods. This work shows that integrating DL technologies into traditional computational methods is a viable approach for developing next-generation PIC algorithms.
SMILEI is a collaborative, open-source, object-oriented (C++) particle-in-cell code. To benefit from the latest advances in high-performance computing (HPC), SMILEI is co-developed by both physicists and HPC experts. The codes structures, capabilities, parallelization strategy and performances are discussed. Additional modules (e.g. to treat ionization or collisions), benchmarks and physics highlights are also presented. Multi-purpose and evolutive, SMILEI is applied today to a wide range of physics studies, from relativistic laser-plasma interaction to astrophysical plasmas.
The process of particle acceleration by left-hand, circularly polarised inertial Alfven waves (IAW) in a transversely inhomogeneous plasma is studied using 3D particle-in-cell simulation. A cylindrical tube with, transverse to the background magnetic field, inhomogeneity scale of the order of ion inertial length is considered on which IAWs with frequency $0.3 omega_{ci}$ are launched that are allowed to develop three wavelength. As a result time-varying parallel electric fields are generated in the density gradient regions which accelerate electrons in the parallel to magnetic field direction. Driven perpendicular electric field of IAWs also heats ions in the transverse direction. Such numerical setup is relevant for solar flaring loops and earth auroral zone. This first, 3D, fully-kinetic simulation demonstrates electron acceleration efficiency in the density inhomogeneity regions, along the magnetic field, of the order of 45% and ion heating, in the transverse to the magnetic field direction, of 75%. The latter is a factor of two times higher than the previous 2.5D analogous study and is in accordance with solar flare particle acceleration observations. We find that the generated parallel electric field is localised in the density inhomogeneity region and rotates in the same direction and with the same angular frequency as the initially launched IAW. Our numerical simulations seem also to suggest that the knee often found in the solar flare electron spectra can alternatively be interpreted as the Landau damping (Cerenkov resonance effect) of IAWs due to the wave-particle interactions.
When a charged particle moves through a plasma at a speed much higher than the thermal velocity of the plasma, it is subjected to the force of the electrostatic field induced in the plasma by itself and loses its energy. This process is well-known as the stopping power of a plasma. In this paper we show that the same process works in particle-in-cell (PIC) simulations as well and the energy loss rate of fast particles due to this process is mainly determined by the number of plasma electrons contained in the electron skin depth volume. However, since there are generally very few particles in that volume in PIC simulations compared with real plasmas, the energy loss effect can be exaggerated significantly and can affect the results. Therefore, especially for the simulations that investigate the particle acceleration processes, the number of particles used in the simulations should be chosen large enough to avoid this artificial energy loss.
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