SearcharxivSearch

arXiv · 2609.21951

Azimuthal mode decomposition Particle in Cell algorithm for cylindrical plasma sources

Abstract

An efficient Particle-in-Cell numerical approach to perform full-dimensional kinetic simulations of low temperature plasmas is presented. Taking advantage of the cylindrical geometry of most plasma sources, a Fourier mode decomposition of the fields is carried out in the azimuthal ($θ$) direction up to a chosen maximum number of modes $N_m$. Macroparticles are pushed in all $D$ dimensions and weighed, for each mode $m$, onto a $(D-1)$ dimensional grid. The computation of the electric field for each mode is independent and reduces to solving $(N_m+1)$ $(D-1)$ dimensional Poisson problems. The approach brings spectral accuracy in the azimuthal direction, while the computational cost is comparable to that of a simulation with $(D-1)$ dimensions. We verify this approach against a planar test case based on a Penning discharge, widely used for benchmarking and validation purposes in the low-temperature plasma community. Our approach allows us to reduce the 2D problem into a collection of coupled 1D problems and to naturally perform spectral analysis of the different azimuthal modes, recovering the contribution of each mode to radial transport, with a computational time saving of one order of magnitude with respect to state of the art 2D particle-in-cell codes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matteo Ripoli, Eduardo Ahedo, Mario Merino. 2026-09-18. Azimuthal mode decomposition Particle in Cell algorithm for cylindrical plasma sources. https://arxiv.org/abs/2609.21951

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Runaway electron interactions with whistler waves in tokamak plasmas: energy-dependent transport scaling

Resonant interactions between high energy runaway electrons (REs) and whistler waves are a promising mechanism for RE mitigation in tokamak plasmas. While prior studies have largely relied on quasi-linear diffusion models in simplified geometries, we present a first-principles-informed framework that models RE-whistler interactions in a 3D tokamak equilibrium. This is achieved by coupling AORSA, which computes whistler eigenmodes for a given tokamak plasma equilibrium, and KORC, a kinetic orbit code that tracks full orbit RE trajectories in prescribed wave fields. Our results demonstrate that REs undergo scattering to large pitch angles and exhibit anomalous diffusion in both pitch-angle and kinetic energy space. Crucially, we observe a transition between diffusive, sub-diffusive, and super-diffusive transport regimes as a function of initial RE energy - an effect not captured by existing quasi-linear models. This anomalous transport behavior represents a significant advancement in understanding RE dynamics in the presence of wave - particle interactions. By identifying the conditions under which anomalous diffusion arises, this work lays the theoretical foundation for designing targeted, wave-based mitigation strategies in future tokamak experiments.

physics.plasm-ph

Geodesic Acoustic Modes in pair plasmas confined in tokamak magnetic fields

This paper is devoted to the derivation of the dispersion relation of the Geodesic Acoustic Mode in pair plasmas, i.e. assuming that ions and electrons have the same mass. Geodesic Acoustic Modes are plasma perturbations playing a crucial role in turbulence regulation, and therefore in the determination of the plasma confinement in tokamaks. Experiments with pair plasmas, like electron-positron plasmas, have been proposed with different kinds of confinements, and aim to study fundamental processes in plasma physics and understanding the formation of the early universe.

physics.plasm-ph

Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms

Reliable disruption prediction is essential for the safe operation of future tokamaks. Existing full-distribution survival methods model the complete residual time-to-disruption distribution, whereas operational decisions primarily depend on disruption risk within a finite prediction horizon. This mismatch motivates introducing Early Event Prediction (EEP) objectives into survival-based disruption prediction. We take Deep Survival Machines (DSM) as the full-distribution baseline and propose applying two established EEP methods to tokamak disruption prediction: Temporal Label Smoothing (TLS), which directly predicts disruption probability within a finite horizon, and survTLS, which additionally models the event-time distribution within that horizon. Using a common causal encoder, we compare these methods on DIII-D, Alcator C-Mod, and EAST. We distinguish threshold-free deadline ranking from validation-selected fixed-policy alarm performance and evaluate prediction horizons and encoder architectures. TLS achieves the best mean alarm performance on DIII-D and EAST, whereas all methods perform poorly on Alcator C-Mod. survTLS does not consistently outperform DSM, suggesting that directly learning horizon-level event probability is more effective than modeling detailed within-horizon event-time distributions in the present setting. Finally, the selected prediction horizons and encoder-ablation results vary across devices, reflecting differences in disruption characteristics.

physics.plasm-ph