Simulations of Kinetic Electrostatic Electron Nonlinear (KEEN) Waves with Variable Velocity Resolution Grids and High-Order Time-Splitting
Bedros Afeyan, Fernando Casas (IMAC), Nicolas Crouseilles (INRIA -, IRMAR, IRMAR), Adila Dodhy (IPP), Erwan Faou (INRIA - IRMAR, IRMAR), Michel, Mehrenberger (IPP, IRMA, INRIA Nancy - Grand Est / IRMA), Eric, Sonnendr\"ucker (IPP)

TL;DR
This paper introduces a novel semi-Lagrangian Vlasov-Poisson solver with adaptive velocity resolution and high-order time-splitting, enabling efficient simulation of nonlinear KEEN waves across different amplitudes and dimensions.
Contribution
The authors develop a new numerical method combining non-uniform cubic splines and high-order time-splitting to improve KEEN wave simulations, especially for weakly driven cases.
Findings
Enhanced velocity resolution captures weak KEEN wave dynamics.
Longer simulation times are feasible with the new high-order scheme.
Performance improvements over uniform grid methods are demonstrated.
Abstract
KEEN waves are nonlinear, non-stationary, self-organized asymptotic states in Vlasov plasmas outside the scope or purview of linear theory constructs such as electron plasma waves or ion acoustic waves. Nonlinear stationary mode theories such as those leading to BGK modes also do not apply. The range in velocity that is strongly perturbed by KEEN waves depends on the amplitude and duration of the ponderomotive force used to drive them. Smaller amplitude drives create highly localized structures attempting to coalesce into KEEN waves. These cases have much more chaotic and intricate time histories than strongly driven ones. The narrow range in which one must maintain adequate velocity resolution in the weakly driven cases challenges xed grid numerical schemes. What is missing there is the capability of resolving locally in velocity while maintaining a coarse grid outside the highly…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
