Accelerating the estimation of collisionless energetic particle confinement statistics in stellarators using multifidelity Monte Carlo

Frederick Law, Antoine Cerfon, Benjamin Peherstorfer

Research output: Contribution to journalArticlepeer-review

Abstract

In the design of stellarators, energetic particle confinement is a critical point of concern which remains challenging to study from a numerical point of view. Standard Monte Carlo (MC) analyses are highly expensive because a large number of particle trajectories need to be integrated over long time scales, and small time steps must be taken to accurately capture the features of the wide variety of trajectories. Even when they are based on guiding center trajectories, as opposed to full-orbit trajectories, these standard MC studies are too expensive to be included in most stellarator optimization codes. We present the first multifidelity Monte Carlo (MFMC) scheme for accelerating the estimation of energetic particle confinement in stellarators. Our approach relies on a two-level hierarchy, in which a guiding center model serves as the high-fidelity model, and a data-driven linear interpolant is leveraged as the low-fidelity surrogate model. We apply MFMC to the study of energetic particle confinement in a four-period quasi-helically symmetric stellarator, assessing various metrics of confinement. Stemming from the very high computational efficiency of our surrogate model as well as its sufficient correlation to the high-fidelity model, we obtain speedups of up to 10 with MFMC compared to standard MC.

Original languageEnglish (US)
Article number076019
JournalNuclear Fusion
Volume62
Issue number7
DOIs
StatePublished - Jul 2022

Keywords

  • energetic particle confinement
  • Monte Carlo
  • stellarator
  • variance reduction

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Condensed Matter Physics

Fingerprint

Dive into the research topics of 'Accelerating the estimation of collisionless energetic particle confinement statistics in stellarators using multifidelity Monte Carlo'. Together they form a unique fingerprint.

Cite this