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Abstract Details

April 27-29

Abstract Details

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Abstracts

Author: Patrick S Kim
Requested Type: Poster
Submitted: 2026-03-20 08:10:06

Co-authors: A. Geraldini, A.C.D. Hoffman, S. Ku, F.I. Parra

Contact Info:
Princeton University
4 Ivy Ln
Princeton, NJ   08540
USA

Abstract Text:
To minimize heat fluxes onto the walls of fusion reactors, the magnetic field is inclined at a shallow angle relative to the wall. Due to their lighter mass, electrons typically reach the wall first, setting up an electron-repelling Debye sheath and magnetic presheath. In recent years, gyrokinetic models for shallow-angle incidence [1], [2], [3] have been developed to self-consistently calculate the electrostatic potential in the Debye sheath and the magnetic presheath. These models are fast and run for about a minute on a single processor. While these models can take any ion or electron distribution function at the magnetic presheath entrance as input, until now they have only been tested using analytical distribution functions. We present results using numerical distribution functions from the XGC [4], [5] and Gkeyll [6], [7] gyrokinetic codes. In some cases, the numerical distribution functions can give results that are significantly different from those obtained with analytical distribution functions. Additionally, it has been observed that for both analytical and numerical distribution functions, these models do not converge to any solution below certain critical angles. We believe that the models fail to converge because they assume a monotonic potential profiles within the sheath, and the potential is not monotonic below the critical angle. To characterize the failure to obtain monotonic potential profiles, we construct a matched asymptotic solution of the sheath potential and study small deviations to the potential profile at the critical angle.

[1] Geraldini et al., PPCF 2018
[2] Ewart et al., PPCF 2021
[3] Geraldini et al., 2025
[4] S. Ku et al., PoP, 2018
[5] Hager et al., PoP, 2022
[6] Shi et al., PoP, 2019
[7] Hoffmann et al., NF, 2026

Characterization: 2.0

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