Plasma and Fusion Research

Volume 20, 1203035 (2025)

Rapid Communications

Estimation of Electrostatic Potential Fluctuations in Kelvin-Helmholtz Turbulence in Linear Plasmas using Multi-Scale CNN
Shuta HOSHINO1), Makoto SASAKI1), Ryohtaroh T. ISHIKAWA2,3), Motoki NAKATA4)
1)
College of Industrial Technology, Nihon University, Narashino 275-8575, Japan
2)
National Institute for Fusion Science, National Institutes of Natural Sciences, Toki 509-5292, Japan
3)
Graduate Institute for Advanced Studies, The Graduate University for Advanced Studies, SOKENDAI, Toki 509-5292, Japan
4)
Faculty of Arts and Sciences, Komazawa University, Setagaya-ku, Tokyo 154-8525, Japan
(Received 14 February 2025 / Accepted 8 May 2025 / Published 25 June 2025)

Abstract

We demonstrate the estimation of electrostatic potential fluctuations in dynamically varying Kelvin-Helmholtz turbulence using multi-scale convolutional neural network. The turbulence field is obtained from simulations based on a reduced fluid model in cylindrical magnetized plasmas. The target turbulence shows limit-cycle oscillations, and coherent and spiral structures are generated and annihilated repeatedly. High accuracy of the prediction is realized for the electrostatic potential field, and the estimation of the particle flux calculated from the predicted potential agrees with the answer with 98.4% accuracy. Behavior of the prediction accuracy is also discussed by changing the hyper parameters, such as the number of filters and the size of the training data.

Keywords

deep learning, plasma turbulence, limit-cycle oscillations, velocity field, Kelvin-Helmholtz turbulence

DOI: 10.1585/pfr.20.1203035

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