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Abstract Details

April 27-29

Abstract Details

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Abstracts

Author: Vasilii Khavin
Requested Type: Poster
Submitted: 2026-03-19 23:18:00

Co-authors: A.V. Dudkovskaia, D.M. Orlov

Contact Info:
University of California, San Diego
9500 Gilman Drive
La Jolla,   92093
US

Abstract Text:
In tokamak plasmas, any magnetic perturbation that drives the formation of a magnetic island also produces a thin stochastic layer in the vicinity of the island separatrix. For an isolated resonance, this layer is exponentially narrow, and the resulting chaotic motion remains confined. As a consequence of the presence of invariant curves in the layer, field-line trajectories exhibit limited radial transport and cannot diffuse far from their initial positions.

In realistic conditions, however, externally applied perturbations contain modes that can open islands on neighboring magnetic surfaces near the main island separatrix. Their overlap, according to a crude Chirikov resonance overlap criterion [2], leads to the formation of a stochastic layer of finite width.

To account for the physics described above without setting any restrictions on plasma beta (ratio of thermal to magnetic field pressure) and the associated magnetic field geometry, the 5-dimensional generalized drift kinetic theory developed in [3] has been extended by introducing a sequence of small Gaussian-shaped perturbations localized to the island separatrix. To analyze the impact of Gaussian-shaped perturbations on the corresponding drift kinetic solutions, the RDK code of [3] is extended accordingly. The work is currently ongoing. The proposed framework also enables the investigation of radially localized modes in the vicinity of rational surfaces, extending the ideas originally presented in [1].


[1] A. D. Beklemishev and W. Horton. Physics of Fluids B: Plasma Physics, 4(1):200–206, 1992.
[2] B. V. Chirikov. J. Nucl. Energy, Part C: Plasma Phys., 1:253, 1960.
[3] A. V. Dudkovskaia, J. W. Connor, D. Dickinson, P. Hill, K. Imada, S. Leigh, and H. R.
Wilson. Nuclear Fusion, 63(12):126040, 2023.

Characterization: 4.0

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