| Issue |
EPJ Web Conf.
Volume 346, 2026
25th Topical Conference on Radio-Frequency Power in Plasmas (RFPPC2025)
|
|
|---|---|---|
| Article Number | 01021 | |
| Number of page(s) | 9 | |
| Section | Theory and Modeling of Radio-Frequency Waves in Plasmas | |
| DOI | https://doi.org/10.1051/epjconf/202634601021 | |
| Published online | 07 January 2026 | |
https://doi.org/10.1051/epjconf/202634601021
Impact of edge/SOL density fluctuations on ion cyclotron wave propagation and absorption in tokamaks: A stochastic 1D model in a DTT plasma scenario
1 Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Sud (INFN-LNS), Catania, Italy
2 Dipartimento di Fisica e Astronomia, Università degli Studi di Catania, Catania, Italy
3 Centro Ricerche Fusione, Università degli Studi di Padova, Padova, Italy
4 Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching, Germany
* Corresponding author: francalanza@lns.infn.it
** Corresponding author: cardinali@lns.infn.it
Published online: 7 January 2026
This work introduces a novel 1D hybrid modeling approach to investigate how density fluctuations localized in the edge and Scrape-Off Layer (SOL) regions of tokamaks affect ion-cyclotron wave propagation and absorption in a relevant Divertor Tokamak Test (DTT) plasma scenario. The wave-power transmission through the edge is obtained using the Invariant Imbedding Method in the fluctuating layer, while the absorbed power fractions in the smooth core are evaluated with finite-element and Wentzel–Kramers–Brillouin based approaches. Time-independent and spatially correlated density fluctuations are generated analytically via a correlation-matrix method with prescribed root-mean-square amplitude and correlation length, and are superposed on an analytical equilibrium profile. A Monte Carlo approach is then used to sample many realizations and perform statistical analysis. The resulting edge power-transmission distribution exhibits a clearly multimodal structure, with peaks at high, low, and intermediate transmission levels. The model is fully implemented in Python and is computationally lightweight, making it suitable for fast exploratory studies without the need for high-performance computing resources, which are typical of high-fidelity modeling.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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