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JOINPoster Sessions
2024 KPS Spring Meeting
Tuesday-Friday, October 18-21, 2022; 부산 BEXCO
Session P2-pl: Nuclear fusion; Basic plasmas; Plasma applications
5:00 PM-6:30 PM, Thursday, October 20, 2022
Room: Grand Ballroom
Chair: 나용수, 서울대학교
Abstract: P2-pl27* : Unsupervised machine-learning algorithms for Orbit classification of trajectories under magnetic island
Presenter:
YUM Sungpil
(Department of Nuclear Engineering, UNIST)
Author:
YUM Sungpil 1, YOON Eisung *1
(1Department of Nuclear Engineering, UNIST)
The tearing mode in fusion reactor is one of the instabilities which tears and reconnects magnetic field lines forming a topological object, so-called ‘Magnetic Island’ on the current sheet where the magnetic field lines of opposing directions are close to each other[1]. In the magnetic island structure, particles, such as electrons, are expected to exhibit different trajectories with those in the absence of the magnetic island structure. In this work, the differences between particles’ trajectories in the presence / absence of magnetic island were simulated in Tokamak geometry by a passive particle code, named Particle Around Magnetic Island(PAMI), which has been developed on C++ and parallelized with Message Passing Interface(MPI). The simulation results calculated by PAMI were classified using unsupervised machine-learning(ML) algorithms, such as Self-Organizing Map(SOM)[2], Hierarchical Cluster Analysis(HCA)[3], and K-mean clustering.
In order to scan various particle trajectories near magnetic island structures, the code PAMI has been executed with inputs sampled based on initial speed, pitch-angle, and initial position of particles. The simulation results, which are stored in 3D positions on cylindrical coordinates of each time step, were transformed into parameters by Fourier Transforms, Chebyshev Polynomial, and Least Square Polynomials, in order to be dealt with by the ML algorithms for clustering.
This work was supported by the National Research Foundation of Korea(NRF) funded by the Korea government. (Ministry of Science and ICT) (RS-2022-00155991)
References
[1] H. Zohm, 2015, "Magnetohydrodynamic Stability of Tokamaks", Wiley.
[2] T. Kohonen, 1990, "The self-organizing map" in Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, Sept. 1990, doi: 10.1109/5.58325.
[3] Joe H. Ward Jr., 1963, "Hierarchical Grouping to Optimize an Objective Function", Journal of the American Statistical Association, Volume 58, 1963.
In order to scan various particle trajectories near magnetic island structures, the code PAMI has been executed with inputs sampled based on initial speed, pitch-angle, and initial position of particles. The simulation results, which are stored in 3D positions on cylindrical coordinates of each time step, were transformed into parameters by Fourier Transforms, Chebyshev Polynomial, and Least Square Polynomials, in order to be dealt with by the ML algorithms for clustering.
This work was supported by the National Research Foundation of Korea(NRF) funded by the Korea government. (Ministry of Science and ICT) (RS-2022-00155991)
References
[1] H. Zohm, 2015, "Magnetohydrodynamic Stability of Tokamaks", Wiley.
[2] T. Kohonen, 1990, "The self-organizing map" in Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, Sept. 1990, doi: 10.1109/5.58325.
[3] Joe H. Ward Jr., 1963, "Hierarchical Grouping to Optimize an Objective Function", Journal of the American Statistical Association, Volume 58, 1963.
Keyword:
Fusion Plasma, Particle Trajectory, Orbit classification, Unsupervised machine-learning, magnetic island