Plasma and Fusion Research

Volume 17, 2403079 (2022)

Regular Articles

A Novel Approach for Data Analysis Based on Visualization of Phase Space Distribution Function in Plasma Turbulence Simulations
Tsubasa SADAKATA1), Shuta KITAZAWA1), Masanori NUNAMI1,2), Takahiro KATAGIRI1), Satoshi OHSHIMA1) and Toru NAGAI1)
1)
Nagoya University, Nagoya 464-8601, Japan
2)
National Institute for Fusion Science, Toki 509-5292, Japan
(Received 26 December 2021 / Accepted 18 April 2022 / Published 22 June 2022)

Abstract

Gyrokinetic simulations are important for analyzing magnetically confined plasmas. However, the data obtained from the gyrokinetic simulations are time-series of a five-dimensional phase space distribution function, making analyzing the transport phenomena extremely difficult because of its high dimensionality and large data size. We propose a novel method for analyzing such phase space distribution functions. First, the two-dimensional velocity space distribution function is mapped into the wavenumber space and visualized as an image. This enables us to easily capture the global features and the features of the individual velocity space distribution functions. Second, we apply similarity analysis based on the local features of images and cluster analysis based on distances between images and the velocity space distribution function. The proposed method enables us to automatically extract similar structures in the velocity space distribution function and quantify the duration of these structures.

Keywords

gyrokinetic simulation, visualization, similarity analysis, cluster analysis

DOI: 10.1585/pfr.17.2403079

Full Text

PDF format (4.1Mbytes) PDF Download

References

  • [1] T. Görler et al., Phys. Plasmas 21, 122307 (2014).
  • [2] T.-H. Watanabe, H. Sugama and S. Ferrando-Margalet, Phys. Rev. Lett. 100, 195002 (2008).
  • [3] R. Szeliski, Computer Vision: Algorithms and Applications (2010).
  • [4] T.-H. Watanabe and H. Sugama, Nucl. Fusion 46, 24 (2006).
  • [5] M. Nunami, T.-H. Watanebe, H. Sugama and K. Tanaka, Phys. Plasmas 19, 042504 (2012).
  • [6] Y. Takeiri et al., Nucl. Fusion 57, 102023 (2017).
  • [7] M. Nunami, T.-H.Watanebe and H. Sugama, Phys. Plasmas 20, 092307 (2013).
  • [8] S. Kitazawa, M. Nunami et al., The 81st national convention of IPSJ, 2M-05, Fukuoka, Japan, 2019.
  • [9] P.F. Alcantarilla, J. Nuevo and A. Bartoli, Fast explicit diffusion for accelerated features in nonlinear scale spaces, British Machine Vision Conference (BMVC), 09 2013.
  • [10] Opencv official web site, https://opencv.org/
  • [11] J. MacQueen, Some methods for classification and analysis of multivariate observations (Berkeley: University of California Press, 1967) p.281-297.
  • [12] A. Jain, Data clustering: 50 years beyond k-means, Pattern Recognition Letters 31, no. 8, 651 (June 2010).