Compressing the time series of five dimensional distribution function data from gyrokinetic simulation using principal component analysis

主成分分析を用いた5次元分布関数時系列データの圧縮

朝比 祐一   ; 藤井 恵介*; Heim, D. M.*; 前山 伸也*; Garbet, X.*; Grandgirard, V.*; Sarazin, Y.*; Dif-Pradalier, G.*; 井戸村 泰宏   ; 矢木 雅敏*

Asahi, Yuichi; Fujii, Keisuke*; Heim, D. M.*; Maeyama, Shinya*; Garbet, X.*; Grandgirard, V.*; Sarazin, Y.*; Dif-Pradalier, G.*; Idomura, Yasuhiro; Yagi, Masatoshi*

プラズマ乱流の運動論的シミュレーションによって得られた5次元分布関数の時系列データに主成分分析を適用した。これにより、3桁におよぶデータ圧縮を実現しつつ、83%の累積寄与率を保持できた。各主成分ごとの熱輸送への寄与を調べることで、雪崩的熱輸送には速度空間の共鳴構造が関連していることが明らかとなった。

This article demonstrates a data compression technique for the time series of five dimensional distribution function data based on Principal Component Analysis (PCA). Phase space bases and corresponding coefficients are constructed by PCA in order to reduce the data size and the dimensionality. It is shown that about 83% of the variance of the original five dimensional distribution can be expressed with 64 components. This leads to the compression of the degrees of freedom from to . One of the important findings - resulting from the detailed analysis of the contribution of each principal component to the energy flux - deals with avalanche events, which are found to be mostly driven by coherent structures in the phase space, indicating the key role of resonant particles.