Compressing the time series of five dimensional distribution function data from gyrokinetic simulation using principal component analysis
Compressing the time series of five dimensional distribution function data from gyrokinetic simulation using principal component analysis
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DOI：
10.1063/5.0023166
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发表时间：
2021-01
影响因子：
2.2
通讯作者：
Y. Asahi;K. Fujii;D. M. Heim;S. Maeyama;X. Garbet;V. Grandgirard;Y. Sarazin;G. Dif-Pradalier
中科院分区：
文献类型：
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作者：
Y. Asahi;K. Fujii;D. M. Heim;S. Maeyama;X. Garbet;V. Grandgirard;Y. Sarazin;G. Dif-Pradalier
相空间结构提取的时间序列的五维分布函数的数据计算的磁通驱动的全f gyrokinetic代码GT 5D。主成分分析（PCA）被用来降低数据的维数和大小。通过PCA构造（φ，v θ，w）中的相空间基和相应的空间系数（极向截面），其中φ、v θ和w分别表示环向角、平行速度和垂直速度。结果表明，原始五维分布函数的83%的方差可以用64个主成分表示，即，自由度从1.3 × 1012压缩到1.4 × 109。其中一个重要的发现，从每个主成分的能量通量的贡献的详细分析所产生的处理雪崩事件，这被发现主要是由相空间中的相干结构驱动，表明共振粒子的关键作用。所提出的分析的另一个优点是将6D（1D时间和5D相空间）数据解耦成人眼可见的3D数据的组合。
Phase space structures are extracted from the time series of five dimensional distribution function data computed by the flux-driven full-f gyrokinetic code GT5D. Principal component analysis (PCA) is applied to reduce the dimensionality and the size of the data. Phase space bases in ( φ , v ∥ , w ) and the corresponding spatial coefficients (poloidal cross section) are constructed by PCA, where φ , v ∥, and w, respectively, mean the toroidal angle, the parallel velocity, and the perpendicular velocity. It is shown that 83% of the variance of the original five dimensional distribution function can be expressed with 64 principal components, i.e., the compression of the degrees of freedom from 1.3 × 1 0 12 to 1.4 × 10 9. 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. Another advantage of the proposed analysis is the decoupling of 6D (1D time and 5D phase space) data into the combinations of 3D data which are visible to the human eye.