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

Asahi, Yuichi   ; Fujii, Keisuke*; Dennis, H.*; Maeyama, Shinya*; Idomura, Yasuhiro   

Phase space structures are extracted from the time series of five dimensional distribution function data computed by the flux-driven full-fgyrokinetic code GT5D. Principal component analysis (PCA) is applied to reduce the dimensionality and the size of the data. Phase space bases and the corresponding spatial coefficients (poloidal cross section) are constructed by PCA. It is shown that 83% of the variance of the original five-dimensional distribution can be expressed with 64 principal components, i.e., the compression of the degrees of freedom from 10 to . The relationship between avalanche-like transport phenomena and phase space structure is discussed based on the contribution of each principal component to the heat transport.