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

Abstract

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 1.3×1012 to 1.4×109. 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.