A statistical analysis approach of plasma dynamics in gyrokinetic simulations of stellarator turbulence

TL;DR

This paper introduces a geometric and statistical framework for analyzing plasma turbulence in gyrokinetic simulations of stellarators, focusing on stochastic processes, avalanche detection, and physical property extraction.

Contribution

It presents a novel geometric methodology using thermodynamic length and a combined SSA and clustering approach for identifying physical signals in turbulence data.

Findings

Effective detection of avalanche events in simulation data

Quantitative characterization of plasma turbulence properties

Application of Riemannian metrics to plasma phase space analysis

Abstract

A geometrical method is used for the analysis of stochastic processes in plasma turbulence. Distances between thermodynamic states can be computed according the thermodynamic length methodology which allows the use of a Riemannian metric on the phase space. A geometric methodology is suitable in order to understand stochastic processes involved in e.g. order-disorder transition, where a sudden increase in distance is expected. Gyrokinetic simulations of Ion-Temperature-Gradient (ITG) mode driven turbulence in the core-region of the stellarator W7-X, with realistic quasi-isodynamic topologies are considered. In gyrokinetic plasma turbulence simulations avalanches, e.g. of heat and particles, are often found and in this work a novel method for detection is investigated. This new method combines the Singular Spectrum Analysis algorithm and Hierarchical Clustering such that the gyrokinetic simulation time series is decomposed into a part of useful physical information and noise. The informative component of the time series is used for the calculation of the Hurst exponent, the Information Length and the Dynamic Time. Based on these measures the physical properties of the time series is revealed.