Statistical Analysis of Plasma Dynamics in Gyrokinetic Simulations of Stellarator Turbulence

A geometrical method for assessing stochastic processes in plasma turbulence is investigated in this study. The thermodynamic length methodology allows using a Riemannian metric on the phase space; thus, distances between thermodynamic states can be computed. It constitutes a geometric methodology to understand stochastic processes involved in, e.g., order-disorder transitions, where a sudden increase in distance is expected. We consider gyrokinetic simulations of ion-temperature-gradient (ITG)-mode-driven turbulence in the core region of the stellarator W7-X with realistic quasi-isodynamic topologies. 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 with a hierarchical clustering method such that the time series is decomposed into two parts: 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 are revealed.

Automated summary

A geometrical method for assessing stochastic processes in plasma turbulence is investigated, using a Riemannian metric on the phase space to compute distances between thermodynamic states. This method provides a geometric approach to understand stochastic processes, such as order-disorder transitions. The study focuses on gyrokinetic simulations of ion-temperature-gradient (ITG)-mode-driven turbulence in the W7-X stellarator.