Causality detection and turbulence in fusion plasmas

TL;DR

This paper applies an information-theoretical causality detection method to analyze turbulence and interactions in fusion plasmas, revealing new insights into plasma behavior relevant for fusion reactor design.

Contribution

It introduces and tests a causality detection method on fusion plasma data, uncovering interactions missed by traditional techniques.

Findings

Identified causal relationships between turbulence amplitude and Zonal Flow.

Revealed interactions between turbulent flux and confinement transitions.

Method outperforms traditional analysis methods in detecting plasma interactions.

Abstract

This work explores the potential of an information-theoretical causality detection method for unraveling the relation between fluctuating variables in complex nonlinear systems. The method is tested on some simple though nonlinear models, and guidelines for the choice of analysis parameters are established. Then, measurements from magnetically confined fusion plasmas are analyzed. The selected data bear relevance to the all-important spontaneous confinement transitions often observed in fusion plasmas, fundamental for the design of an economically attractive fusion reactor. It is shown how the present method is capable of clarifying the interaction between fluctuating quantities such as the turbulence amplitude, turbulent flux, and Zonal Flow amplitude, and uncovers several interactions that were missed by traditional methods.