Tessellation-based analysis of impurity clustering in the edge plasma of tokamaks

TL;DR

This paper investigates impurity clustering in tokamak edge plasma using high-resolution simulations, revealing how particle inertia influences spatial intermittency and large-scale structures, quantified through tessellation and statistical analysis.

Contribution

It introduces a tessellation-based method to analyze impurity clustering dynamics in plasma, linking particle inertia to spatial distribution patterns.

Findings

Impurity clustering increases with Stokes number.

Large-scale structures form due to particle inertia.

Tessellation and statistical methods effectively quantify clustering.

Abstract

Confinement quality in fusion plasma is significantly influenced by the presence of heavy impurities, which can lead to radiative heat loss and reduced confinement. This study explores the clustering of heavy impurity, \textit{i.e.}, Tungsten in edge plasma, using high-resolution direct numerical simulations of the Hasegawa--Wakatani equations. We use Stokes number to quantify the inertia of impurity particles. It is found that particle inertia will cause spatial intermittency in particle distribution and the formation of large-scale structures, \textit{i.e.}, the clustering of particles. The degrees of clustering are influenced by Stokes number. To quantify these observations, we apply a modified Voronoi tessellation, which assigns specific volumes to impurity particles. By determining time changes of these volumes, we can calculate the impurity velocity divergence, which allows to assess the clustering dynamics. To quantify the clustering statistically, several approaches are applied, such as probability density function (PDF) of impurity velocity divergence and joint PDF of volume and divergence.