Turbulent field fluctuations in gyrokinetic and fluid plasmas

TL;DR

This paper introduces a physics-informed deep learning framework to compare turbulent field fluctuations in gyrokinetic and fluid plasma models, aiding the validation of reduced turbulence models for magnetic confinement fusion.

Contribution

It presents the first direct quantitative comparison between two-fluid and gyrokinetic turbulence models using deep learning, enhancing model validation capabilities.

Findings

Good overall agreement in magnetized helical plasmas at low normalized pressure

Framework is adaptable to experimental and astrophysical environments

Provides a new technique for validating and discovering plasma turbulence models

Abstract

A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertainty is the validation in accuracy of reduced turbulent transport models. Drift-reduced Braginskii two-fluid theory is one such set of reduced equations that has for decades simulated boundary plasmas in experiment, but significant questions exist regarding its predictive ability. To this end, using a novel physics-informed deep learning framework, we demonstrate the first ever direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling with good overall agreement found in magnetized helical plasmas at low normalized pressure. This framework is readily adaptable to experimental and astrophysical environments, and presents a new technique for the numerical validation and discovery of reduced global plasma turbulence models.