Deep electric field predictions by drift-reduced Braginskii theory with plasma-neutral interactions based upon experimental images of boundary turbulence

TL;DR

This paper introduces a physics-informed deep learning method to predict turbulent electric fields in fusion plasmas, integrating Braginskii theory with experimental imaging data, highlighting the influence of neutrals on turbulence.

Contribution

It develops a novel deep learning framework that combines plasma physics models with experimental data to accurately predict electric fields in turbulent fusion plasmas.

Findings

Neutrals increase turbulence amplitude distribution.

Electric field correlations with electron pressure are strengthened.

Neutral effects enhance E×B shearing rates.

Abstract

We present 2-dimensional turbulent electric field calculations via physics-informed deep learning consistent with (i) drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field and (ii) experimental estimates of the fluctuating electron density and temperature on open field lines obtained from analysis of gas puff imaging of a discharge on the Alcator C-Mod tokamak. The inclusion of effects from the locally puffed atomic helium on particle and energy sources within the reduced plasma turbulence model are found to strengthen correlations between the electric field and electron pressure. The neutrals are also directly associated with broadening the distribution of turbulent field amplitudes and increasing ${\bf E \times B}$ shearing rates. This demonstrates a novel approach in plasma experiments by solving for nonlinear dynamics consistent with partial differential equations and data without encoding explicit boundary nor initial conditions.