A Physics-Informed Deep Learning Model of the Hot Tail Runaway Electron Seed

TL;DR

This paper introduces a physics-informed neural network (PINN) to accurately model the hot tail runaway electron seed during tokamak disruptions, capturing complex physics without relying on experimental data.

Contribution

The work develops a novel PINN approach to solve the relativistic Fokker-Planck equation for hot tail seed prediction during thermal quenches in tokamaks.

Findings

PINN accurately predicts hot tail seed across various parameters

Excellent agreement with Monte Carlo solutions

Applicable without experimental or simulation data

Abstract

A challenging aspect of the description of a tokamak disruption is evaluating the hot tail runaway electron (RE) seed that emerges during the thermal quench. This problem is made challenging due to the requirement of describing a strongly non-thermal electron distribution, together with the need to incorporate a diverse range of multiphysics processes including magnetohydrodynamic instabilities, impurity transport, and radiative losses. The present work develops a physics-informed neural network (PINN) tailored to the solution of the hot tail seed during an idealized axisymmetric thermal quench. Here, a PINN is developed to identify solutions to the adjoint relativistic Fokker-Planck equation in the presence of a rapid quench of the plasma's thermal energy. It is shown that the PINN is able to accurately predict the hot tail seed across a range of parameters including the thermal quench time scale, initial plasma temperature, and local current density, in the absence of experimental or simulation data. The hot tail PINN is verified by comparison with a direct Monte Carlo solution, with excellent agreement found across a broad range of thermal quench conditions.