Evaluation of the Dreicer runaway generation rate in the presence of high-Z impurities using a neural network

TL;DR

This paper develops a neural network model to accurately predict Dreicer runaway generation rates in plasmas with high-Z impurities, improving self-consistent runaway electron simulations by capturing effects neglected in previous formulas.

Contribution

A multilayer neural network for Dreicer runaway generation rate is trained on kinetic simulation data, enabling accurate and efficient modeling in impurity-rich plasmas.

Findings

Neural network accurately reproduces kinetic simulation results.

Implementation shows significant differences in runaway dynamics.

Improved rates enhance self-consistent plasma modeling.

Abstract

Integrated modelling of electron runaway requires computationally expensive kinetic models that are self-consistently coupled to the evolution of the background plasma parameters. The computational expense can be reduced by using parameterized runaway generation rates rather than solving the full kinetic problem. However, currently available generation rates neglect several important effects; in particular, they are not valid in the presence of partially ionized impurities. In this work, we construct a multilayer neural network for the Dreicer runaway generation rate which is trained on data obtained from kinetic simulations performed for a wide range of plasma parameters and impurities. The neural network accurately reproduces the Dreicer runaway generation rate obtained by the kinetic solver. By implementing it in a fluid runaway electron modelling tool, we show that the improved generation rates lead to significant differences in the self-consistent runaway dynamics as compared to the results using the previously available formulas for the runaway generation rate.