Evaluation of the Dreicer runaway generation rate in the presence of high-impurities using a neural network
Journal article, 2019

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. © Cambridge University Press 2019.

fusion plasma

runaway electrons

Author

Linnea Hesslow

Chalmers, Physics, Subatomic and Plasma Physics

Lucas Unnerfelt

Chalmers, Physics, Subatomic and Plasma Physics

Oskar Vallhagen

Chalmers, Physics, Subatomic and Plasma Physics

Ola Embréus

Chalmers, Physics, Subatomic and Plasma Physics

Mathias Hoppe

Chalmers, Physics, Subatomic and Plasma Physics

Gergely Papp

Max Planck Society

Tünde Fülöp

Chalmers, Physics, Subatomic and Plasma Physics

Journal of Plasma Physics

0022-3778 (ISSN) 1469-7807 (eISSN)

Vol. 85 6 475850601

Subject Categories

Computational Mathematics

Other Physics Topics

Bioinformatics (Computational Biology)

DOI

10.1017/S0022377819000874

More information

Latest update

10/5/2020