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

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.

16 pages, 5 figures

References in corpus (3)

• Effective Critical Electric Field for Runaway-Electron Generation
• NORSE: A solver for the relativistic non-linear Fokker-Planck equation for electrons in a homogeneous plasma
• Runaway electron modelling in the self-consistent core European Transport Simulator, ETS

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