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O Embreus

Publications and source records attributed to O Embreus.

2 recordsLinked to original sources

Runaway dynamics in the DT phase of ITER operations in the presence of massive material injection

A runaway avalanche can result in a conversion of the initial plasma current into a relativistic electron beam in high current tokamak disruptions. We investigate the effect of massive material injection of deuterium-noble gas mixtures on the coupled dynamics of runaway generation, resistive diffusion of the electric field, and temperature evolution during disruptions in the DT phase of ITER operations. We explore the dynamics over a wide range of injected concentrations and find substantial runaway currents, unless the current quench time is intolerably long. The reason is that the cooling associated with the injected material leads to high induced electric fields that, in combination with a significant recombination of hydrogen isotopes, leads to a large avalanche generation. Balancing Ohmic heating and radiation losses provides qualitative insights into the dynamics, however, an accurate modeling of the temperature evolution based on energy balance appears crucial for quantitative predictions.

physics.plasm-ph

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

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.

physics.plasm-ph