Fast ion synergistic effects in JET high performance pulses

K. K. Kirov, Yu Baranov, I. S. Carvalho, C. D. Challis, J. Eriksson, D. Frigione, L. Garzotti, J. Graves, P. Jacquet, D. L. Keeling, E. Lerche, P. J. Lomas, C. Lowry, M. Mantsinen, F. Rimini

Research output: Contribution to journalArticlepeer-review

Abstract

Fast ion synergistic effects were studied by predictive modelling of JET best performing pulses for various levels of neutral beam injection (NBI) and radio frequency (RF) power. Calculated DD neutron yields were analysed with the intention of separating the impact of RF synergistic effects due to changes in fast ion (FI) distribution function (DF) from secondary effects accompanying the application of RF power, namely changes in T e and T i. A novel approach in analysing the efficiency of fast ions in fusion reactions based on evaluation of the cumulative reaction rates is outlined and used in the study. Conclusions on the impact of fast ion synergistic effects on fusion performance are based on comparisons of beam-target (BT) and thermal (Th) DD reaction rates. It was found that changes in auxiliary heating power, NBI and RF, by 4 MW will affect DD fusion performance and neutron rates significantly. Simulations of the best performing JET pulses show that for H minority RF heating scheme with available RF power the impact of RF synergistic effects is somewhat lesser than the secondary effects related to changes in T e and T i. In conditions of much higher RF power the modification in fast ion distribution function (FI DF) and the impact of the fast ions on BT DD fusion becomes significant. The impact of the RF and NBI power on the BT reactivities was found to be of similar order; however, the NBI power has greater impact on reaction rates due to its larger effect on fast ion density.

Original languageEnglish
Article number056005
JournalNuclear Fusion
Volume59
Issue number5
DOIs
Publication statusPublished - 22 Mar 2019

Keywords

  • JET
  • NBI/ICRH synergy
  • fast ion distribution function
  • predictive modelling

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