2018
DOI: 10.1088/1361-6587/aac700
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Flux-surface variations of the electrostatic potential in stellarators: impact on the radial electric field and neoclassical impurity transport

Abstract: Flux-surface variations of the electrostatic potential are typically neglected in standard neoclassical theory, but in 3D devices they can be large enough to affect the radial particle flux of impurities. The radially local drift-kinetic equation solver SFINCS (stellarator Fokker-Planck iterative neoclassical conservative solver) (Landreman et al 2014 Phys. Plasmas 21 042503) has been updated to account for these variations. In the present work we use SFINCSto perform a novel study of neoclassical particle tr… Show more

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Cited by 32 publications
(65 citation statements)
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References 49 publications
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“…However, these conclusions, drawn from the results presented in refs. [1,20], cover still a very narrow parameter and configuration window of W7-X. In particular all plasmas studied in those references are ion root plasmas foreseen during the future W7-X operation phase OP2.…”
Section: Potential Variations In W7-x: Cerc Plasmas and Effect Of Kinmentioning
confidence: 99%
“…However, these conclusions, drawn from the results presented in refs. [1,20], cover still a very narrow parameter and configuration window of W7-X. In particular all plasmas studied in those references are ion root plasmas foreseen during the future W7-X operation phase OP2.…”
Section: Potential Variations In W7-x: Cerc Plasmas and Effect Of Kinmentioning
confidence: 99%
“…2017; Mollén et al. 2018) for tokamaks and stellarators. Turbulent transport is also known to be affected by these variations, see for example Mollén et al.…”
Section: Introductionmentioning
confidence: 99%
“…We optimize n z for a W7-X and an LHD model case, with the logarithmic and potential gradients shown in Fig. 1 and magnetic geometry in from LHD discharge #113208 at t = 4.64 s 25,26 . Note that the cases considered here are not experimental cases, and merely use simulated or experimental data as realistic inputs.…”
Section: Resultsmentioning
confidence: 99%