DOI: 10.7910/dvn/irewnj
title: Runaway electron deconfinement in SPARC and DIII-D by a passive 3D coil

description: The operation of a 3D coil (passively driven by the current quench loop voltage) for the deconfinement of runaway electrons is modeled for disruption scenarios in the SPARC and DIII-D tokamaks. Nonlinear MHD modeling is carried out with the NIMROD code including time-dependent magnetic field boundary conditions to simulate the effect of the coil. Further modeling in some cases uses the ASCOT5 code to calculate advection and diffusion coefficients for runaway electrons based on the NIMROD-calculated fields, and the DREAM code to compute the runaway evolution in the presence of these transport coefficients. Compared with similar modeling in Tinguely, et al [2021 Nucl. Fusion 61 124003], considerably more conservative assumptions are made with the ASCOT5 results, zeroing low levels of transport, particularly in regions in which closed flux surfaces have reformed. Of three coil geometries considered in SPARC, only the n = 1 coil is found to have sufficient resonant components to suppress the runaway current growth. Without the new conservative transport assumptions, full suppression of the RE current is maintained when the TQ MHD is included in the simulation or when the RE current is limited to 250kA, but when transport in closed ux regions is fully suppressed, these scenarios allow RE beams on the order of 1-2MA to appear. Additional modeling is performed to consider the effects of the close ideal wall. In DIII-D, the current quench is modeled for both limited and diverted equilibrium shapes. In the limited shape, the onset of stochasticity is found to be insensitive to the coil current amplitude and governed largely by the evolution of the safety-factor prole. In both devices, prediction of the q-prole evolution is seen to be critical to predicting the later time effects of the coil.
description: PSFC REPORT PSFC/JA-22-50<br><br>The authors are grateful to M. Hoppe and T. Fulop for fruitful discussions. This work is supported in part by Commonwealth Fusion Systems. This work was partly supported by the Swedish Research Council (Dnr. 2018-03911). This material is also based on work supported by the Department of Energy under Awards Number DEFG02-95ER54309 and DE-FC02-04ER54698. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. Part of the data analysis was performed using the OMFIT integrated modeling framework [60]. Disclaimer. Part of this report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.<br><br>If this record does not contain the full text, then the manuscript has been embargoed by the publisher thus restricting open access for 12 to 24 months after publication.