4.5 Article

Hollow pellet injection for magnetic fusion

Journal

NUCLEAR FUSION
Volume 59, Issue 8, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/1741-4326/ab19eb

Keywords

hollow pellet; ELMs control; boron

Funding

  1. US Department of Energy (DoE) Fusion Energy Sciences long-pulse tokamak program through the Triad National Security, LLC ('Triad') contract [89233218CNA000001]
  2. US DoE [DE-AC02-09CH11466, DE-FC02-04ER54698]
  3. National Key Research and Development Program of China [2017YFA0402500]
  4. National Natural Science Foundation of China [11625524]

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The precise delivery of mass to burning plasmas is an area of growing interest in magnetic fusion (MF). The amount of mass that is necessary and sufficient can vary depending on such parameters as the type of atoms involved, the type of applications, plasma conditions, mass injector, and injection timing. Motivated by edge localized mode (ELM) control in H-mode plasmas, disruption mitigation and other applications in MF, we report the progress and new possibilities in mass delivery based on hollow pellets. Here, a hollow pellet refers to a spherical shell mass structure with a hollow core. Based on an empirical model of pellet ablation, coupled with BOUT++ simulations of the ELM triggering threshold, hollow pellets are found to be attractive in comparison with solid spheres for ELM control. By using hollow pellets, it is possible to tailor mass delivery to certain regions of edge plasmas while minimizing core contamination and reducing the total amount of mass needed. We also include the experimental progress in mass delivery experiments, in situ diagnostics and hollow pellet fabrication, and emphasize new experimental possibilities for ELM control based on hollow pellets. A related application is the disruption mitigation scheme using powder encapsulated inside hollow shells. Further experiments will also help to resolve known discrepancies between theoretical predictions and experiments in using mass injection for ELM control and leading to better predictive models for ELM stability and triggering.

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