Initial testing of Alfvén eigenmode feedback control with machine-learning observers on DIII-D

Andrew Rothstein, Azarakhsh Jalalvand, Joseph Abbate, Keith Erickson, Egemen Kolemen

Research output: Contribution to journalArticlepeer-review

Abstract

A first of its kind fully data-driven system has been developed and implemented into the DIII-D plasma control system to detect and control Alfvén eigenmodes (AE) in real-time. Susceptibility to fast ion-induced AE is a challenge in fully non-inductive tokamak operation, which significantly reduces fast-particle confinement and results in degraded fusion gain. Controlling AEs in real-time to improve fast-ion confinement is, hence, important for future advanced tokamak fusion reactors. The models were implemented and tested in experiments which showed that neural networks (NN) are highly effective in detecting 5 types of AE (BAE, EAE, LFM, RSAE, TAE) using high resolution ECE. To estimate the neutron deficit, a NN has been trained that outputs the classical neutron rate using similar inputs to NUBEAM. Also a preliminary ML-based proportional control has been designed and gone through initial testing in experiment to use feedback-control on the neutral beam power to achieve desired amplitude of AE modes and neutron deficits. The effect of AEs on fast-ion confinement is measured by analysing the gap in classical neutron rate from the proposed NN-based NUBEAM and the measured neutron rate.

Original languageEnglish (US)
Article number096020
JournalNuclear Fusion
Volume64
Issue number9
DOIs
StatePublished - Sep 2024

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Condensed Matter Physics

Keywords

  • Alfvén eigenmodes
  • control
  • DIII-D
  • machine learning
  • neural networks

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