Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

F. Felici, J. Citrin, A.A. Teplukhina, J. Redondo, C. Bourdelle, F. Imbeaux, O. Sauter

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)

Abstract

The RAPTOR code is a control-oriented core plasma profile simulator with various applications in control design and verification, discharge optimization and real-time plasma simulation. To date, RAPTOR was capable of simulating the evolution of poloidal flux and electron temperature using empirical transport models, and required the user to input assumptions on the other profiles and plasma parameters. We present an extension of the code to simulate the temperature evolution of both ions and electrons, as well as the particle density transport. A proof-of-principle neural-network emulation of the quasilinear gyrokinetic QuaLiKiz transport model is coupled to RAPTOR for the calculation of first-principle-based heat and particle turbulent transport. These extended capabilities are demonstrated in a simulation of a JET discharge. The multi-channel simulation requires ∼0.2 s to simulate 1 second of a JET plasma, corresponding to ∼20 energy confinement times, while predicting experimental profiles within the limits of the transport model. The transport model requires no external inputs except for the boundary condition at the top of the H-mode pedestal. This marks the first time that simultaneous, accurate predictions of T e, T i and n e have been obtained using a first-principle-based transport code that can run in faster-than-real-time for present-day tokamaks.

Original languageEnglish
Article number096006
Number of pages12
JournalNuclear Fusion
Volume58
Issue number9
DOIs
Publication statusPublished - Sep 2018

Keywords

  • integrated tokamak simulation
  • machine learning
  • real-time control
  • tokamak profiles
  • tokamak transport

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