Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities and compatible with ITER IMAS
 
research article

Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities and compatible with ITER IMAS

Meneghini, O.
•
Snoep, G.
•
Lyons, B. C.
Show more
February 1, 2021
Nuclear Fusion

An integrated modeling workflow capable of finding the steady-state plasma solution with self-consistent core transport, pedestal structure, current profile, and plasma equilibrium physics has been developed and tested against a DIII-D discharge. Key features of the achieved core-pedestal coupled workflow are its ability to account for the transport of impurities in the plasma self-consistently, as well as its use of machine learning accelerated models for the pedestal structure and for the turbulent transport physics. Notably, the coupled workflow is implemented within the One Modeling Framework for Integrated Tasks (OMFIT) framework, and makes use of the ITER integrated modeling and analysis suite data structure for exchanging data among the physics codes that are involved in the simulations. Such technical advance has been facilitated by the development of a new numerical library named ordered multidimensional arrays structure.

  • Details
  • Metrics
Type
research article
DOI
10.1088/1741-4326/abb918
Web of Science ID

WOS:000601129700001

Author(s)
Meneghini, O.
Snoep, G.
Lyons, B. C.
McClenaghan, J.
Imai, C. S.
Grierson, B.
Smith, S. P.
Staebler, G. M.
Snyder, P. B.
Candy, J.
Show more
Date Issued

2021-02-01

Publisher

IOP Publishing Ltd

Published in
Nuclear Fusion
Volume

61

Issue

2

Article Number

026006

Subjects

Physics, Fluids & Plasmas

•

Physics

•

tokamak

•

integrated

•

modeling

•

omfit

•

omas

•

gacode

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
SPC  
Available on Infoscience
March 26, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/176836
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés