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This content will become publicly available on July 12, 2018

Title: Self-consistent core-pedestal transport simulations with neural network accelerated models

Fusion whole device modeling simulations require comprehensive models that are simultaneously physically accurate, fast, robust, and predictive. In this paper we describe the development of two neural-network (NN) based models as a means to perform a snon-linear multivariate regression of theory-based models for the core turbulent transport fluxes, and the pedestal structure. Specifically, we find that a NN-based approach can be used to consistently reproduce the results of the TGLF and EPED1 theory-based models over a broad range of plasma regimes, and with a computational speedup of several orders of magnitudes. These models are then integrated into a predictive workflow that allows prediction with self-consistent core-pedestal coupling of the kinetic profiles within the last closed flux surface of the plasma. Finally, the NN paradigm is capable of breaking the speed-accuracy trade-off that is expected of traditional numerical physics models, and can provide the missing link towards self-consistent coupled core-pedestal whole device modeling simulations that are physically accurate and yet take only seconds to run.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [2] ;  [3] ; ORCiD logo [4]
  1. General Atomics, San Diego, CA (United States)
  2. Politecnico di Torino, Torino (Italy)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Publication Date:
Grant/Contract Number:
FG02-95ER54309
Type:
Accepted Manuscript
Journal Name:
Nuclear Fusion
Additional Journal Information:
Journal Volume: 57; Journal Issue: 8; Journal ID: ISSN 0029-5515
Publisher:
IOP Science
Research Org:
General Atomics, San Diego, CA (United States)
Sponsoring Org:
USDOE Office of Nuclear Energy (NE)
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY; neural networks; transport; pedestal; tokamak
OSTI Identifier:
1374389