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Article Dans Une Revue Nuclear Fusion Année : 2015

Real-time capable first principle based modelling of tokamak turbulent ă transport

Jonathan Citrin
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F. Felici
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Frédéric Imbeaux
T. Ă Aniel
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J. F. Artaud
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B. Baiocchi
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J. Ă Garcia
  • Fonction : Auteur

Résumé

A real-time capable core turbulence tokamak transport model is ă developed. This model is constructed from the regularized nonlinear ă regression of quasilinear gyrokinetic transport code output. The ă regression is performed with a multilayer perceptron neural network. The ă transport code input for the neural network training set consists of ă five dimensions, and is limited to adiabatic electrons. The neural ă network model successfully reproduces transport fluxes predicted by the ă original quasilinear model, while gaining five orders of magnitude in ă computation time. The model is implemented in a real-time capable ă tokamak simulator, and simulates a 300 s ITER discharge in 10 s. This ă proof-of-principle for regression based transport models anticipates a ă significant widening of input space dimensionality and physics realism ă for future training sets. This aims to provide unprecedented ă computational speed coupled with first-principle based physics for ă real-time control and integrated modelling applications.

Dates et versions

hal-01465047 , version 1 (10-02-2017)

Identifiants

Citer

Jonathan Citrin, S. Breton, F. Felici, Frédéric Imbeaux, T. Ă Aniel, et al.. Real-time capable first principle based modelling of tokamak turbulent ă transport. Nuclear Fusion, 2015, 55 (9), ⟨10.1088/0029-5515/55/9/092001⟩. ⟨hal-01465047⟩
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