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Neural network evaluation of tokamak current profiles for real time control

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1 Author(s)
Wroblewski, Dariusz ; ORINCON Corporation, San Diego, California 92121-3017

Your organization might have access to this article on the publisher's site. To check, click on this link:http://dx.doi.org/+10.1063/1.1147899 

Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental datais demonstrated. © 1997 American Institute of Physics.

Published in:

Review of Scientific Instruments  (Volume:68 ,  Issue: 2 )