Andrew Rothstein, A. Jalalvand, J. Abbate, K. Erickson, E. Kolemen
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引用次数: 0
Abstract
A first of its kind fully data-driven system has been developed and implemented into the DIII-D plasma control system to detect and control Alfvén Eigenmodes (AE) in real-time. Susceptibility to fast ion-induced AE is a challenge in fully non-inductive tokamak operation, which significantly reduces fast-particle confinement and results in degraded fusion gain. Controlling AEs in real-time to improve fast-ion confinement is, hence, important for future Advanced Tokamak fusion reactors. The models were implemented and tested in experiments which showed that neural networks (NN) are highly effective in detecting 5 types of AE (BAE, EAE, LFM, RSAE, TAE) using high resolution ECE. To estimate the neutron deficit, a neural network has been trained that outputs the classical neutron rate using similar inputs to NUBEAM. Also a preliminary ML-based proportional control has been designed and gone through initial testing in experiment to use feedback-control on the neutral beam power to achieve desired amplitude of AE modes and neutron deficits. The effect of AEs on fast-ion confinement is measured by analysing the gap in classical neutron rate from the proposed NN-based NUBEAM and the measured neutron rate.
期刊介绍:
Nuclear Fusion publishes articles making significant advances to the field of controlled thermonuclear fusion. The journal scope includes:
-the production, heating and confinement of high temperature plasmas;
-the physical properties of such plasmas;
-the experimental or theoretical methods of exploring or explaining them;
-fusion reactor physics;
-reactor concepts; and
-fusion technologies.
The journal has a dedicated Associate Editor for inertial confinement fusion.