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Fusion Science and Technology
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PPPL develops framework for unifying tokamak ML control models
Princeton Plasma Physics Laboratory announced that researchers at the lab, in collaboration with Princeton University, have developed a general algorithm for prediction and control in tokamak systems and have tested it at DIII-D, as presented in a recent Nuclear Fusion paper.
According to the paper, most machine learning (ML)–based tools for use in fusion machines have been implemented as stand-alone demonstrations, aiming to predict the plasma profile, suppress a form of instability, for example. PPPL’s project provides a framework that aims to accommodate these disparate models into an integrated system, which the team calls PACMAN (Prediction and Control Using Machine Learning).
A. Itakura et al.
Fusion Science and Technology | Volume 47 | Number 1 | January 2005 | Pages 300-302
Technical Paper | Open Magnetic Systems for Plasma Confinement | doi.org/10.13182/FST05-A670
Articles are hosted by Taylor and Francis Online.
Fluctuation of electron density is observed by using a microwave reflectometry in the central cell of GAMMA 10 tandem mirror. An ultrashort-pulse train, whose pulse width is 65 ps, is transmitted into the plasma in the ordinary-wave mode and reflected at the cut-off layer. The reflected wave is detected by the receiving system, and its time-of-flight, i.e., round trip time, is measured. Fluctuation of the time-of-flight is fluctuation of the cutoff layer and it means density fluctuation. The pulse has a broad frequency spectrum, so each frequency component is reflected at different layer corresponding to its frequency. The frequency range of the receiving system is 7 to 11 GHz, and cut-off density ranges 0.61 to 1.5 × 1018 m-3. Density on the central axis of the plasma is about 2 × 1018 m-3. Radial intensity distribution of the fluctuation is observed without any perturbation. Frequency of the fluctuation is around several kHz.
