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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).
Yasuhiro Suzuki, Yuji Nakamura, Katsumi Kondo, Noriyoshi Nakajima, Takaya Hayashi
Fusion Science and Technology | Volume 46 | Number 2 | September 2004 | Pages 234-240
Technical Papers | Stellarators | doi.org/10.13182/FST04-A560
Articles are hosted by Taylor and Francis Online.
Magnetohydrodynamic (MHD) equilibria of Heliotron J plasma are investigated by using the HINT code. HINT calculates an MHD equilibrium by using the relaxation method, which calculates the time evolution of dissipative MHD equations. Since HINT uses Eulerian rectangular grids, it does not assume nested flux surfaces. In the standard configuration, magnetic islands appear inside the plasma in spite of low (<> ~ 0.4%). The width of the islands depends on the pressure distribution. To reduce island width, feedback control of the external vertical field is introduced. Because of the effect of the external vertical field, magnetic islands are suppressed. The effects of the net toroidal currents on MHD equilibrium are also investigated. The rotational transform is changed by the currents, but the Shafranov shift is almost unaffected. The width of the islands changes because of the change in the rotational transform.
