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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).
J. G. Gilligan, K. Evans, J. Jung
Fusion Science and Technology | Volume 4 | Number 2 | September 1983 | Pages 273-278
Fusion Systems Studies | doi.org/10.13182/FST83-A22880
Articles are hosted by Taylor and Francis Online.
If sufficient tritium cannot be produced and processed in tokamak blankets then at least two alternatives are possible. Tritium can be purchased; or reactors with reduced tritium (RT) content in the plasma can be designed. The latter choice may require development of magnet technology etc., but we show that the impact on the cost-of-electricity may be mild. Cost tradeoffs are compared to the market value of tritium. Adequate tritium production in fusion blankets is preferred, but we show there is some flexibility in the deployment of fusion if this is not possible.