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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).
R. Haange, H. Yoshida, O. Kveton, J. Koonce, H. Horikiri
Fusion Science and Technology | Volume 28 | Number 3 | October 1995 | Pages 491-495
Plenary Session | Proceedings of the Fifth Topical Meeting on Tritium Technology in Fission, Fusion, and Isotopic Applications Belgirate, Italy May 28-June 3, 1995 | doi.org/10.13182/FST95-A30450
Articles are hosted by Taylor and Francis Online.
This paper describes the design status of the ITER tritium plant. The key processing requirements and basis for process selection are summarized. Status of the remaining R&D efforts and selection of the process for impurity detritiation are also given. Evaluation studies for preliminary tritium plant processes were completed during the ITER Conceptual Design Activities (CDA) phase with an emphasis towards use of existing technology and proven industrial applications, where available [1]. Design of systems has continued during the Engineering Design Activities (EDA) phase. Layout designs are now available of most subsystems.