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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).
P. A. Nelson, D. K. Butler, M. G. Chasanov, D. Meneghetti
Nuclear Technology | Volume 3 | Number 9 | September 1967 | Pages 540-547
Technical Paper and Note | doi.org/10.13182/NT67-A27935
Articles are hosted by Taylor and Francis Online.
The characteristics of fast reactors having molten fuels consisting of uranium and plutonium trichlorides dissolved in alkali chlorides and alkaline-earth chlorides were studied. The study included considerations of the physical and chemical properties of the fuel, the heat-removal problems, and neutronic characteristics for three types of chloride reactors: a homogeneous reactor and two internally cooled reactors. Optimization of the core size for 1000-MW(e) reactors resulted in a core volume of 10 000 liters for each type. These reactors have the favorable characteristics (even for natural chlorine) of high breeding ratio, large negative temperature coefficients of reactivity, and low fuel-cycle costs. However, the unattractive characteristics of large plutonium inventory, large volume, complex design, and container material problems indicate that a sizeable program to develop chloride-fueled reactors