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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).
S. V. Mirnov, V. A. Evtikhin
Fusion Science and Technology | Volume 47 | Number 3 | April 2005 | Pages 698-702
Technical Paper | Fusion Energy - Divertor and Plasma-Facing Components | doi.org/10.13182/FST05-A766
Articles are hosted by Taylor and Francis Online.
The experimental study of liquid metal tokamak limiters was undertaken in Russian T-3M and T-11M tokamaks (Ip 100kA BT [is approximately equal to] 1T) between 1990-2004. In T-3M droplet stream and film flow Ga limiters were tested. In T-11M the experiments with Li Capillary Pore Systems (CPS) as rail limiter for investigation of real Li erosion in typical tokamak boundary condition (Te = 30 ± 10 eV, Pload = 10 MW/m2, D, He plasma) were performed. It was shown, that use of a liquid metal as material of tokamak limiter has not a serious physical obstacles.
