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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).
Douglas W. Stamps
Nuclear Science and Engineering | Volume 157 | Number 3 | November 2007 | Pages 331-343
Technical Paper | doi.org/10.13182/NSE07-A2731
Articles are hosted by Taylor and Francis Online.
A general analytical model was developed to predict the thermal-hydraulic behavior in box-type catalytic recombiners of different sizes and configurations. The fluid mechanics of the recombiner was modeled as flow through a chimney, which resulted in a modified form of the classic chimney equation to predict the exit gas velocity and flow rate. The thermal behavior of the recombiner was modeled using the transient form of the energy equation for reacting flow. The model was assessed using data from recombiners developed by the NIS Ingenieurgesellschaft Company (NIS), Siemans, and Atomic Energy of Canada Limited. Good agreement was obtained between the model and experimental data for the time-dependent hydrogen concentration in the test facility and the capacity of the recombiner in terms of the hydrogen recombination rate, both key parameters in the analyses of accidents in nuclear power plants. The analytical model could be reduced to the form of an empirical correlation developed for the NIS recombiner under simplifying conditions.