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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).
M. Wohlmuther, F. X. Gallmeier, M. Brugger, S. Roesler
Nuclear Technology | Volume 168 | Number 3 | December 2009 | Pages 685-688
Accelerators | Special Issue on the 11th International Conference on Radiation Shielding and the 15th Topical Meeting of the Radiation Protection and Shielding Division (PART 3) / Radiation Measurements and Instrumentation | doi.org/10.13182/NT09-A9290
Articles are hosted by Taylor and Francis Online.
In the framework of activation calculations of accelerator components with Monte Carlo methods, an unsolved problem is to take into account the spallation products of trace elements and impurities in a bulk material. Because of the low probability of spallation reactions with these elements, a large number of primary particles are necessary to obtain some information about their spallation products. A new algorithm for treating high-energy reactions has been implemented into MCNPX 2.5.0 to overcome these deficiencies. With this algorithm, spallation reactions of all constituents of a material will be performed at each high-energy interaction. This leads to the production of spallation products from all elements in a material. We will present examples of how this new methodology influences the outcome of activation calculations.
