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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).
Edward J. Waller
Nuclear Technology | Volume 175 | Number 1 | July 2011 | Pages 89-92
Technical Note | Special Issue on the 16th Biennial Topical Meeting of the Radiation Protection and Shielding Division / Radiation Measurements and General Instrumentation | doi.org/10.13182/NT11-A12275
Articles are hosted by Taylor and Francis Online.
Recent nuclear weapons testing in the limit of low-yield detonations has underscored the need to ensure that radiation detection and monitoring equipment can adequately respond to these events. Testing and validating equipment in appropriate reference fields have become difficult since the closing of the NATO primary fission spectra reference at the Aberdeen Proving Ground Fast Burst Reactor facility post-9/11. A simple and low-cost device was designed to perform testing of commercial off-the-shelf neutron detection equipment to the expected spectral shape from a low-yield nuclear weapon. By enclosing an 241AmBe (,n) neutron source within a heavy water-moderated sphere, the general shape of a 1-kiloton standard fission weapon was generated at 1 m, valid between 100 and 2000 keV. The 1-m dose rate expected from this configuration is [approximately]2.16 × 10-10 Svh-1Bq-1 , which is less than one-half of the unshielded dose rate.