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Nuclear Science and Engineering
Fusion Science and Technology
AI can predict and prevent fusion plasma instabilities in milliseconds
A team of engineers, physicists, and data scientists from Princeton University and the Princeton Plasma Physics Laboratory (PPPL) have used artificial intelligence (AI) to predict—and then avoid—the formation of a specific type of plasma instability in magnetic confinement fusion tokamaks. The researchers built and trained a model using past experimental data from operations at the DIII-D National Fusion Facility in San Diego, Calif., before proving through real-time experiments that their model could forecast so-called tearing mode instabilities up to 300 milliseconds in advance—enough time for an AI controller to adjust operating parameters and avoid a tear in the plasma that could potentially end the fusion reaction.
T. Cutler, H. Trellue, M. Blood, T. Grove, E. Luther, N. Thompson, N. Wynne
Nuclear Technology | Volume 209 | Number 1 | January 2023 | Pages S92-S108
Technical Paper | doi.org/10.1080/00295450.2022.2027146
Articles are hosted by Taylor and Francis Online.
The Hypatia measurement campaign with YHx moderators and highly enriched uranium (HEU) was completed in January 2021 at the U.S. Department of Energy’s National Criticality Experiments Research Center at the Nevada National Security Site. This measurement campaign provided unique integral measurements based on two experimental configurations and investigated the temperature effects of yttrium hydride (YHX = 1.8 and 1.9) in a critical reactor system, which is of potential interest for microreactor designs. The Hypatia experiment consisted of a fuel column composed of HEU, 93 wt% 235U discs, encapsulated YHX, aluminum oxide heater plates, and other moderator and reflector materials (beryllium, depleted uranium, and graphite) inserted into a thick beryllium reflector. During the Hypatia experiment, baseline measurements were taken at room temperature. The aluminum oxide heater plates were specially designed and used for this project to increase the central core temperature to a range of temperatures, after which additional reactivity measurements were taken. Thermal and neutronic calculations predicted that YHX is a unique material that can exhibit a positive temperature coefficient of reactivity (i.e., reactivity can increase as the temperature in the YHX increases). Reactors using YHX should account for this unique feature during design, and the results of the Hypatia experiment significantly aid that process. For configuration 1, six different temperature reactivity measurements were taken with four YHX cans in the fuel column. For configuration 2, six different temperature reactivity measurements were taken with two YHX cans in the fuel column. The use of these two configurations provide a comparison of neutronic effects from the YHX cans versus other components. Preliminary conclusions show the positive temperature coefficient is similar but slightly less than predicted by simulations. These two sets of data will be used to separate the reactivity coefficients of the fuel and other materials in the fuel column.