DIII-D Researchers Use Machine Learning to Steer Fusion Plasmas Near Operational Limits

Researchers at the DIII-D National Fusion Facility recently achieved a scientific first when they used machine learning calculations to automatically prevent fusion plasma disruptions in real time, while simultaneously optimizing the plasma for peak performance. The new experiments are the first of what they expect to be a wave of research in which machine learning–augmented controls could broaden the understanding of fusion plasmas. The work may help deliver reliable, peak-performance operation of future fusion reactors.

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Investigating Materials that Can Go the Distance in Fusion Reactors

Future fusion reactors will require materials that can withstand extreme operating conditions, including being bombarded by high-energy neutrons at high temperatures. Scientists recently irradiated titanium diboride (TiB2) in the High Flux Isotope Reactor (HFIR) to better understand the effects of fusion neutrons on performance.

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Department of Energy Announces Private-Public Awards to Advance Fusion Energy Technology

The U.S. Department of Energy (DOE) announced funding for 12 projects with private industry to enable collaboration with DOE national laboratories on overcoming challenges in fusion energy development.

The awards are the first provided through the Innovation Network for Fusion Energy program (INFUSE).

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Vlad Soukhanovskii

Vsevolod A. Soukhanovskii is a group leader at the Fusion Energy Sciences Program at the Department of Energy’s Lawrence Livermore National Laboratory. He and his research group are stationed on a long-term assignment focusing on edge plasma transport and plasma-surface interactions in spherical tokamaks at the Department of Energy’s Princeton Plasma Physics Laboratory.

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