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Jefferson Lab has deployed a machine learning framework using digital twins of DIII-D tokamak coils to predict hardware distortions before they disrupt fusion experiments. This proactive monitoring addresses critical stability challenges in high-temperature plasma environments by forecasting physical shifts during operational downtimes. The approach directly accelerates the pursuit of viable fusion energy through enhanced predictive maintenance and experimental continuity.
Jefferson Lab’s AI spots trouble in fusion reactors
Jefferson Lab’s machine learning framework helps researchers at the DIII-D National Fusion Facility, predicting hardware changes—essentially, AI spots trouble—before it impacts experiments.
Plasma inside the DIII-D tokamak at the U.S. Department of Energy’s fusion science facility reaches temperatures hotter than the sun’s core, creating a harsh environment where even subtle distortions of the machine’s physical structure can significantly impact ongoing experiments. To address this challenge, data scientists from Jefferson Lab and partner institutions have developed a machine learning framework to predict changes in the tokamak’s hardware. “The technique we have developed would help…
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