Avoiding fusion plasma tearing instability with deep reinforcement learning

• 成果类型: Article
• 署名作者: Seo, Jaemin; Kim, Sangkyeun; Jalalvand, Azarakhsh; Conlin, Rory; Rothstein, Andrew; Abbate, Joseph; Erickson, Keith; Wai, Josiah; Shousha, Ricardo; Kolemen, Egemen
• 署名单位: Princeton University; Chung Ang University; Princeton University; United States Department of Energy (DOE); Princeton Plasma Physics Laboratory; Princeton University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-3747
• DOI: 10.1038/s41586-024-07024-9
• 发表日期: 2024-02-22
• 关键词: modes; DESIGN

摘要

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance1-4. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators5. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D6, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER. Artificial intelligence control is used to avoid the emergence of disruptive tearing instabilities in the magnetically confined fusion plasma in the DIII-D tokamak reactor.