エンジニアがAIを使って送電網用の核融合発電を開発(Engineers use AI to wrangle fusion power for the grid)

2024-02-21 プリンストン大学

＜関連情報＞

• https://engineering.princeton.edu/news/2024/02/21/engineers-use-ai-wrangle-fusion-power-grid
• https://www.nature.com/articles/s41586-024-07024-9

深層強化学習で核融合プラズマの不安定性を回避する Avoiding fusion plasma tearing instability with deep reinforcement learning

Jaemin Seo,SangKyeun Kim,Azarakhsh Jalalvand,Rory Conlin,Andrew Rothstein,Joseph Abbate,Keith Erickson,Josiah Wai,Ricardo Shousha & Egemen Kolemen
Nature  Published:21 February 2024
DOI:https://doi.org/10.1038/s41586-024-07024-9

Abstract

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 performance^1,2,3,4. 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.