深層補強学習で融合プラズマの破裂の不安定性を回避する
Jaemin Seo1,2, SangKyeun Kim1,3, Azarakhsh Jalalvand1
1Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA.
Nature
|February 21, 2024
まとめ
トカマックを使用して訓練された人工知能 (AI)
科学分野:
- 核融合エネルギー研究
- プラズマ物理学
- 制御システム工学
背景:
- 安定したトカマック操作には,プラズマの破壊を防ぐために,アクティブな制御が必要です.
- 破裂の不安定さは 混乱の主要な原因で 予測や回避が困難です
- 破裂の不安定性を予測する ダイナミックモデルを開発しました
研究 の 目的:
- トカマックの破壊的な破裂の不安定性を防ぐためのAI駆動制御システムを開発する.
- 自動制御のための強化学習の枠組みの中でマルチモダルのダイナミックモデルを活用する.
- 安定したプラズマ操作を維持する AI コントローラーの有効性を示すために.
主な方法:
- 将来の破裂の不安定性を推定する多様式ダイナミックモデルを開発した.
- このモデルを補強学習 (AI) のトレーニング環境として利用した.
- DIII-DトカマックでAIコントローラを実装し,テストしました.
主要な成果:
- AI コントローラーは 破壊的な破裂の不安定性を成功裏に減少させました
- 挑戦的な条件下 (低安全因子,低トルク) において,破裂の不安定性を維持した.
- コントローラーは安定したプラズマ追跡とHモードのパフォーマンスを可能にし,従来の方法を超えました.
結論:
- 予測ダイナミックモデルで訓練されたAI制御は トカマックプラズマの破壊を効果的に防止します
- このアプローチは,ITERのような将来の核融合炉の安定した高性能プラズマシナリオを容易にする.
- 核融合エネルギー生産を進めるには 自動化された不安定性の防止が不可欠です
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