在托卡马卡没有有害的边缘能量爆发的情况下,最高的融合性能
Nature communications
|May 11, 2024
概括
研究人员为tokamaks开发了一种新的3D磁场方法,使用机器学习来防止等离子体不稳定. 这种方法提高了未来的核聚变反应堆 (如ITER) 的核聚变发电量和设备寿命.
科学领域:
- 核聚变能源是核聚变能源.
- 等离子体物理学的物理学
- 机器学习应用 机器学习应用
背景情况:
- 托卡马克核聚变反应堆的目标是利用高性能等离子体来产生核聚变电力.
- 在等离子体边界的等离子体不稳定性会导致短暂的能量爆发,阻碍核聚变功率.
- 目前使用3D磁性扰动的方法可以降低融合性能,并引入新的不稳定性.
研究的目的:
- 提出一种创新的3D磁场优化方法,用于抑制等离子体不稳定性.
- 克服传统方法在保持高性能等离子体方面的局限性.
- 提高核聚变发电量,同时确保设备的安全性和寿命.
主要方法:
- 使用机器学习实现了优化的3D磁场光谱.
- 使用实时自适应控制策略.
- 在 DIII-D 和 KSTAR 托卡马克实验中应用了该方法.
主要成果:
- 实现了与反应堆相关的核心限制和创纪录的核聚变性能.
- 成功抑制了破坏性的短暂能量爆发.
- 在复杂的等离子体系统中证明了ML驱动方法的有效性.
结论:
- 开发的3D场优化方法提高了融合效率和设备保护.
- 自动化,实时的自适应控制对于未来的核聚变反应堆至关重要.
- 这种方法对于ITER的成功和推进核聚变能源至关重要.
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