使用卷积神经网络从多普勒反射测量中检测DIII-D中的边缘局部模式
N Q X Teo1,2, V H Hall-Chen1, K Barada3
1Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore 138632.
The Review of scientific instruments
|July 22, 2024
概括
托卡马克等离子体中的边缘局部化模式 (ELM) 可以使用多普勒逆向散射 (DBS) 数据来检测. 在DBS数据上训练的神经网络实现了高精度,为未来的核聚变反应堆中ELM检测提供了可靠的方法.
科学领域:
- 血物理学的等离子体物理学
- 核聚变能源的研究.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 边缘局部模式 (ELM) 是H模式托卡马克操作中的等离子体喷射.
- 电磁雷达会造成能量损失和船只损伤,需要有效的检测和缓解策略.
- 目前的诊断方法,如-α (Dα) 光谱,有其局限性.
研究的目的:
- 开发和评估一种神经网络模型,用于使用多普勒逆向散射 (DBS) 数据检测ELM.
- 建立DBS作为一个可行的诊断ELM检测在未来的操作tokamaks.
- 为了证明神经网络对DBS诊断数据的更广泛适用性.
主要方法:
- 使用DIII-D托卡马克数据库进行培训和测试.
- 训练了一个神经网络,根据ELM事件使用Dα数据作为基本真理来分类时间步骤.
- 在各种ELM类型和封闭制度中评估模型性能.
主要成果:
- 在测试数据上获得了0.93的高f1得分,表明了强大的ELM检测.
- 在不同ELM模式 (草地,RMP缓解,宽脚架) 中表现一致.
- 证实神经网络用于分析DBS诊断数据的广泛适用性.
结论:
- 神经网络提供了使用DBS数据进行ELM检测的有希望和准确的方法.
- 由于其高时间分辨率和稳定性,DBS对于未来的tokamaks来说是一个有价值的诊断.
- 这项工作验证了机器学习用于处理DBS数据的使用,超出了ELM检测范围.
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