论文标题
自动识别DIII-D Tokamak中边缘局部模式
Automatic Identification of Edge Localized Modes in the DIII-D Tokamak
论文作者
论文摘要
Tokamaks中的融合功率生产使用排放配置，冒着产生强大I型边缘本地化模式的出现配置。这些模式中最大的一种可能会增加血浆中的杂质，并可能损坏面向组件的血浆，例如保护热和废物分流。基于机器学习的预测和控制可能可以在线缓解这些破坏性模式，然后才能抑制这些损害模式。为此，需要大量标记的数据集来监督机器学习模型的培训。我们提出了一种算法，该算法在大型DIII-D Tokamak放电数据库中自动标记边缘局部模式时，可以达到97.7％的精度。该算法没有用户控制的参数，对于Tokamak和等离子配置的变化基本上是鲁棒的。这种自动标记的事件数据库随后可以为针对自主边缘局部模式控制和抑制的机器学习模型提供未来的培训。
Fusion power production in tokamaks uses discharge configurations that risk producing strong Type I Edge Localized Modes. The largest of these modes will likely increase impurities in the plasma and potentially damage plasma facing components such as the protective heat and waste divertor. Machine learning-based prediction and control may provide for online mitigation of these damaging modes before they grow too large to suppress. To that end, large labeled datasets are required for supervised training of machine learning models. We present an algorithm that achieves 97.7% precision when automatically labeling Edge Localized Modes in the large DIII-D tokamak discharge database. The algorithm has no user controlled parameters and is largely robust to tokamak and plasma configuration changes. This automatically-labeled database of events can subsequently feed future training of machine learning models aimed at autonomous Edge Localized Mode control and suppression.
